purpose of assignment model

Assignment Model | Linear Programming Problem (LPP) | Introduction

What is assignment model.

→ Assignment model is a special application of Linear Programming Problem (LPP) , in which the main objective is to assign the work or task to a group of individuals such that;

i) There is only one assignment.

ii) All the assignments should be done in such a way that the overall cost is minimized (or profit is maximized, incase of maximization).

→ In assignment problem, the cost of performing each task by each individual is known. → It is desired to find out the best assignments, such that overall cost of assigning the work is minimized.

For example:

Suppose there are 'n' tasks, which are required to be performed using 'n' resources.

The cost of performing each task by each resource is also known (shown in cells of matrix)

In the above asignment problem, we have to provide assignments such that there is one to one assignments and the overall cost is minimized.

How Assignment Problem is related to LPP? OR Write mathematical formulation of Assignment Model.

→ Assignment Model is a special application of Linear Programming (LP).

→ The mathematical formulation for Assignment Model is given below:

→ Let, C i j \text {C}_{ij} C ij denotes the cost of resources 'i' to the task 'j' ; such that

→ Now assignment problems are of the Minimization type. So, our objective function is to minimize the overall cost.

→ Subjected to constraint;

(i) For all j t h j^{th} j t h task, only one i t h i^{th} i t h resource is possible:

(ii) For all i t h i^{th} i t h resource, there is only one j t h j^{th} j t h task possible;

(iii) x i j x_{ij} x ij is '0' or '1'.

Types of Assignment Problem:

(i) balanced assignment problem.

It consist of a suqare matrix (n x n).
Number of rows = Number of columns

(ii) Unbalanced Assignment Problem

It consist of a Non-square matrix.
Number of rows ≠ \not=  = Number of columns

Methods to solve Assignment Model:

(i) integer programming method:.

In assignment problem, either allocation is done to the cell or not.

So this can be formulated using 0 or 1 integer.

While using this method, we will have n x n decision varables, and n+n equalities.

So even for 4 x 4 matrix problem, it will have 16 decision variables and 8 equalities.

So this method becomes very lengthy and difficult to solve.

(ii) Transportation Methods:

As assignment problem is a special case of transportation problem, it can also be solved using transportation methods.

In transportation methods ( NWCM , LCM & VAM), the total number of allocations will be (m+n-1) and the solution is known as non-degenerated. (For eg: for 3 x 3 matrix, there will be 3+3-1 = 5 allocations)

But, here in assignment problems, the matrix is a square matrix (m=n).

So total allocations should be (n+n-1), i.e. for 3 x 3 matrix, it should be (3+3-1) = 5

But, we know that in 3 x 3 assignment problem, maximum possible possible assignments are 3 only.

So, if are we will use transportation methods, then the solution will be degenerated as it does not satisfy the condition of (m+n-1) allocations.

So, the method becomes lengthy and time consuming.

(iii) Enumeration Method:

It is a simple trail and error type method.

Consider a 3 x 3 assignment problem. Here the assignments are done randomly and the total cost is found out.

For 3 x 3 matrix, the total possible trails are 3! So total 3! = 3 x 2 x 1 = 6 trails are possible.

The assignments which gives minimum cost is selected as optimal solution.

But, such trail and error becomes very difficult and lengthy.

If there are more number of rows and columns, ( For eg: For 6 x 6 matrix, there will be 6! trails. So 6! = 6 x 5 x 4 x 3 x 2 x 1 = 720 trails possible) then such methods can't be applied for solving assignments problems.

(iv) Hungarian Method:

It was developed by two mathematicians of Hungary. So, it is known as Hungarian Method.

It is also know as Reduced matrix method or Flood's technique.

There are two main conditions for applying Hungarian Method:

(1) Square Matrix (n x n). (2) Problem should be of minimization type.

Suggested Notes:

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Transportation Model - Introduction

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Tie in selecting row and column (Vogel's Approximation Method - VAM) | Numerical | Solving Transportation Problem | Transportation Model

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Home » Management Science » Transportation and Assignment Models in Operations Research

Transportation and Assignment Models in Operations Research

Transportation and assignment models are special purpose algorithms of the linear programming. The simplex method of Linear Programming Problems(LPP) proves to be inefficient is certain situations like determining optimum assignment of jobs to persons, supply of materials from several supply points to several destinations and the like. More effective solution models have been evolved and these are called assignment and transportation models.

The transportation model is concerned with selecting the routes between supply and demand points in order to minimize costs of transportation subject to constraints of supply at any supply point and demand at any demand point. Assume a company has 4 manufacturing plants with different capacity levels, and 5 regional distribution centres. 4 x 5 = 20 routes are possible. Given the transportation costs per load of each of 20 routes between the manufacturing (supply) plants and the regional distribution (demand) centres, and supply and demand constraints, how many loads can be transported through different routes so as to minimize transportation costs? The answer to this question is obtained easily through the transportation algorithm.

Similarly, how are we to assign different jobs to different persons/machines, given cost of job completion for each pair of job machine/person? The objective is minimizing total cost. This is best solved through assignment algorithm.

Uses of Transportation and Assignment Models in Decision Making

The broad purposes of Transportation and Assignment models in LPP are just mentioned above. Now we have just enumerated the different situations where we can make use of these models.

Transportation model is used in the following:

To decide the transportation of new materials from various centres to different manufacturing plants. In the case of multi-plant company this is highly useful.
To decide the transportation of finished goods from different manufacturing plants to the different distribution centres. For a multi-plant-multi-market company this is useful.
To decide the transportation of finished goods from different manufacturing plants to the different distribution centres. For a multi-plant-multi-market company this is useful. These two are the uses of transportation model. The objective is minimizing transportation cost.

Assignment model is used in the following:

To decide the assignment of jobs to persons/machines, the assignment model is used.
To decide the route a traveling executive has to adopt (dealing with the order inn which he/she has to visit different places).
To decide the order in which different activities performed on one and the same facility be taken up.

In the case of transportation model, the supply quantity may be less or more than the demand. Similarly the assignment model, the number of jobs may be equal to, less or more than the number of machines/persons available. In all these cases the simplex method of LPP can be adopted, but transportation and assignment models are more effective, less time consuming and easier than the LPP.

Economic interpretation of linear programming duality
Waiting Lines and Queuing System in Management Science
Model of Quantitative Analysis
Introduction to Linear Programming (L.P)
Procedure for finding an optimum solution for transportation problem
Introduction to Transportation Problem
Initial basic feasible solution of a transportation problem
Introduction to Crtical Path Analysis
Construction of Mathematical Decision Model
Duality in linear programming

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13 Last Step of Four Step Modeling (Trip Assignment Models)

Chapter overview.

Chapter 13 presents trip assignment, the last step of the Four-Step travel demand Model (FSM). This step determines which paths travelers choose for moving between each pair of zones. Additionally, this step can yield numerous results, such as traffic volumes in different transportation corridors, the patterns of vehicular movements, total vehicle miles traveled (VMT) and vehicle travel time (VTT) in the network, and zone-to-zone travel costs. Identification of the heavily congested links is crucial for transportation planning and engineering practitioners. This chapter begins with some fundamental concepts, such as the link cost functions. Next, it presents some common and useful trip assignment methods with relevant examples. The methods covered in this chapter include all-or-nothing (AON), user equilibrium (UE), system optimum (SO), feedback loop between distribution and assignment (LDA), incremental increase assignment, capacity restrained assignment, and stochastic user equilibrium assignment.

Learning Objectives

Describe the reasons for performing trip assignment models in FSM and relate these models’ foundation through the cost-function concept.
Compare static and dynamic trip assignment models and infer the appropriateness of each model for different situations.
Explain Wardrop principles and relate them to traffic assignment algorithms.
Complete simple network traffic assignment models using static models such as the all-or-nothing and user equilibrium models.
Solve modal split analyses manually for small samples using the discrete choice modeling framework and multinominal logit models.

Introduction

In this chapter, we continue the discussion about FSM and elaborate on different methods of traffic assignment, the last step in the FSM model after trip generation, trip distribution, and modal split. The traffic assignment step, which is also called route assignment or route choice , simulates the choice of route selection from a set of alternatives between the origin and the destination zones (Levinson et al., 2014). The first three FSM steps determine the number of trips produced between each zone and the proportion completed by different transportation modes. The purpose of the final step is to determine the routes or links in the study area that are likely to be used. For example, when updating a Regional Transportation Plan (RTP), traffic assignment is helpful in determining how much shift or diversion in daily traffic happens with the introduction an additional transit line or extension a highway corridor (Levinson et al., 2014). The output from the last step can provide modelers with numerous valuable results. By analyzing the results, the planner can gain insight into the strengths and weaknesses of different transportation plans. The results of trip assignment analysis can be:

The traffic flows in the transportation system and the pattern of vehicular movements.
The volume of traffic on network links.
Travel costs between trip origins and destinations (O-D).
Aggregated network metrics such as total vehicle flow, vehicle miles traveled (VMT) , and vehicle travel time (VTT).
Zone-to-zone travel costs (travel time) for a given level of demand.
Modeled link flows highlighting congested corridors.
Analysis of turning movements for future intersection design.
Determining the Origin-Destination (O-D) pairs using a specific link or path.
Simulation of the individual choice for each pair of origins and destinations (Mathew & Rao, 2006).

Link Performance Function

Building a link performance function is one of the most important and fundamental concepts of the traffic assignment process. This function is usually used for estimating travel time, travel cost, and speed on the network based on the relationship between speed and travel flow. While this function can take different forms, such as linear, polynomial , exponential , and hyperbolic , one of the most common functions is the link performance function which represents generalized travel costs (United States Bureau of Public Roads, 1964). This equation estimates travel time on a free-flow road (travel with speed limit) adding a function that exponentially increases travel time as the road gets more congested. The road volume-to-capacity ratio can represent congestion (Meyer, 2016).

While transportation planners now recognize that intersection delays contribute to link delays, the following sections will focus on the traditional function. Equation (1) is the most common and general formula for the link performance function.

$t=t_o[1+\alpha\left(\frac{x}{k}\right)\beta]$

t and x are the travel time and vehicle flow;
t 0 is the link free flow travel time;
k is the link capacity;
α and β are parameters for specific type of links and calibrated using the field data. In the absence of any field data, it is usually assumed = 0.15, and β= 4.0.

α and β are the coefficients for this formula and can take different values (model parameters). However, most studies and planning practices use the same value for them. These values can be locally calibrated for the most efficient results.

Figure 13.1 demonstrates capacity as the relationship between flow and travel time. In this plot, the travel time remains constant as vehicle volumes increase until the turning point , which indicates that the link’s volume is approaching its capacity.

This figure shows the exponential relationship between travel time and flow of traffic,

The following example shows how the link performance function helps us to determine the travel time according to flow and capacity.

Performance Function Example

Assume the traffic volume on a path between zone i and j was 525. The travel time recorded on this path is 15 minutes. If the capacity of this path would be 550, then calculate the new travel time for future iteration of the model.

Based on the link performance function, we have:

Now we have to plug in the numbers into the formula to determine the new travel time:

$t=15[1+\0.15\left(\frac{525}{550}\right)\4]=16.86$

Traffic Assignment Models

Typically, traffic assignment is calculated for private cars and transit systems independently. Recall that the impedance function differs for drivers and riders, and thus simulating utility maximization behavior should be approached differently. For public transit assignment, variables such as fare, stop or transfer, waiting time, and trip times define the utility (equilibrium) (Sheffi, 1985). For private car assignment, however, in some cases, the two networks are related when public buses share highways with cars, and congestion can also affect the performance.

Typically, private car traffic assignment models the path choice of trip makers using:

algorithms like all-or-nothing
user equilibrium
system optimum assignment

Of the assignment models listed above, user equilibrium is widely adopted in the U.S. (Meyer, 2016). User equilibrium relies on the premise that travelers aim to minimize their travel costs. This algorithm achieves equilibrium when no user can decrease their travel time or cost by altering their travel path.

incremental
capacity-restrained
iterative feedback loop
Stochastic user equilibrium assignment
Dynamic traffic assignment

All-or-nothing Model

Through the all-or-nothing (AON) assignment, it is assumed that the impedance of a road or path between each origin and destination is constant and equal to the free-flow level of service. This means that the traffic time is not affected by the traffic flow on the path. The only logic behind this model is that each traveler uses the shortest path from his or her origin to the destination, and no vehicle is assigned to other paths (Hui, 2014). This method is called the all-or-nothing assignment model and is the simplest one among all assignment models. This method is also called the 0-1 assignment model, and its advantage is its simple procedure and calculation. The assumptions of this method are:

Congestion does not affect travel time or cost, meaning that no matter how much traffic is loaded on the route, congestion does not take place.
Since the method assigns one route to any travel between each pair of OD, all travelers traveling from a particular zone to another particular zone choose the same route (Hui, 2014).

To run the AON model, the following process can be followed:

Step 0: Initialization. Use free flow travel costs Ca=Ca(0) , for each link a on the empty network. Ɐ
Step 1: Path finding. Find the shortest path P for each zonal pair.
Step 2: Path flows assigning. Assign both passenger trips (hppod) and freight trips (hfpod) in PCEs from zonal o to d to path P.
Step 3: Link flows computing. Sum the flows on all paths going through a link as total flows of this link.

Example 2 illustrates the above-mentioned process for the AON model

All-or-nothing Example

Table 13.1 shows a trip distribution matrix with 4 zones. Using the travel costs between each pair of them shown in Figure 13.2, assign the traffic to the network. Load the vehicle trips from the trip distribution table shown below using the AON technique. After assigning the traffic, illustrate the links and the traffic volume on each on them.

Table 13.1 Trip Distribution Results.

This photo shows the hypothetical network and travel time between zones: 1-2: 5 mins 1-4: 10 min 4-2: 4 mins 3-2: 4 mins 3-4: 9 mins

To solve this problem, we need to find the shortest path among all alternatives for each pair of zones. The result of this procedure would be 10 routes in total, each of which bears a specific amount of travels. For instance, the shortest path between zone 1 and 2 is the straight line with 5 min travel time. All other routes like 1 to 4 to 2 or 1 to 4 to 3 to 2 would be empty from travelers going from zone 1 to zone 2. The results are shown in Table 13.2.

As you can see, some of the routes remained unused. This is because in all-or-nothing if a route has longer travel time or higher costs, then it is assumed it would not be used at all.

User Equilibrium

The next method for traffic assignment is called User Equilibrium (UE). The rule or algorithm is adapted from the well-known Wardrop equilibrium (1952) conditions (Correa & Stier-Moses, 2011). In this algorithm, it is assumed that travelers will always choose the shortest path, and equilibrium conditions are realized when no traveler is able to decrease their travel impedance by changing paths (Levinson et al., 2014).

As we discussed, the UE method is based on the first principle of Wardrop : “for each origin- destination (OD) pair, with UE, the travel time on all used paths is equal and less than or equally to the travel time that would be experienced by a single vehicle on any unused path”( Jeihani Koohbanani, 2004, p. 10). The mathematical format of this principle is shown in equation (3):

For a given OD pair, the UE condition can be expressed in equation (3):

$fk\left(ck-u\right)=0:\forall k$

This model assumes that all paths have equal travel time. Additionally, the model includes the following general assumptions:

The users possess all the knowledge needed about different paths.
The users have perfect knowledge of the path cost.
Travel time in a route is subject to change only by the cost flow function of that route.
Travel times increases as we load travel into the network (Mathew & Rao, 2006).

Hence, the UE assignment comes to an optimization problem that can be formulated using equation (4):

$Minimize\ Z=\sum_{a}\int_{0}^{Xa}ta\left(xa\right)dx$

k is the path x a equilibrium flow in link a t a travel time on link a f k rs flow on path connecting OD pairs q rs trip rate between and δ a, k rs is constraint function defined as 1 if link a belongs to path k and 0 otherwise

Example 3 shows how the UE method can be applied for the traffic assignment step. This example is a very simple network consisting of two zones with two possible paths between them.

UE Example

This photo shows the hypothetical network with two possible paths between two zones 1: 5=4x_1 2: 3+2x_2 (to power of two)

In this example, t 1 and t 2 are travel times measured by min on each route, and x 1 and x 2 are traffic flows on each route measured by (Veh/Hour).

Using the UE method, assign 4,500 Veh/Hour to the network and calculate travel time on each route after assignment, traffic volume, and system total travel time.

According to the information provided, total flow (X 1 +X 2 ) is equal to 4,500 (4.5).

First, we need to check, with all traffic assigned to one route, whether that route is still the shortest path. Thus we have:

T 1 (4.5)=23min

T 2 (0)=3min

if all traffic is assigned to route 2:

T 1 (0)=3min

T 2 (4.5)=43.5 min

Step 2: Wardrope equilibrium rule: t 1 =t 2 5+4x 1 =3+ 2x 2 2 and we have x 1 =4.5-x 2

Now the equilibrium equation can be written as: 6 + 4(4.5 − x2)=4+ x222

x 1 = 4.5 − x 2 = 1.58

Now the updated average travel times are: t 1 =5+4(1.58)=11.3min and T 2 =3+2(2.92)2=20.05min

Now the total system travel time is:

Z(x)=X 1 T 1 (X 1 )+X 2 T 2 (X 2 )=2920 veh/hr(11.32)+1585 veh/hr(20.05)=33054+31779=64833 min

System Optimum Assignment

One traffic assignment model is similar to the previous one and is called system optimum (SO). The second principle of the Wardrop defines the model’s logic. Based on this principle, drivers’ rationale for choosing a path is to minimize total system costs with one another to minimize total system travel time (Mathew & Rao, 2006). Using the SO traffic assignment, one can solve various problems, such as optimizing the departure time for a single commuting route, minimizing the total travel time from multiple origins to a single destination, or minimizing travel time in stochastic time-dependent O-D flows from several origins to a single destination ( Jeihani & Koohbanani, 2004).

One other traffic assignment model similar to the previous one is called system optimum (SO) in which the second principle of the Wardrop defines the logic of the model. Based on this principle, drivers’ rationale for choosing a path is to minimize total system costs with one another in order to minimize total system travel time (Mathew & Rao, 2006). Using the SO traffic assignment, problems like optimizing departure time for a single commuting route, minimizing total travels from multiple origins to one destination, or minimizing travel time in stochastic time-dependent OD flows from several origins to a single destination can be solved (Jeihani Koohbanani, 2004).

The basic mathematical formula for this model that satisfies the principle of the model is shown in equation (5):

$minimize\ Z=\sum_{a}{xata\left(xa\right)}$

In example 4, we will use the same network we described in the UE example in order to compare the results for the two models.

In that simple two-zone network, we had:

T 1 =5+4X 1 T2=3+2X 2 2

Now, based on the principle of the model we have:

Z(x)=x 1 t 1 (x 1 )+x 2 t 2 (x 2 )

Z(x)=x 1 (5+4x 1 )+x 2 (3+2x 2 2 )

Z(x)=5x 1 +4x 1 2 +3x 2 +2x 2 3

From the flow conservation. we have: x 1 +x 2 =4.5 x 1 =4.5-x 2

Z(x)=5(4.5-x 2 )+4(4.5-x 2 )2+4x 2 +x 2 3

Z(x)=x 3 2 +4x 2 2 -27x 2 +103.5

In order to minimize the above equation, we have to take derivatives and equate it to zero. After doing the calculations, we have:

Based on our finding, the system travel time would be:

T 1 =5+4*1.94=12.76min T 2 =3+ 2(2.56)2=10.52 min

And the total travel time of the system would be:

Z(x)=X 1 T 1 (X 1 )+X 2 T 2 (X 2 )=1940 veh/hr(12.76)+2560 veh/hr(10.52)=24754+26931=51685 min

Incremental Increase model

Incremental increase is based on the logic of the AON model and models a process designed with multiple steps. In each step or level, a fraction of the total traffic volume is assigned, and travel time is calculated based on the allocated traffic volume. Through this incremental addition of traffic, the travel time of each route in step (n) is the updated travel time from the previous step (n-1) (Rojo, 2020).

The steps for the incremental increase traffic assignment model are:

Finding the shortest path between each pair of O-Ds (Origin Destination).
Assigning a portion of the trips according to the matrix (usually 40, 30, 20 and 10 percent to the shortest path).
Updating the travel time after each iteration (each incremental increase).
Continuing until all trips are assigned.
Summing the results.

The example below illustrates the implementation process of this method.

A hypothetical network accommodates two zones with three possible links between them. Perform an incremental increase traffic assignment model for assigning 200 trips between the two zones with increments of: 30%, 30%, 20%, 20%. (The capacity is 50 trips.)

Incremental Increase Example

This photo shows the hypothetical network with two possible paths between two zones 1: 6 mins 2: 7 mins 3: 12 mins

Step 1 (first iteration): Using the method of AON, we now assign the flow to the network using the function below:

$t=to[1+\alpha\left(\frac{x}{k}\right)\beta]$

Since the first route has the shortest travel time, the first 30% of the trips will be assigned to route 1. The updated travel time for this path would be:

$t=6\left[1+0.15\left(\frac{60}{50}\right)4\right]=7.86$

And the remaining route will be empty, and thus their travel times are unchanged.

Step 2 (second iteration): Now, we can see that the second route has the shortest travel time, with 30% of the trips being assigned to this route, and the new travel time would be:

$t=7\left[1+0.15\left(\frac{60}{50}\right)4\right]=9.17$

Step 3 (third iteration): In the third step, the 20% of the remaining trips will be assigned to the shortest path, which in this case is the first route again. The updated travel time for this route is:

$t=7.86\left[1+0.15\left(\frac{40}{50}\right)4\right]=8.34$

Step 4 (fourth iteration): In the last iteration, the remaining 10% would be assigned to first route, and the time is:

$t=8.34\left[1+0.15\left(\frac{40}{50}\right)4\right]=8.85$

Finally, we can see that route 1 has a total of 140 trips with a 8.85 travel time, the second route has a total of 60 trips with a 9.17 travel time, and the third route was never used.

Capacity Restraint Assignment

So far, all the presented algorithms or rules have considered the model’s link capacity. The flow is assigned to a link based on travel time as the only factor. In this model, after each iteration, the total number of trips is compared with the capacity to observe how much increase in travel time was realized by the added volume. In this model, the iteration stops if the added volume in step (n) does not change the travel time updated in step (n-1). With the incorporation of such a constraint, the cost or performance function would be different from the cost functions discussed in previous algorithms (Mathew & Rao, 2006). Figure 13.6 visualizes the relationship between flow and travel time with a capacity constraint.

This figure shows the exponential relationship between travel time and flow of traffic with capacity line.

Based on this capacity constraint specific to each link, the α, β can be readjusted for different links such as highways, freeways, and other roads.

Feedback Loop Model (Combined Traffic Assignment and Trip Distribution)

The feedback loop model defines an interaction between the trip distribution route choice step with several iterations. The model allows travelers to change their destination if a route is congested. For example, the feedback loop models that the traveler has a choice of similar destinations, such as shopping malls, in the area. In other words, in a real-world situation, travelers usually simultaneously decide about their travel characteristics (Qasim, 2012).

The chart below shows how the combination of these two modes can take place:

This photo shows the feedback loop in FSM.

Equation (6), shown below for this model, ensures convergence at the end of the model is:

$Min\funcapply\sum_a\hairsp\int_0^{p_a+f_a}\hairsp C_a(x)dx+\frac{1}{\zeta}\sum_o\hairsp\sum_d\hairsp T^{od}\left(\ln\funcapply T^{od}-K\right)$

where C a (t) is the same as previous

P a , is total personal trip flows on link a,

f a ; is total freight trip flows on link a,

T od is the total flow from node o to node d,

p od is personal trip from node o to node d,

F od is freight trip from node o to node d,

ζ is a parameter estimated from empirical data,

K is a parameter depending on the type of gravity model used to calculate T od , Evans (1976) proved that K’ equals to 1 for distribution using doubly constrained gravity model and it equals to 1 plus attractiveness for distribution using singly constrained model. Florian et al. (1975) ignored K for distribution using a doubly constrained gravity model because it is a constant.

Stochastic User Equilibrium Traffic Assignment

Stochastic user equilibrium traffic assignment is a sophisticated and more realistic model in which the level of uncertainty regarding which link should be used based on a measurement of utility function is introduced. This model performs a discrete choice analysis through a logistic model. Based on the first Wardrop principle, this model assumes that all drivers perceive the costs of traveling in each link identically and choose the route with minimum cost. In stochastic UE, however, the model allows different individuals to have different perceptions about the costs, and thus, they may choose non-minimum cost routes (Mathew & Rao, 2006). In this model, flow is assigned to all links from the beginning, unlike previous models, which is closer to reality. The probability of using each path is calculated with the following logit formula shown in equation (7):

$Pi=\frac{e^{ui}}{\sum_{i=1}^{k}e^{ui}}$

P i is the probability of using path i

U i is the utility function for path i

In the following, an example of a simple network is presented.

Stochastic User Equilibrium Example

There is a flow of 200 trips between two points and their possible path, each of which has a travel time specified in Figure 13.7.

This photo shows the hypothetical network with two possible paths between two zones 1: 21 mins 2: 23 mins 3: 26 mins

Using the mentioned logit formula for these paths, we have:

$P1=\frac{e^{-21i}}{e^{-21i}+e^{-23}+e^{-26i}}=0.875$

Based on the calculated probabilities, the distribution of the traffic flow would be:

Q 1 =175 trips

Q 2 =24 trips

Q 3 =1 trips

Dynamic Traffic Assignment

Recall the first Wardrop principle, in which travelers are believed to choose their routes with the minimum cost. Dynamic traffic assignment is based on the same rule, but the difference is that delays result from congestion. In this way, not only travelers’ route choice affects the network’s level of service, but also the network’s level of service affects travelers’ choice. However, it is not theoretically proven that an equilibrium would result under such conditions (Mathew & Rao, 2006).

Today, various algorithms are developed to solve traffic assignment problems. In any urban transportation system, travelers’ route choice and different links’ level of service have a dynamic feedback loop and affect each other simultaneously. However, a lot of these rules are not present in the models presented here. In real world cases, there can be more than thousands of nodes and links in the network, and therefore more sensitivity to dynamic changes is required for a realistic traffic assignment (Meyer, 2016). Also, the travel demand model applies a linear sequence of the four steps, which is unlike reality. Additionally, travelers may have only a limited knowledge of all possible paths, modes, and opportunities and may not make rational decisions.

In this last chapter of landuse/transportation modeling book, we reviewed the basic concepts and principles of traffic assignment models as the last step in travel demand modeling. Modeling the route choice and other components of travel behavior and demand for transportation proven to be very challenging and can incorporate multiple factors. For instance, going from AON to incremental increase assignment, we factor in the capacity and volume (and resulting delays) relationship in the assignment to make more realistic models. Multiple-time-period assignments for multiple classes, separate specification of facilities like high-occupancy vehicle (HOV) and high-occupancy toll (HOT) lanes; and, independent transit assignment using congested highway travel times to estimate a bus ridership assignment, are some of the new extensions and variation of algorithms that take into account more realities within transportation network. A new prospect in traffic assignment models that adds several capabilities for such efforts is emergence of ITS such as data that can be collected from connected vehicles or autonomous vehicles. Using these data, perceived utility or impedances of different modes or infrastructure from individuals perspective can be modeled accurately, leading to more accurate assignment models, which are crucial planning studies such as growth and land use control efforts, environmental studies, transportation economies, etc.

Route choice is the process of choosing a certain path for a trip from a very large choice sets.

Regional Transportation Plan is long term planning document for a region’s transportation usually updated every five years.

Vehicles (VMT) is the aggregate number of miles deriven from in an area in particular time of day.

Total vehicle travel time is the aggregate amount of time spent in transportation usually in minutes.

Link performance function is function used for estimating travel time, travel cost, and speed on the network based on the relationship between speed and travel flow.

Hyperbolic function is a function used for linear differential equations like calculating distances and angels in hyperbolic geometry.

Free-flow road is situation where vehicles can travel with the maximum allowed travel speed.

Algorithms like all-or-nothing an assignment model where we assume that the impedance of a road or path between each origin and destination is constant and is equal to free-flow level of service, meaning that the traffic time is not affected by the traffic flow on the path.

Capacity-restrained is a model which takes into account the capacity of a road compared to volume and updates travel times.

User equilibrium is a traffic assignment model where we assume that travelers will always choose the shortest path and equilibrium condition would be realized when no traveler is able to decrease their travel impedance by changing paths.

System optimum assignment is an assignment model based on the principle that drivers’ rationale for choosing a path is to minimize total system costs with one another in order to minimize total system travel time.

Static user-equilibrium assignment algorithm is an iterative traffic assignment process which assumes that travelers chooses the travel path with minimum travel time subject to constraints.
Iterative feedback loop is a model that iterates between trip distribution and route choice step based on the rational that if a path gets too congested, the travel may alter travel destination.

First principle of Wardrop is the assumption that for each origin-destination (OD) pair, with UE, the travel time on all used paths is equal and less than or equally to the travel time that would be experienced by a single vehicle on any unused path.

System optimum (SO) is a condition in trip assignment model where total travel time for the whole area is at a minimum.

Stochastic time-dependent OD is a modeling framework where generation and distribution of trips are randomly assigned to the area.

Incremental increase is AON-based model with multiple steps in each of which, a fraction of the total traffic volume is assigned, and travel time is calculated based on the allocated traffic volume.

Stochastic user equilibrium traffic assignment employs a probability distribution function that controls for uncertainties when drivers compare alternative routes and make decisions.

Dynamic traffic assignment is a model based on Wardrop first principle in which delays resulted from congestion is incorporated in the algorithm.

Key Takeaways

In this chapter, we covered:

Traffic assignment is the last step of FSM, and the link cost function is a fundamental concept for traffic assignment.
Different static and dynamic assignments and how to perform them using a simplistic transportation network.
Incorporating stochastic decision-making about route choice and how to solve assignment problems with regard to this feature.

Prep/quiz/assessments

Explain what the link performance function is in trip assignment models and how it is related to link capacity.
Name a few static and dynamic traffic assignment models and discuss how different their rules or algorithms are.
How does stochastic decision-making on route choice affect the transportation level of service, and how it is incorporated into traffic assignment problems?
Name one extension of the all-or-nothing assignment model and explain how this extension improves the model results.

Correa, J.R., & Stier-Moses, N.E.(2010).Wardrope equilibria. In J.J. Cochran( Ed.), Wiley encyclopedia of operations research and management science (pp.1–12). Hoboken, NJ: John Wiley & Sons. http://dii.uchile.cl/~jcorrea/papers/Chapters/CS2010.pdf

Hui, C. (2014). Application study of all-or-nothing assignment method for determination of logistic transport route in urban planning. Computer Modelling & New Technologies , 18 , 932–937. http://www.cmnt.lv/upload-files/ns_25crt_170vr.pdf

Jeihani Koohbanani, M. (2004). Enhancements to transportation analysis and simulation systems (Unpublished Doctoral dissertation, Virginia Tech). https://vtechworks.lib.vt.edu/bitstream/handle/10919/30092/dissertation-final.pdf?sequence=1&isAllowed=y

Levinson, D., Liu, H., Garrison, W., Hickman, M., Danczyk, A., Corbett, M., & Dixon, K. (2014). Fundamentals of transportation . Wikimedia. https://upload.wikimedia.org/wikipedia/commons/7/79/Fundamentals_of_Transportation.pdf

Mathew, T. V., & Rao, K. K. (2006). Introduction to transportation engineering. Civil engineering–Transportation engineering. IIT Bombay, NPTEL ONLINE, Http://Www. Cdeep. Iitb. Ac. in/Nptel/Civil% 20Engineering .

Meyer, M. D. (2016). Transportation planning handbook . John Wiley & Sons.

Qasim, G. (2015). Travel demand modeling: AL-Amarah city as a case study . [Unpublished Doctoral dissertation , the Engineering College University of Baghdad]

Rojo, M. (2020). Evaluation of traffic assignment models through simulation. Sustainability , 12 (14), 5536. https://doi.org/10.3390/su12145536

Sheffi, Y. (1985). Urban transportation networks: Equilibrium analysis with mathematical programming method . Prentice-Hall. http://web.mit.edu/sheffi/www/selectedMedia/sheffi_urban_trans_networks.pdf

US Bureau of Public Roads. (1964). Traffic assignment manual for application with a large, high speed computer . U.S. Department of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division.

https://books.google.com/books/about/Traffic_Assignment_Manual_for_Applicatio.html?id=gkNZAAAAMAAJ

Wang, X., & Hofe, R. (2008). Research methods in urban and regional planning . Springer Science & Business Media.

Polynomial is distribution that involves the non-negative integer powers of a variable.

Hyperbolic function is a function that the uses the variable values as the power to the constant of e.

A point on the curve where the derivation of the function becomes either maximum or minimum.

all-or-nothing is an assignment model where we assume that the impedance of a road or path between each origin and destination is constant and is equal to free-flow level

Incremental model is a model that the predictions or estimates or fed into the model for forecasting incrementally to account for changes that may occur during each increment.

Iterative feedback loop is a model that iterates between trip distribution and route choice step based on the rational that if a path gets too congested, the travel may alter travel destination

Wardrop equilibrium is a state in traffic assignment model where are drivers are reluctant to change their path because the average travel time is at a minimum.

second principle of the Wardrop is a principle that assumes drivers’ rationale for choosing a path is to minimize total system costs with one another in order to minimize total system travel time

Stochastic time-dependent OD is a modeling framework where generation and distribution of trips are randomly assigned to the area

feedback loop model is type of dynamic traffic assignment model where an iteration between route choice and traffic assignment step is peformed, based on the assumption that if a particular route gets heavily congested, the travel may change the destination (like another shopping center).

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Traffic Assignments to Transportation Networks

First Online: 01 January 2014

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Dietmar P. F. Möller 3

Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

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This chapter begins with a brief overview of traffic assignment in transportation systems. Section 3.1 introduces the assignment problem in transportation as the distribution of traffic in a network considering the demand between locations and the transport supply of the network. Four trip assignment models relevant to transportation are presented and characterized. Section 3.2 covers traffic assignment to uncongested networks based on the assumption that cost does not depend on traffic flow. Section 3.3 introduces the topic of traffic assignment and congested models based on assumptions from traffic flow modeling, e.g., each vehicle is traveling at the legal velocity, v , and each vehicle driver is following the preceding vehicle at a legal safe velocity. Section 3.4 covers the important topic of equilibrium assignment which can be expressed by the so-called fixed-point models where origin to destination (O-D) demands are fixed, representing systems of nonlinear equations or variational inequalities. Equilibrium models are also used to predict traffic patterns in transportation networks that are subject to congestion phenomena. Section 3.5 presents the topic of multiclass assignment, which is based on the assumption that travel demand can be allocated as a number of distinct classes which share behavioral characteristics. In Sect. 3.6, dynamic traffic assignment is introduced which allows the simultaneous determination of a traveler’s choice of departure time and path. With this approach, phenomenon such as peak spreading in response to congestion dynamics or time-varying tolls can be directly analyzed. In Sect. 3.7, transportation network synthesis is introduced which focuses on the modification of a transportation road network to fit a required demand. Section 3.8 covers a case study involving a diverging diamond interchange (DDI), an interchange in which the two directions of traffic on a nonfreeway road cross to the opposite side on both sides of a freeway overpass. The DDI requires traffic on the freeway overpass (or underpass) to briefly drive on the opposite side of the road. Section 3.9 contains comprehensive questions from the transportation system area. A final section includes references and suggestions for further reading.

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Hughes W, Jagannathan R (2010) Double crossover diamond interchange. TECHBRIEF FHWA-HRT-09-054, U.S. Department of Transportation, Federal Highway Administration, Washington, DC, FHWA contact: J. Bared, 202-493-3314

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Yang H, Huang H-J (2004) The multi-class multi-criteria traffic network equilibrium and systems optimum problem. Transport Res Part B 38:1–15

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Möller, D.P.F. (2014). Traffic Assignments to Transportation Networks. In: Introduction to Transportation Analysis, Modeling and Simulation. Simulation Foundations, Methods and Applications. Springer, London. https://doi.org/10.1007/978-1-4471-5637-6_3

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Designing Assignments for Learning

The rapid shift to remote teaching and learning meant that many instructors reimagined their assessment practices. Whether adapting existing assignments or creatively designing new opportunities for their students to learn, instructors focused on helping students make meaning and demonstrate their learning outside of the traditional, face-to-face classroom setting. This resource distills the elements of assignment design that are important to carry forward as we continue to seek better ways of assessing learning and build on our innovative assignment designs.

Reflect On Your Assignment Design

Connect with the ctl.

Resources and References

Cite this resource: Columbia Center for Teaching and Learning (2021). Designing Assignments for Learning. Columbia University. Retrieved [today’s date] from https://ctl.columbia.edu/resources-and-technology/teaching-with-technology/teaching-online/designing-assignments/

Traditional assessments tend to reveal whether students can recognize, recall, or replicate what was learned out of context, and tend to focus on students providing correct responses (Wiggins, 1990). In contrast, authentic assignments, which are course assessments, engage students in higher order thinking, as they grapple with real or simulated challenges that help them prepare for their professional lives, and draw on the course knowledge learned and the skills acquired to create justifiable answers, performances or products (Wiggins, 1990). An authentic assessment provides opportunities for students to practice, consult resources, learn from feedback, and refine their performances and products accordingly (Wiggins 1990, 1998, 2014).

Authentic assignments ask students to “do” the subject with an audience in mind and apply their learning in a new situation. Examples of authentic assignments include asking students to:

Write for a real audience (e.g., a memo, a policy brief, letter to the editor, a grant proposal, reports, building a website) and/or publication;
Solve problem sets that have real world application;
Design projects that address a real world problem;
Engage in a community-partnered research project;
Create an exhibit, performance, or conference presentation ;
Compile and reflect on their work through a portfolio/e-portfolio.

Noteworthy elements of authentic designs are that instructors scaffold the assignment, and play an active role in preparing students for the tasks assigned, while students are intentionally asked to reflect on the process and product of their work thus building their metacognitive skills (Herrington and Oliver, 2000; Ashford-Rowe, Herrington and Brown, 2013; Frey, Schmitt, and Allen, 2012).

It’s worth noting here that authentic assessments can initially be time consuming to design, implement, and grade. They are critiqued for being challenging to use across course contexts and for grading reliability issues (Maclellan, 2004). Despite these challenges, authentic assessments are recognized as beneficial to student learning (Svinicki, 2004) as they are learner-centered (Weimer, 2013), promote academic integrity (McLaughlin, L. and Ricevuto, 2021; Sotiriadou et al., 2019; Schroeder, 2021) and motivate students to learn (Ambrose et al., 2010). The Columbia Center for Teaching and Learning is always available to consult with faculty who are considering authentic assessment designs and to discuss challenges and affordances.

Examples from the Columbia University Classroom

Columbia instructors have experimented with alternative ways of assessing student learning from oral exams to technology-enhanced assignments. Below are a few examples of authentic assignments in various teaching contexts across Columbia University.

E-portfolios: Statia Cook shares her experiences with an ePorfolio assignment in her co-taught Frontiers of Science course (a submission to the Voices of Hybrid and Online Teaching and Learning initiative); CUIMC use of ePortfolios ;
Case studies: Columbia instructors have engaged their students in authentic ways through case studies drawing on the Case Consortium at Columbia University. Read and watch a faculty spotlight to learn how Professor Mary Ann Price uses the case method to place pre-med students in real-life scenarios;
Simulations: students at CUIMC engage in simulations to develop their professional skills in The Mary & Michael Jaharis Simulation Center in the Vagelos College of Physicians and Surgeons and the Helene Fuld Health Trust Simulation Center in the Columbia School of Nursing;
Experiential learning: instructors have drawn on New York City as a learning laboratory such as Barnard’s NYC as Lab webpage which highlights courses that engage students in NYC;
Design projects that address real world problems: Yevgeniy Yesilevskiy on the Engineering design projects completed using lab kits during remote learning. Watch Dr. Yesilevskiy talk about his teaching and read the Columbia News article .
Writing assignments: Lia Marshall and her teaching associate Aparna Balasundaram reflect on their “non-disposable or renewable assignments” to prepare social work students for their professional lives as they write for a real audience; and Hannah Weaver spoke about a sandbox assignment used in her Core Literature Humanities course at the 2021 Celebration of Teaching and Learning Symposium . Watch Dr. Weaver share her experiences.

Tips for Designing Assignments for Learning

While designing an effective authentic assignment may seem like a daunting task, the following tips can be used as a starting point. See the Resources section for frameworks and tools that may be useful in this effort.

Align the assignment with your course learning objectives

Identify the kind of thinking that is important in your course, the knowledge students will apply, and the skills they will practice using through the assignment. What kind of thinking will students be asked to do for the assignment? What will students learn by completing this assignment? How will the assignment help students achieve the desired course learning outcomes? For more information on course learning objectives, see the CTL’s Course Design Essentials self-paced course and watch the video on Articulating Learning Objectives .

Identify an authentic meaning-making task

For meaning-making to occur, students need to understand the relevance of the assignment to the course and beyond (Ambrose et al., 2010). To Bean (2011) a “meaning-making” or “meaning-constructing” task has two dimensions: 1) it presents students with an authentic disciplinary problem or asks students to formulate their own problems, both of which engage them in active critical thinking, and 2) the problem is placed in “a context that gives students a role or purpose, a targeted audience, and a genre.” (Bean, 2011: 97-98).

An authentic task gives students a realistic challenge to grapple with, a role to take on that allows them to “rehearse for the complex ambiguities” of life, provides resources and supports to draw on, and requires students to justify their work and the process they used to inform their solution (Wiggins, 1990). Note that if students find an assignment interesting or relevant, they will see value in completing it.

Consider the kind of activities in the real world that use the knowledge and skills that are the focus of your course. How is this knowledge and these skills applied to answer real-world questions to solve real-world problems? (Herrington et al., 2010: 22). What do professionals or academics in your discipline do on a regular basis? What does it mean to think like a biologist, statistician, historian, social scientist? How might your assignment ask students to draw on current events, issues, or problems that relate to the course and are of interest to them? How might your assignment tap into student motivation and engage them in the kinds of thinking they can apply to better understand the world around them? (Ambrose et al., 2010).

Determine the evaluation criteria and create a rubric

To ensure equitable and consistent grading of assignments across students, make transparent the criteria you will use to evaluate student work. The criteria should focus on the knowledge and skills that are central to the assignment. Build on the criteria identified, create a rubric that makes explicit the expectations of deliverables and share this rubric with your students so they can use it as they work on the assignment. For more information on rubrics, see the CTL’s resource Incorporating Rubrics into Your Grading and Feedback Practices , and explore the Association of American Colleges & Universities VALUE Rubrics (Valid Assessment of Learning in Undergraduate Education).

Build in metacognition

Ask students to reflect on what and how they learned from the assignment. Help students uncover personal relevance of the assignment, find intrinsic value in their work, and deepen their motivation by asking them to reflect on their process and their assignment deliverable. Sample prompts might include: what did you learn from this assignment? How might you draw on the knowledge and skills you used on this assignment in the future? See Ambrose et al., 2010 for more strategies that support motivation and the CTL’s resource on Metacognition ).

Provide students with opportunities to practice

Design your assignment to be a learning experience and prepare students for success on the assignment. If students can reasonably expect to be successful on an assignment when they put in the required effort ,with the support and guidance of the instructor, they are more likely to engage in the behaviors necessary for learning (Ambrose et al., 2010). Ensure student success by actively teaching the knowledge and skills of the course (e.g., how to problem solve, how to write for a particular audience), modeling the desired thinking, and creating learning activities that build up to a graded assignment. Provide opportunities for students to practice using the knowledge and skills they will need for the assignment, whether through low-stakes in-class activities or homework activities that include opportunities to receive and incorporate formative feedback. For more information on providing feedback, see the CTL resource Feedback for Learning .

Communicate about the assignment

Share the purpose, task, audience, expectations, and criteria for the assignment. Students may have expectations about assessments and how they will be graded that is informed by their prior experiences completing high-stakes assessments, so be transparent. Tell your students why you are asking them to do this assignment, what skills they will be using, how it aligns with the course learning outcomes, and why it is relevant to their learning and their professional lives (i.e., how practitioners / professionals use the knowledge and skills in your course in real world contexts and for what purposes). Finally, verify that students understand what they need to do to complete the assignment. This can be done by asking students to respond to poll questions about different parts of the assignment, a “scavenger hunt” of the assignment instructions–giving students questions to answer about the assignment and having them work in small groups to answer the questions, or by having students share back what they think is expected of them.

Plan to iterate and to keep the focus on learning

Draw on multiple sources of data to help make decisions about what changes are needed to the assignment, the assignment instructions, and/or rubric to ensure that it contributes to student learning. Explore assignment performance data. As Deandra Little reminds us: “a really good assignment, which is a really good assessment, also teaches you something or tells the instructor something. As much as it tells you what students are learning, it’s also telling you what they aren’t learning.” ( Teaching in Higher Ed podcast episode 337 ). Assignment bottlenecks–where students get stuck or struggle–can be good indicators that students need further support or opportunities to practice prior to completing an assignment. This awareness can inform teaching decisions.

Triangulate the performance data by collecting student feedback, and noting your own reflections about what worked well and what did not. Revise the assignment instructions, rubric, and teaching practices accordingly. Consider how you might better align your assignment with your course objectives and/or provide more opportunities for students to practice using the knowledge and skills that they will rely on for the assignment. Additionally, keep in mind societal, disciplinary, and technological changes as you tweak your assignments for future use.

Now is a great time to reflect on your practices and experiences with assignment design and think critically about your approach. Take a closer look at an existing assignment. Questions to consider include: What is this assignment meant to do? What purpose does it serve? Why do you ask students to do this assignment? How are they prepared to complete the assignment? Does the assignment assess the kind of learning that you really want? What would help students learn from this assignment?

Using the tips in the previous section: How can the assignment be tweaked to be more authentic and meaningful to students?

As you plan forward for post-pandemic teaching and reflect on your practices and reimagine your course design, you may find the following CTL resources helpful: Reflecting On Your Experiences with Remote Teaching , Transition to In-Person Teaching , and Course Design Support .

The Columbia Center for Teaching and Learning (CTL) is here to help!

For assistance with assignment design, rubric design, or any other teaching and learning need, please request a consultation by emailing [email protected] .

Transparency in Learning and Teaching (TILT) framework for assignments. The TILT Examples and Resources page ( https://tilthighered.com/tiltexamplesandresources ) includes example assignments from across disciplines, as well as a transparent assignment template and a checklist for designing transparent assignments . Each emphasizes the importance of articulating to students the purpose of the assignment or activity, the what and how of the task, and specifying the criteria that will be used to assess students.

Association of American Colleges & Universities (AAC&U) offers VALUE ADD (Assignment Design and Diagnostic) tools ( https://www.aacu.org/value-add-tools ) to help with the creation of clear and effective assignments that align with the desired learning outcomes and associated VALUE rubrics (Valid Assessment of Learning in Undergraduate Education). VALUE ADD encourages instructors to explicitly state assignment information such as the purpose of the assignment, what skills students will be using, how it aligns with course learning outcomes, the assignment type, the audience and context for the assignment, clear evaluation criteria, desired formatting, and expectations for completion whether individual or in a group.

Villarroel et al. (2017) propose a blueprint for building authentic assessments which includes four steps: 1) consider the workplace context, 2) design the authentic assessment; 3) learn and apply standards for judgement; and 4) give feedback.

References

Ambrose, S. A., Bridges, M. W., & DiPietro, M. (2010). Chapter 3: What Factors Motivate Students to Learn? In How Learning Works: Seven Research-Based Principles for Smart Teaching . Jossey-Bass.

Ashford-Rowe, K., Herrington, J., and Brown, C. (2013). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education. 39(2), 205-222, http://dx.doi.org/10.1080/02602938.2013.819566 .

Bean, J.C. (2011). Engaging Ideas: The Professor’s Guide to Integrating Writing, Critical Thinking, and Active Learning in the Classroom . Second Edition. Jossey-Bass.

Frey, B. B, Schmitt, V. L., and Allen, J. P. (2012). Defining Authentic Classroom Assessment. Practical Assessment, Research, and Evaluation. 17(2). DOI: https://doi.org/10.7275/sxbs-0829

Herrington, J., Reeves, T. C., and Oliver, R. (2010). A Guide to Authentic e-Learning . Routledge.

Herrington, J. and Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23-48.

Litchfield, B. C. and Dempsey, J. V. (2015). Authentic Assessment of Knowledge, Skills, and Attitudes. New Directions for Teaching and Learning. 142 (Summer 2015), 65-80.

Maclellan, E. (2004). How convincing is alternative assessment for use in higher education. Assessment & Evaluation in Higher Education. 29(3), June 2004. DOI: 10.1080/0260293042000188267

McLaughlin, L. and Ricevuto, J. (2021). Assessments in a Virtual Environment: You Won’t Need that Lockdown Browser! Faculty Focus. June 2, 2021.

Mueller, J. (2005). The Authentic Assessment Toolbox: Enhancing Student Learning through Online Faculty Development . MERLOT Journal of Online Learning and Teaching. 1(1). July 2005. Mueller’s Authentic Assessment Toolbox is available online.

Schroeder, R. (2021). Vaccinate Against Cheating With Authentic Assessment . Inside Higher Ed. (February 26, 2021).

Sotiriadou, P., Logan, D., Daly, A., and Guest, R. (2019). The role of authentic assessment to preserve academic integrity and promote skills development and employability. Studies in Higher Education. 45(111), 2132-2148. https://doi.org/10.1080/03075079.2019.1582015

Stachowiak, B. (Host). (November 25, 2020). Authentic Assignments with Deandra Little. (Episode 337). In Teaching in Higher Ed . https://teachinginhighered.com/podcast/authentic-assignments/

Svinicki, M. D. (2004). Authentic Assessment: Testing in Reality. New Directions for Teaching and Learning. 100 (Winter 2004): 23-29.

Villarroel, V., Bloxham, S, Bruna, D., Bruna, C., and Herrera-Seda, C. (2017). Authentic assessment: creating a blueprint for course design. Assessment & Evaluation in Higher Education. 43(5), 840-854. https://doi.org/10.1080/02602938.2017.1412396

Weimer, M. (2013). Learner-Centered Teaching: Five Key Changes to Practice . Second Edition. San Francisco: Jossey-Bass.

Wiggins, G. (2014). Authenticity in assessment, (re-)defined and explained. Retrieved from https://grantwiggins.wordpress.com/2014/01/26/authenticity-in-assessment-re-defined-and-explained/

Wiggins, G. (1998). Teaching to the (Authentic) Test. Educational Leadership . April 1989. 41-47.

Wiggins, Grant (1990). The Case for Authentic Assessment . Practical Assessment, Research & Evaluation , 2(2).

Wondering how AI tools might play a role in your course assignments?

See the CTL’s resource “Considerations for AI Tools in the Classroom.”

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Kerry Walk, former director of the Princeton Writing Program, offers these principles to consider when designing a writing assignment (condensed and adapted from the original): “At least one sentence on your assignment sheet should explicitly state what you want students to do. The assignment is usually signaled by a verb, such as “analyze,” “assess,” “explain,” or “discuss.” For example, in a history course, after reading a model biography, students were directed as follows: ‘Your assignment is to write your own biographical essay on Mao, using Mao’s reminiscences (as told to a Western journalist), speeches, encyclopedia articles, a medical account from Mao’s physician, and two contradictory obituaries.’ In addition, including a purpose for the assignment can provide crucial focus and guidance. Explaining to students why they’re doing a particular assignment can help them grasp the big picture—what you’re trying to teach them and why learning it is worthwhile. For example, ‘This assignment has three goals: for you to (1) see how the concepts we’ve learned thus far can be used in a different field from economics, (2) learn how to write about a model, and (3) learn to critique a model or how to defend one.’”

Link course writing goals to assignments

Students are more likely to understand what you are asking them to do if the assignment re-uses language that you’ve already introduced in class discussions, in writing activities, or in your Writing Guide. In the assignment below, Yale professor Dorlores Hayden uses writing terms that have been introduced in class:

Choose your home town or any other town or city you have lived in for at least a year. Based upon the readings on the history of transportation, discuss how well or how poorly pedestrian, horse-drawn, steam- powered, and electric transportation might have served your town or city before the gasoline automobile. (If you live in a twentieth-century automobile-oriented suburb, consider rural transportation patterns before the car and the suburban houses.) How did topography affect transportation choices? How did transportation choices affect the local economy and the built environment? Length, 1000 words (4 typed pages plus a plan of the place and/or a photograph). Be sure to argue a strong thesis and back it up with quotations from the readings as well as your own analysis of the plan or photograph.

Give students methods for approaching their work

Strong writing assignments not only identify a clear writing task, they often provide suggestions for how students might begin to accomplish the task. In order to avoid overloading students with information and suggestions, it is often useful to separate the assignment prompt and the advice for approaching the assignment. Below is an example of this strategy from one of Yale’s English 114 sections:

Assignment: In the essays we have read so far, a debate has emerged over what constitutes cosmopolitan practice , loosely defined as concrete actions motivated by a cosmopolitan philosophy or perspective. Using these readings as evidence, write a 5-6-page essay in which you make an argument for your own definition of effective cosmopolitan practice.

Method: In order to develop this essay, you must engage in a critical conversation with the essays we have read in class. In creating your definition of cosmopolitan practice, you will necessarily draw upon the ideas of these authors. You must show how you are building upon, altering, or working in opposition to their ideas and definitions through your quotation and analysis of their concepts and evidence.

Questions to consider: These questions are designed to prompt your thinking. You do not need to address all these questions in the body of your essay; instead, refer to any of these issues only as they support your ideas.

How would you define cosmopolitan practice? How does your definition draw upon or conflict with the definitions offered by the authors we have read so far?
What are the strengths of your definition of cosmopolitan practice? What problems does it address? How do the essays we have read support those strengths? How do those strengths address weaknesses in other writers’ arguments?
What are the limitations or problems with your definition? How would the authors we have read critique your definition? How would you respond to those critiques?

Case Study: A Sample Writing Assignment and Revision

A student responding to the following assignment felt totally at sea, with good reason:

Write an essay describing the various conceptions of property found in your readings and the different arguments for and against the distribution of property and the various justifications of, and attacks on, ownership. Which of these arguments has any merits? What is the role of property in the various political systems discussed? The essay should concentrate on Hobbes, Locke, and Marx.

“How am I supposed to structure the essay?” the student asked. “Address the first question, comparing the three guys? Address the second question, doing the same, etc.? … Do I talk about each author separately in terms of their conceptions of the nation, and then have a section that compares their arguments, or do I have a 4 part essay which is really 4 essays (two pages each) answering each question? What am I going to put in the intro, and the conclusion?” Given the tangle of ideas presented in the assignment, the student’s panic and confusion are understandable.

A better-formulated assignment poses significant challenges, but one of them is not wondering what the instructor secretly wants. Here’s a possible revision, which follows the guidelines suggested above:

[Course Name and Title]

[Instructor’s Name]

Due date: Thursday, February 24, at 11:10am in section

Length: 5-6pp. double-spaced

Limiting your reading to the sourcebook, write a comparative analysis of Hobbes’s, Locke’s, and Marx’s conceptions of property.

The purpose of this assignment is to help you synthesize some difficult political theory and identify the profound differences among some key theorists.

The best papers will focus on a single shared aspect of the theorists’ respective political ideologies, such as how property is distributed, whether it should be owned, or what role it serves politically. The best papers will not only focus on a specific topic, but will state a clear and arguable thesis about it (“the three authors have differing conceptions of property” is neither) and go on to describe and assess the authors’ viewpoints clearly and concisely.

Note that this revised assignment is now not only clearer than the original; it also requires less regurgitation and more sustained thought.

For more information about crafting and staging your assignments, see “ The Papers We Want to Read ” by Linda Simon, Social Studies; Jan/Feb90, Vol. 81 Issue 1, p37, 3p. (The link to Simon’s article will only work if your computer is on the Yale campus.) See also the discussion of Revising Assignments in the section of this website on Addressing Plagiarism .

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Home > Books > Knowledge Management Strategies and Applications

Knowledge‐Based Assignment Model for Allocation of Employees in Engineering‐to‐Order Production

Submitted: 04 May 2016 Reviewed: 08 June 2017 Published: 21 November 2017

DOI: 10.5772/intechopen.70073

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In today’s rapidly changing business environment, it is necessary to react promptly in response to the product changes that happen constantly in an Engineering‐to‐Order production environment. Very often, there is not sufficient time to educate employees regarding new and necessary knowledge. If we insist on the standardization of a process execution, the process always requires appropriate knowledge from among available employees. In this chapter, an option for adjusting processes to available knowledge is studied. Following calculations, it was concluded that a partial corruption of a perfect process leads to a better knowledge alignment of employees. At first, with the corruption of a perfect process, its efficiency is decreased, but with better knowledge alignment, process efficiency is consequently increased to a level better than the original one. The optimization model presented in this chapter is based on a modified classic assignment problem and it includes a numerical example based on the data of ETO company. We proved our findings from the aspects of balance, employee capacity load and process efficiency.

knowledge allocation
optimization model
activity‐cutting principle

Author Information

Matjaz roblek *.

Faculty of Organizational Sciences, University of Maribor, Kranj, Slovenia

Benjamin Urh

*Address all correspondence to: [email protected]

1. Introduction

Global competitiveness requires constant innovations of products and processes, which inherently require changes on the part of production companies. Management of these changes is especially important for those companies for which the production of new products is a regular business, that is, for which every customer requirement is so unique that it requires for the integration of research and development (R&D) department employees to a certain level. Linking of sales, R&D and production in such way is called an ‘Engineering‐to‐Order production strategy’ (ETO). Products in ETO production have a complex structure and a customer‐specified production that is treated as a project. These projects are generally unique and were never previously executed. Therefore, it is impossible that they be handled with existing standard project activities. Problems with the allocation of employees appear in the first activities of the ETO production project, in which activities require a high level of innovation, and the project requires a proper knowledge allocation prior to capacity allocation. Of course, the management needs both allocation views, but the knowledge aspect is more important when dealing with new product or technology changes. The typical question before executing each ETO project is: Do we have appropriate knowledge to do that?

Knowledge is an element of the employees and also an element of the activities of business processes [ 1 ]. In Make‐to‐Stock (MTS), production activities are highly specialized and require a small set of required knowledge. In ETO production, employees execute many activities with a large set of required knowledge. Due to salary requirements, the human‐resource‐required knowledge is linked to the work position definitions [ 2 ]. The management goal is to optimize the required knowledge of work positions and the current knowledge of employees. With every product or process change, the knowledge structure of the work position is changed. If changes are permanent, there will be a continuous searching for new appropriate employees. However, what if the process of change was adjusted so that it took into consideration currently available knowledge? These employees are the only source that is available at the time a new product requires new knowledge in the process. What if the capacity load of each employee’s knowledge and not just the employee’s capacity in general were taken into consideration?

2. Literature review

In literature, this kind of optimization problem is classified as the worker assignment problem [ 3 ]. Applications of this problem are matching employees on work positions, where the required knowledge of work positions is compared to the actual knowledge of known employees [ 4 ]. The optimal solution (objective function) depends on the global minimum of the current knowledge deficit or the global maximum of the current knowledge surplus.

In a real environment, production processes are complicated and diverse. Almost every product and its production technology require modification of its objective function or modification of the entire optimization problem. Even if there is production of the same product in different locations, there will be modification needs, despite work standardization efforts. During process execution (over several years), the optimization problem also changes because of expected and unexpected events, such as production errors, economic opportunities and new arrangements. These events are sometimes very important for optimization. In the case of the presence of a more important and/or urgent business event, their importance for optimization disappears, and their priorities for optimization are changed. Therefore, there are many specific solutions for the worker assignment problem in the literature. Some solutions are case specific while other are made in an attempt to be universally applicable. Depending on the complexity of the worker assignment problem, researchers implement different optimization methods: mathematic programming models (linear, non‐linear, integer), genetic algorithms and heuristics.

The following research has been used as a background for the worker assignment problem in this chapter. From the perspective of tasks, Azizi and Liang [ 5 ] developed an integrated approach to the worker assignment problem. Their dominant assignment problem includes workforce flexibility acquisition and task rotation. They used a constructive‐search heuristic method and set the objective to minimizing the total cost including the incremental cost of new training cost, flexibility cost and productivity loss cost. The learning effect in the worker assignment model was also the subject of research in a project task scheduling problem [ 6 ]. They used a mixed non‐linear integer program, solved by a proposed genetic algorithm. The objective function was to minimize outsourcing costs. From the task perspective, there is optimization model of task allocation and knowledge worker scheduling [ 7 ]. The purpose of this model is to assign knowledge workers to every task and arrange them (the tasks) in order to minimize the total time required to finish all projects. Their optimization is based on the Ant Colony algorithm as an optimization technique [ 8 ]. Nembhard [ 9 ] uses a heuristic approach for assigning workers to tasks that is based on individual learning rates.

There are also worker assignment models originating in production layout and shifts. McDonald et al. [ 10 ] developed a worker assignment model to evaluate a lean manufacturing cell, using a binary integer programming model that is solved using a branch‐and‐bound approach. The objective of this model is to minimize net present costs (initial training costs, incremental training costs, inventory costs and cost of poor quality). Previously, a model of worker assignment considering technical and human skills in cellular manufacturing was developed [ 11 ]. It is classified as mixed‐integer programming problem. The objective of the model is to maximize profit, where profit has three components: productivity, quality costs and training costs. Ingolfsson et al. [ 12 ] combined integer programming and the randomization method to schedule employees by using an integer programming heuristic to generate schedules; they used the randomization method to compute service levels. They described a method to find low cost shift schedules with a time‐varying service level that is always above a specified minimum.

There are worker‐assigning models that deal with the satisfaction of workers. Brusco and Johns [ 13 ] defined a model of staffing a multi‐skilled workforce with varying levels of productivity. They applied integer linear programming model with the objective of minimizing workforce staffing costs subject to the satisfaction of minimum labour requirements across the planning horizon of a single work shift. Mohan [ 14 ] created a model of scheduling part‐time personnel with availability restrictions and preferences to maximize employee satisfaction. He proposed an integer programming model to maximize employee satisfaction (while considering their seniority and availability) and to meet the demand requirements for each shift. A branch‐and‐bound algorithm was used for this.

From the perspective of competencies [ 15 ], there is a competence‐driven staff assignment approach that is based on a stochastic working status model. This model seeks to minimize employee wages and maximize strategic gains of the company from the increment of desirable competencies. The authors used a genetic algorithm as the optimization method. Competencies are also used in a model that seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub‐problem, the scheduling and staff assignment for a candidate set of selected projects is also optimized [ 16 ]. The authors used non‐linear mixed‐integer program formulation for the overall problem and then proposed heuristic solution techniques composed of a greedy heuristic for the scheduling and staff assignment, and alternative ‘meta’ heuristics for the project selection.

Recent studies are showing that the worker assignment problem is still important subject of research. Grosse et al. [ 17 ] designed a framework for integrating human factors into planning models. Crawford et al. [ 18 ] showed application of worker assignment problem in project scheduling and they innovated optimization approach using hyper‐cube framework. A similar problem that discuses assignment of health care staff to tasks using fuzzy evaluation method was presented by Mutingi et al. [ 19 ]. Olivella et al. [ 20 ] gave emphasis on the cross‐training goals, while Senjuti et al. [ 21 ] optimized the assignment of tasks to workers by proposing efficient adaptive algorithms. Current efforts are dealing with additional variables in creating the perfect optimization framework (knowledge, cross‐training, etc.), or in finding the best optimization algorithms for solving worker assignment problem. They still assume that tasks are allocated to workers as ‘they are'. Our effort was to study the effect of task redefinition in the meaning of splitting tasks on smaller parts with the goal of better knowledge alignment. From the organizational view, especially when the creative job must be done (like in ETO companies), the list of required tasks is created according to the available knowledge of workers, and the new definition of tasks is a subject of optimization output. This was our main theoretical issue that is described as real business example as follows:

At first, there is an optimal worker assignment on the work position requirements of ETO company.

Then, one or many workers leave the company at their own initiative. Because of the high level of customer demand, there is no time to re‐educate the existing employees, and management will not approve recruiting new employees.

The quality of process output (product) must remain at the same quality level. It is assumed that the quality can be reached only with proper knowledge.

The quantity of process output may be reduced.

This is a typical example of a company that needs to increase the use of its internal sources. Many cases have been found in practice in ETO companies in which the management solved the problem of outgoing knowledge with reorganization of internal employees rather than with the simple extension of employees’ existing capacities, for example, overtime work [ 22 ]. We also set two assumptions that were not subjects of this research: first, we accepted that in ETO production, business processes are constantly changing and, therefore, knowledge requirements are also changing. Second, because these are simulations, the relation between knowledge and the process efficiency was accepted: if employees have proper knowledge for the execution of activities, then these activities are performed faster. This has an impact on better efficiency of the whole process if that activity is simultaneously a process bottleneck [ 23 ].

The key solution of adjusting processes to the current knowledge lies in the theory of business process management [ 24 ], in which the main problem of achieving a short process throughput time lies in the waiting times among different work positions that are the consequence of unbalanced work. This problem is insignificant if the entire process is executed by only one employee who occupies one work position, because there are no work position breaks [ 25 ]. This works only in small companies. Large business systems are complicated: they have many business processes with diverse knowledge requirements (e.g. ETO production) and require many employees with different types and levels of knowledge. Work is divided into activities between different work positions. Each work position has its own knowledge requirements. In this case, management needs control over the specific knowledge and over the number of the work position changes, and must keep them at the ‘desired’ minimum level so that the optimal process efficiency and the work balance are reached. The problem is also in the required and actual capacity of the specific knowledge. The process output quantity reflects the frequency of activity executions [ 26 ]. From a previous description of the principle of minimization work position breaks, when the capacity of one employee is exceeded, an additional employee who can perform all activities in the process is required. Such a broadly educated employee is too expensive, and this solution is thus irrational. Therefore, the process is divided into activities (tasks) among many work positions with the least expensive employees. Management creates work positions with a simple and complex knowledge structure. However, dividing work in too many work positions slows down the process: the throughput time is extended because of the additional waiting time each time the work position is switched.

Regarding the theory of work position breaks, work position knowledge structure and employee knowledge capacity, we modified our previously published model [ 22 ]. Figure 1 shows the steps of upgraded conceptual model. In the new model, we are measuring the effect of the partial corruption of a perfect process regarding better current knowledge alignment from the perspective of employee capacity load and from that of process efficiency; with corruption of the process, we are decreasing its efficiency due to new additional work position breaks, but with better knowledge alignment we are again increasing the process efficiency.

Figure 1.

Knowledge‐based assignment conceptual model.

3.1. Measuring optimal knowledge alignment

We can observe in practice that if the current knowledge deficit is below the required knowledge, the result is less efficient work. Surprisingly, even an excess of actual knowledge over the required level of knowledge has the same result of over‐educated and intelligent employees becoming bored when they are executing routine activities [ 22 ]. Therefore, we modified a classic assignment linear integer problem of Kolman and Beck [ 3 ]. In the original optimization model ( Eq. (1) ), the value c ij represents the added value if employee i is allocated to work position j and the optimization function maximizes a profit.

We replaced the added value with the minimal knowledge deficit/surplus (absolute) gap of n key required knowledge K k . That means if we allocate an employee with his/her actual knowledge that is nearest to required knowledge on the work position (neither below nor above) then we have attained optimal knowledge alignment. The idea is to minimize the overall absolute key knowledge gap in the processes of the specific company ( Eq. (2) ).

where i… n = number of compared employees; j… n = number of different work positions; k… n = number of compared key knowledge; and |K k | = absolute difference between required and actual knowledge K .

In case of a new required ETO production change, this model can be used in the following situations:

If there is an ‘open’ set of available employees, all potential candidates in the optimization function can be matched. If the candidate knowledge gap is excessive (the appropriate level was not a subject of this research) the candidate is inappropriate for the work position because the performed work will be less efficient. This action has certain inherent costs (hiring, firing).

If there is time to provide additional education to employees, then the knowledge deficit can be decreased with additional knowledge. This action has additional education and training costs.

Existing employees can also be re‐assigned on existing work positions so that the company knowledge alignment is optimal.

Are these all the possible management actions?

3.2. Measuring the corruption of a perfect process

As an innovation, the effect of a partial corruption of a perfect process was tested, including its impact on a better knowledge alignment with the limitation that the set of employees must remain untouched. The hypothesis was that with a corruption of the process, a better knowledge alignment can be achieved and, consequently, the process efficiency can be increased, despite a simultaneous decrease of its efficiency due to new additional work position breaks. Moreover, there must be a point in the process corruption procedure after which the inefficiency of the process exceeds the benefits of better knowledge alignment.

The effect of work position breaks in the process is measured by structural index K wpb ( Eq. (3) ) [ 27 ]. This is a common key performance indicator in the theory of analysing business processes.

C wp counts all work position breaks in a specific process. P a counts all activities in that process. In this theory, the process slightly stops each time the next process activity is performed by different employee (on a different work position). This is one of practical causes for additional waiting time in the structure of throughput time of the process. There can be up to n − 1 work position breaks in a process of n sequential activities. According to the total number of all process activities, a small number of work position breaks means that the process is more efficient.

In practice, poor work quality can be found in the process due to inappropriate knowledge alignment. This generates additional feedback loops, activities are repeated and the result is additional work position breaks. Determining the causes of additional activity breaks is not a subject of this research.

3.3. Linking knowledge optimization and work position breaks

From the perspective of real business in ETO production, especially in this time of global economic crisis, accessibility to newly required knowledge is greatly limited due to extra educational costs. Downsizing also means that processes must be executed with fewer employees but at the same time the level of product quality must remain equal to previous process executions. Management typically reacts with reorganization of employees on activities. Furthermore, because we cannot split ‘the human body', his or her structure of knowledge and the time capacity of that knowledge cannot be optimal for current (ideal) process. In the theory, the problem can be easily solved if we have all current employees with all required knowledge of the process.

In ETO production, there are many specialists (e.g. electrical engineers, mechanical engineers, software engineers) with one or two dominant fields of knowledge of very high quality or strength, and few employees with wide spectra of high quality knowledge (senior engineers, mechatronics), because the latter are too expensive. However, they are also key employees for the ETO production; they have the big picture over each new product, and they can control the efficiency and quality of the overall production process. They are never ‘bottlenecks’ in the process with regard to knowledge, but they can be problematic with regard to the available time capacity of his/her specific required knowledge, because they are involved in many processes (ETO projects).

This phenomenon is also a result of the accumulation of many small organizational changes in processes over time. When the company was established (or after process re‐engineering project), processes and work positions were optimally designed for execution, employees were carefully selected and their knowledge was appropriate for knowledge requirements of work positions ( Figure 2 ).

Figure 2.

Explanation of cutting activities when employee leaves the process.

Over time, new activities were slowly added to work positions, thus generating newly required knowledge. These changes were so small at the beginning that the management did not recognize them as knowledge problems or capacity problems. They had no effect on the employees except that the work position received one or two new key pieces of knowledge that employees had to obtain. After a few years of small changes, the work position and their key knowledge structure had expanded in such a way that the management and the employee did not know which pieces of knowledge of the work position were key for business success (e.g. a designer in ETO production is working 30% of his capacity on designing, 40% of the time he is occupied with routine paper work and another 30% he is attending meetings; if we require 100% design work, then this person’s design knowledge is a capacity bottleneck).

For such cases, we created a process and knowledge algorithm that is connected with a Key performance indicators (KPI) that measures process corruption as follows:

We must have input data of current processes (As‐Is), their activities and times, current work positions, required knowledge, current employees and their actual knowledge.

Then, we test the impact of employee reduction on the knowledge structure of process. We can start with required knowledge that is recognized as a process bottleneck or with knowledge that is missing at the new activity executor.

In first case, we reduce the process activity until only work with knowledge that was bottlenecked remains (i.e. knowledge that is available by only one employee). The removed parts of activity with removed knowledge are distributed among other employees in the process until the optimal knowledge alignment is reached ( Eq. (2) ). If some knowledge is insufficient with one employee, the part of activity requiring this knowledge is given to an employee who can cover it successfully. Then, we repeat this procedure until optimal process knowledge alignment is reached.

At the same time, we measure the impact of the activity‐cutting principle on the process ( Eq. (3) ). Because the better knowledge alignment improves the process efficiency, and the activity‐cutting principle reduces the process efficiency, the algorithm serves as a ‘trading’ point when we are balancing and allocating employee knowledge on activities within his/her available time capacity ( Figure 3 )

The final result (output) is a new process (To‐Be) that is feasible.

Figure 3.

Possible outputs of algorithm for optimal knowledge alignment in ETO production.

Such a reorganized process is reengineered on the basis of knowledge.

4. Input data

4.1. processes, process activities, work positions and required knowledge.

In ETO production, at first sight, almost every product has its own and unique production process (routing). The fact is that activities (operations) among different processes are almost the same with regard to required knowledge. They differ mostly in the time required for execution. Because each product has its unique structure (bill of material), the process is named in practice as a project and its operations are named as activities. However, from the top‐down approach, each project in ETO production has almost the same set and the same sequence of project phases (with many sub‐activities), for example, (1) preparation, (2) design, (3) construction, and (4) testing. Therefore, it can be assumed that we have a standard form of the process (with activities) for almost all new products.

The same process activity could appear in a structure of many different processes and it is usually performed by the same work position (e.g. the same quality control activity with the same control parameters and tools for the whole product group). Moreover, one work position executes many activities. Until the system is well organized, a work position aggregates activities with approximately the same required set of knowledge. We defined that the required knowledge of a specific work position is represented as a set of knowledge from all executed activities. The sets of required knowledge of specific activity and their strength (Likert scale from 1 to 5; 5 meaning very important) are defined by the company’s internal and external experts. If a specific piece of knowledge is required for the execution of many activities, the model uses its maximal value as a required strength.

Complex work positions have a wide range of required knowledge, many of unimportant strength. Reducing the amount of various required knowledge can simplify the calculations. Simplification was achieved with the definition of key knowledge K k for each work position. If the strength of specific knowledge is above a specific level, it is treated as key knowledge of that work position.

In practice, the above‐described idea of capturing process activities and their required knowledge can be used for documenting As‐Is processes and, more importantly, for predicting future products, To‐Be processes and their expected required knowledge. This is of great importance for planning required knowledge of future ETO production. We can analyse the following:

Which activity among all activities of specific process is the most important from the key knowledge aspect, for example, to find the activity that is the ‘knowledge bottleneck’ in a process. Then we can combine this information with activity throughput rate and find an activity that is the real‐time capacity bottleneck in the process.

Which process (from among all of them) is the most important from the aspect of key knowledge, for example, for ranking all processes on the basis of the knowledge required (i.e. which process is currently the most important/crucial for the company from the knowledge view; this is important information for any ETO company in addition to the information regarding which process is crucial from capacity aspect).

In ETO production, each work position typically executes many different activities in many different processes. Therefore, we are interested which work position has the highest required strength of all key knowledge, for example, we can use this information as a basis for creating salary grades.

Which work positions in the company are exceptional from the knowledge aspect; a work position that has only one key type of knowledge but with a high required strength (e.g. CNC programmer) and which work positions are universal, that is, have many key types of required knowledge (e.g. ETO project manager).

Which type of knowledge is dominant (repeats at every executed activity) for the specific process (short‐term view) and for the whole company (long‐term view).

If we have proper data on all the above mentioned entities (processes, activities, work positions, knowledge requirements with required strength) for the present time, and if we have good knowledge requirements (definitions) of new products (especially required technology and activities), we can then simulate all future knowledge requirements in advance. Therefore, we can determine differences, for example, which work position must be knowledge‐reconstructed in the future; consequently, we can define projected mandatory changes in a structure of actual knowledge (employees).

4.2. Employees, actual knowledge and knowledge gap

Employees represent the basis for gathering current knowledge. There are many approaches to prove that an employee possesses specific knowledge and what the quality of it is (strength, level). In our approach, the 360° feedback method [ 28 ] was used. We used a list of all key required knowledge and assessed all employees (Likert scale from 0 to 5; 0 means knowledge not available). We gave employees the opportunity to extend this explicit knowledge with their tacit knowledge. In the context of our model, the term ‘tacit’ means the knowledge of an employee that is currently unknown to the company. Knowing about tacit knowledge is essential information when new processes have requirements for new types of knowledge. In practice, for optimization, it is also recommended that we have the knowledge data about potential candidates for employees.

The last step of input data preparation is a calculation of the key knowledge gap: each employee is compared to all work positions. We used the criterion c ij , explained in Eq. (2) . Any deviation of actual knowledge over and below the required knowledge is considered to be inappropriate and will lower process efficiency ( Table 1 ).

Table 1.

Matching required and actual knowledge.

Table 1 shows a numerical example of matching the actual knowledge from k 1 to k 10 of employee E 1 on activities from a 1 to a 7 of work position W 1 (e.g. Product Manager of ETO project). The example is based on the real data of ETO company, Iskratel. Negative values (grey cells) represent deficits of employee knowledge strength compared to the required knowledge of a work position. The top rows represent activities of the work position with a sum of negative values. We can identify activities that the employee is not suitable to execute (e.g. a 1 , a 2 , a 3). The left column represents the required knowledge with the sum of negative values. We can identify the lack of employee knowledge (e.g. k 4 , k 8).

In practice, we could integrate in our model the effect of learning and forgetting knowledge over time (decreasing knowledge strength if employee is not using that type of knowledge in processes for a long time). Because of model simplicity, this was not a subject of this research.

We demonstrated the capabilities of our model on a small section of the real process that was described in Figure 3 . This numerical example is based on the data of company Iskratel. We performed simulations of this example with the same tools as the calculations of real cases ( Tables 2 and 3 ). Definitions of processes were recorded in the repository of Aris Toolset software [ 29 ]. Definitions of actual and required knowledge were recorded with MS Share Point and MS SQL. All data were then exported to the MS Excel analytical tool and solved with the WhatsBest [ 30 ] add‐on. MS Excel was also used as reporting tool.

Table 2.

Input data of simulation scenarios.

Table 3.

Simulation results.

5.1. Input data of simulation scenarios

We prepared four simulation scenarios as follows:

Scenario 0: As‐Is situation. In the current state, there are three employees assigned to their own work positions, and the processing of four activities with four different types of knowledge.

Scenario 1: employee on work position w 2 left the company. His/her activity a 2 is assigned to w 1 and a 3 to w 3 . This is typical management decision that does not generate an additional work position switch in the sequence of activities.

Scenario 2: use of our algorithm: achieving better knowledge alignment. Employee on w 3 has no knowledge K 3 that is required for execution of activity a 3 ; therefore, we split activity on a ′ 3 and a ″ 3 .

Scenario 3: is same as scenario 2, with one additional activity cut: we are searching for better balance of capacities between w 1 and w 3 . We split activity a 2 and we add knowledge K 2 to work position w 3 .

We can observe the things as follows:

In scenario 1, the result of management action on knowledge distribution among work positions: Knowledge K 1 and K 2 are moved from w 2 to w 1 . Knowledge K 3 and K 4 are moved from w 2 to w 3 . In case this is the same knowledge, we used the maximal strength as the required strength.

In scenario 2, the result of optimization algorithm: according to As‐Is situation, we moved from w 2 to w 1 knowledge K 1 and K 3 . This caused the rise of the strength of both types of knowledge for w 1 . We moved from w 2 to w 3 only knowledge K 4 , because the newly required strength is below the current required strength so it remains as it was for w 3 .

In scenario 3, the new activity cut did not cause any change in knowledge requirements (and strength) of w 1 and w 3 according to scenario 2.

5.2. Simulation results

We can see in Scenario 2 (implementing activity‐cutting principle) that we decreased the knowledge gap in Scenario 1. Now, we must ‘merge’ the results of optimal knowledge alignment to determine the impact of using the activity‐cutting principle on classic production optimization parameters (Scenario 3). Otherwise, we will break some lean manufacturing principles, for example, work balancing or eliminating waiting times. We added additional input data of As‐Is process in Table 4 .

The first assumption (i) in our evaluation is the amount of time that is added to process throughput time each time we change the work position (sending work from me to you etc.). In a real case, this could be measured exactly but in our demonstration we assumed a fixed value of 3 min.

The second assumption (ii) in our evaluation is the amount of time that is added to process throughput time because of non‐optimal knowledge alignment. In the As‐Is process, we know that we have 0.8 by the Likert non‐optimal knowledge alignment. If the times in this table were measured without being aware of this knowledge gap then the real throughput time is longer. In a real case, we could measure this by comparing the knowledge gap and the difference between planned and real production times (we have to exclude other causes for time extension first). In our demonstration, we assumed that every 0.1 of knowledge gap adds 1% to planned process throughput time.

6. Discussion

The main specialty of our model is that we permit changes of the process because the actual knowledge is not appropriate for it. However, we do not allow changes in the sequence of activities; we allow only changes in the sequence of using employees. The results are new partial activities in the process; consequently, the process workflow is jumping forwards and backwards between employees.

In our model, we removed all unnecessary knowledge from the work positions that were process ‘bottlenecks’ and replaced it with the new process structure; this was done by taking into consideration the availability of the actual knowledge of employees. The entire individual employee time capacity is now focused only on the utilization of knowledge that is bottlenecked. Other required knowledge in the process that is also present in other employees is removed from that work position. Employee capacity is now free of all non‐bottleneck knowledge, and this raises its capacity availability.

In our simulations, we used process time indicators to verify our assumption, even if we know, on the basis of real projects [ 31 , 32 ], that the best improvements in the ETO production are achieved on the process quality indicators. Time indicators are improved indirectly as a result of better product quality: fewer aftermarket repairs means less additional invested time in the total production time of the specific product. The starting point of all scenarios is the departure of one employee from the original process (Scenario 0). In Scenario 1, we reacted by implementing the lean manufacturing principle of capacity balancing: the work of the lost employee is divided among remaining employees on the basis of capacity levelling without additional work position breaks. This is a common management decision, and it is expressed as a load capacity per shift (%) indicator in Table 4 . This decision produced the knowledge gap of 1.7 ( Table 3 ).

In Scenario 2, we used our model with the activity‐cutting principle, and we reduced the knowledge gap by 0.4 or 23.5% ( Table 3 ). Most time indicators were also improved ( Table 5 ), except for the unbalanced load capacity per shift (%) indicator, and a lower process throughput rate (from 9 to 8 products per shift). Both indicators would have negative impact in mass or serial production, but according to the requirements of the ETO production it is more important that we achieved the desired quality of knowledge for production process because there are no repetitions (rather only unique, one‐time process executions). Management can balance these indicators and make the decision that is adopted for a specific process ‘case'.

Table 4.

Production parameters of As‐Is process.

Table 5.

The impact of activity‐cutting principle on production parameters in scenarios from 1 to 3.

In Scenario 3, we tested the total ignorance of the Lean Manufacturing principles, and we performed additional activity cuts for searching for even better knowledge alignment. We did not achieve a lower knowledge gap ( Table 3 ); we also worsened all time indicators according to Scenario 2 ( Table 5 ). This indicated that there is a point in the repetition of activity‐cutting procedure after which the process becomes so inefficient that is better to hire a new employee if the knowledge gap is still too high for achieving the appropriate quality of ETO products. Where that point is, what the gap should be and whether its value is of universal use or case sensitive are all subjects of future research.

7. Conclusions

In Make‐to‐Stock, Assemble‐to‐Order and Make‐to‐Order production, assignment models for the allocation of employees assume that tasks of production processes (or routings) are of a fixed structure. Managers believe they found the most ‘efficient’ process of producing products and, therefore, all current optimization models are searching for appropriate employees for that process. Small deviations between the required and the actual knowledge are resolved with alternative routing; its structure is also known and fixed in advance. All of this is possible because extra time is invested for testing and preparing optimal processes for many repetitions. Extra time is also invested for finding employees with proper knowledge for that processes. This is the case of known theoretical and practical solutions of worker assignment problem.

However, in ETO production, and consequently in all knowledge‐intensive processes or case‐like processes, we determined that processes are structured around the available knowledge of employees. Otherwise, the cost of searching for missing knowledge in the form of a new employee could exceed all the added value to the business. Process ‘cases’ are never the same and each process ‘repetition’ requires a process structure that is adapted to the actual knowledge and its capacity in the company; the bottleneck is not the capacity of the employee but the capacity of his/her specific actual knowledge. With the activity‐cutting principle in our assignment model, we proved that we can release the ‘hidden’ time capacity of employee who is the bottleneck so that we could remove all activities and consequently the knowledge that is also available with other employees from the work position. We recommend that this principle can be an option of all assignment models for the allocation of employees for ETO production and all other knowledge‐intense companies. This is our main contribution to the theory of modelling worker assignment problem.

Of course, this research raises additional questions for our future work, especially in the field of practical application: is knowledge the right category in our assignment model or is it better to use all measureable work habits and personal skills [ 33 ]? There are also assumptions in Table 4 that will need additional research and explanation. Nevertheless, our concept of redefining tasks with the goal of reaching optimal worker knowledge alignment could be used as a ‘smart’ reorganization principle for dynamic and real‐time redefinition of processes in companies, where the standardization of tasks is not the main factor of reaching efficiency.

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© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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4.3: Types of Assignments

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Page ID 133160

Rhian Morgan; Lisa Moody; Ana Stevenson; Colleen Kaesehagen; Lyle Cleeland; Gemma Lynch; Brenda Carter; Tony Hewitt; Meghan Boland; and Camilla Robson

Ana Stevenson
James Cook University via James Cook University

Introduction

As discussed in the previous chapter, assignments are a common method of assessment at university. You may encounter many assignments over your years of study, yet some will look quite different from others. By recognising different types of assignments and understanding the purpose of the task, you can direct your writing skills effectively to meet task requirements. This chapter draws on the skills from the previous chapter, and extends the discussion, showing you where to aim with different types of assignments.

The chapter begins by exploring the popular essay assignment, with its two common categories, analytical and argumentative essays. It then examines assignments requiring case study responses , as often encountered in fields such as health or business. This is followed by a discussion of assignments seeking a report (such as a scientific report) and reflective writing assignments, which are common in nursing, education, and human services. The chapter concludes with an examination of annotated bibliographies and literature reviews. The chapter also has a selection of templates and examples throughout to enhance your understanding and improve the efficacy of your assignment writing skills.

Different Types of Written Assignments

At university, an essay is a common form of assessment. In the previous chapter Writing Assignments, we discussed what was meant by showing academic writing in your assignments. It is important that you consider these aspects of structure, tone, and language when writing an essay.

Components of an essay

Essays should use formal but reader-friendly language and have a clear and logical structure. They must include research from credible academic sources such as peer reviewed journal articles and textbooks. This research should be referenced throughout your essay to support your ideas (see the chapter Working with Information).

Diagram that allocates words of assignment

If you have never written an essay before, you may feel unsure about how to start. Breaking your essay into sections and allocating words accordingly will make this process more manageable and will make planning the overall essay structure much easier.

An essay requires an introduction, body paragraphs, and a conclusion.
Generally, an introduction and conclusion are each approximately 10% of the total word count.
The remaining words can then be divided into sections and a paragraph allowed for each area of content you need to cover.
Use your task and criteria sheet to decide what content needs to be in your plan

An effective essay introduction needs to inform your reader by doing four basic things:

An effective essay body paragraph needs to:

An effective essay conclusion needs to:

Common types of essays

You may be required to write different types of essays, depending on your study area and topic. Two of the most commonly used essays are analytical and argumentative . The task analysis process discussed in the previous chapter Writing Assignments will help you determine the type of essay required. For example, if your assignment question uses task words such as analyse, examine, discuss, determine, or explore, then you would be writing an analytical essay . If your assignment question has task words such as argue, evaluate, justify, or assess, then you would be writing an argumentative essay . Regardless of the type of essay, your ability to analyse and think critically is important and common across genres.

Analytical essays

These essays usually provide some background description of the relevant theory, situation, problem, case, image, etcetera that is your topic. Being analytical requires you to look carefully at various components or sections of your topic in a methodical and logical way to create understanding.

The purpose of the analytical essay is to demonstrate your ability to examine the topic thoroughly. This requires you to go deeper than description by considering different sides of the situation, comparing and contrasting a variety of theories and the positives and negatives of the topic. Although your position on the topic may be clear in an analytical essay, it is not necessarily a requirement that you explicitly identify this with a thesis statement. In an argumentative essay, however, it is necessary that you explicitly identify your position on the topic with a thesis statement. If you are unsure whether you are required to take a position, and provide a thesis statement, it is best to check with your tutor.

Argumentative essays

These essays require you to take a position on the assignment topic. This is expressed through your thesis statement in your introduction. You must then present and develop your arguments throughout the body of your assignment using logically structured paragraphs. Each of these paragraphs needs a topic sentence that relates to the thesis statement. In an argumentative essay, you must reach a conclusion based on the evidence you have presented.

Case study responses

Case studies are a common form of assignment in many study areas and students can underperform in this genre for a number of key reasons.

Students typically lose marks for not:

Relating their answer sufficiently to the case details.
Applying critical thinking.
Writing with clear structure.
Using appropriate or sufficient sources.
Using accurate referencing.

When structuring your response to a case study, remember to refer to the case. Structure your paragraphs similarly to an essay paragraph structure, but include examples and data from the case as additional evidence to support your points (see Figure 68). The colours in the sample paragraph below show the function of each component.

The Nursing and Midwifery Board of Australia (NMBA) Code of Conduct and Nursing Standards (2018) play a crucial role in determining the scope of practice for nurses and midwives. A key component discussed in the code is the provision of person-centred care and the formation of therapeutic relationships between nurses and patients (NMBA, 2018). This ensures patient safety and promotes health and wellbeing (NMBA, 2018). The standards also discuss the importance of partnership and shared decision-making in the delivery of care (NMBA, 2018, 4). Boyd and Dare (2014) argue that good communication skills are vital for building therapeutic relationships and trust between patients and care givers. This will help ensure the patient is treated with dignity and respect and improve their overall hospital experience. In the case, the therapeutic relationship with the client has been compromised in several ways. Firstly, the nurse did not conform adequately to the guidelines for seeking informed consent before performing the examination as outlined in principle 2.3 (NMBA, 2018). Although she explained the procedure, she failed to give the patient appropriate choices regarding her health care.

Topic sentence | Explanations using paraphrased evidence including in-text references | Critical thinking (asks the so what? question to demonstrate your student voice). | Relating the theory back to the specifics of the case. The case becomes a source of examples as extra evidence to support the points you are making.

Reports are a common form of assessment at university and are also used widely in many professions. It is a common form of writing in business, government, scientific, and technical occupations.

Reports can take many different structures. A report is normally written to present information in a structured manner, which may include explaining laboratory experiments, technical information, or a business case. Reports may be written for different audiences, including clients, your manager, technical staff, or senior leadership within an organisation. The structure of reports can vary, and it is important to consider what format is required. The choice of structure will depend upon professional requirements and the ultimate aims of the report. Consider some of the options in the table below (see Table 18.2).

Reflective writing

Reflective writing is a popular method of assessment at university. It is used to help you explore feelings, experiences, opinions, events, or new information to gain a clearer and deeper understanding of your learning.

A reflective writing task requires more than a description or summary. It requires you to analyse a situation, problem or experience, consider what you may have learnt, and evaluate how this may impact your thinking and actions in the future. This requires critical thinking, analysis, and usually the application of good quality research, to demonstrate your understanding or learning from a situation.

Diagram of bubbles that state what, now what, so what

Essentially, reflective practice is the process of looking back on past experiences and engaging with them in a thoughtful way and drawing conclusions to inform future experiences. The reflection skills you develop at university will be vital in the workplace to assist you to use feedback for growth and continuous improvement. There are numerous models of reflective writing and you should refer to your subject guidelines for your expected format. If there is no specific framework, a simple model to help frame your thinking is What? So what? Now what? (Rolfe et al., 2001).

The Gibbs’ Reflective Cycle

The Gibbs’ Cycle of reflection encourages you to consider your feelings as part of the reflective process. There are six specific steps to work through. Following this model carefully and being clear of the requirements of each stage, will help you focus your thinking and reflect more deeply. This model is popular in Health.

Gibb's reflective cycle of decription, feelings, evauation, analysis, action plan, cocnlusion

The 4 R’s of reflective thinking

This model (Ryan and Ryan, 2013) was designed specifically for university students engaged in experiential learning. Experiential learning includes any ‘real-world’ activities, including practice led activities, placements, and internships. Experiential learning, and the use of reflective practice to heighten this learning, is common in Creative Arts, Health, and Education.

Annotated bibliography

What is it.

An annotated bibliography is an alphabetical list of appropriate sources (e.g. books, journal articles, or websites) on a topic, accompanied by a brief summary, evaluation, and sometimes an explanation or reflection on their usefulness or relevance to your topic. Its purpose is to teach you to research carefully, evaluate sources and systematically organise your notes. An annotated bibliography may be one part of a larger assessment item or a stand-alone assessment item. Check your task guidelines for the number of sources you are required to annotate and the word limit for each entry.

How do I know what to include?

When choosing sources for your annotated bibliography, it is important to determine:

The topic you are investigating and if there is a specific question to answer.
The type of sources on which you need to focus.
Whether these sources are reputable and of high quality.

What do I say?

Important considerations include:

Is the work current?
Is the work relevant to your topic?
Is the author credible/reliable?
Is there any author bias?
The strength and limitations (this may include an evaluation of research methodology).

Literature reviews

Generally, a literature review requires that you review the scholarly literature and establish the main ideas that have been written about your chosen topic. A literature review does not summarise and evaluate each resource you find (this is what you would do in an annotated bibliography). You are expected to analyse and synthesise or organise common ideas from multiple texts into key themes which are relevant to your topic (see Figure 18.10). You may also be expected to identify gaps in the research.

It is easy to get confused by the terminology used for literature reviews. Some tasks may be described as a systematic literature review when actually the requirement is simpler; to review the literature on the topic but do it in a systematic way. There is a distinct difference (see Table 15.4). As a commencing undergraduate student, it is unlikely you would be expected to complete a systematic literature review as this is a complex and more advanced research task. It is important to check with your lecturer or tutor if you are unsure of the requirements.

When conducting a literature review, use a table or a spreadsheet, if you know how, to organise the information you find. Record the full reference details of the sources as this will save you time later when compiling your reference list (see Table 18.5).

Overall, this chapter has provided an introduction to the types of assignments you can expect to complete at university, as well as outlined some tips and strategies with examples and templates for completing them. First, the chapter investigated essay assignments, including analytical and argumentative essays. It then examined case study assignments, followed by a discussion of the report format. Reflective writing , popular in nursing, education, and human services, was also considered. Finally, the chapter briefly addressed annotated bibliographies and literature reviews. The chapter also has a selection of templates and examples throughout to enhance your understanding and improve the efficacy of your assignment writing skills.

Not all assignments at university are the same. Understanding the requirements of different types of assignments will assist in meeting the criteria more effectively.
There are many different types of assignments. Most will require an introduction, body paragraphs, and a conclusion.
An essay should have a clear and logical structure and use formal but reader-friendly language.
Breaking your assignment into manageable chunks makes it easier to approach.
Effective body paragraphs contain a topic sentence.
A case study structure is similar to an essay, but you must remember to provide examples from the case or scenario to demonstrate your points.
The type of report you may be required to write will depend on its purpose and audience. A report requires structured writing and uses headings.
Reflective writing is popular in many disciplines and is used to explore feelings, experiences, opinions, or events to discover what learning or understanding has occurred. Reflective writing requires more than description. You need to be analytical, consider what has been learnt, and evaluate the impact of this on future actions.
Annotated bibliographies teach you to research and evaluate sources and systematically organise your notes. They may be part of a larger assignment.
Literature reviews require you to look across the literature and analyse and synthesise the information you find into themes.

Gibbs, G. (1988). Learning by doing: A guide to teaching and learning methods. Further Education Unit, Oxford Brookes University.

Rolfe, G., Freshwater, D., Jasper, M. (2001). Critical reflection in nursing and the helping professions: A user’s guide . Palgrave Macmillan.

Ryan, M. & Ryan, M. (2013). Theorising a model for teaching and assessing reflective learning in higher education. Higher Education Research & Development , 32(2), 244-257. https://doi.org/10.1080/07294360.2012.661704

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Business Essentials

Assignment Method: Examples of How Resources Are Allocated

What Is the Assignment Method?

The assignment method is a way of allocating organizational resources in which each resource is assigned to a particular task. The resource could be monetary, personnel , or technological.

Understanding the Assignment Method

The assignment method is used to determine what resources are assigned to which department, machine, or center of operation in the production process. The goal is to assign resources in such a way to enhance production efficiency, control costs, and maximize profits.

The assignment method has various applications in maximizing resources, including:

Allocating the proper number of employees to a machine or task
Allocating a machine or a manufacturing plant and the number of jobs that a given machine or factory can produce
Assigning a number of salespersons to a given territory or territories
Assigning new computers, laptops, and other expensive high-tech devices to the areas that need them the most while lower priority departments would get the older models

Companies can make budgeting decisions using the assignment method since it can help determine the amount of capital or money needed for each area of the company. Allocating money or resources can be done by analyzing the past performance of an employee, project, or department to determine the most efficient approach.

Regardless of the resource being allocated or the task to be accomplished, the goal is to assign resources to maximize the profit produced by the task or project.

Example of Assignment Method

A bank is allocating its sales force to grow its mortgage lending business. The bank has over 50 branches in New York but only ten in Chicago. Each branch has a staff that is used to bring in new clients.

The bank's management team decides to perform an analysis using the assignment method to determine where their newly-hired salespeople should be allocated. Given the past performance results in the Chicago area, the bank has produced fewer new clients than in New York. The fewer new clients are the result of having a small market presence in Chicago.

As a result, the management decides to allocate the new hires to the New York region, where it has a greater market share to maximize new client growth and, ultimately, revenue.

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Types of Assignments

Cristy Bartlett and Kate Derrington

Introduction

The chapter begins by exploring the popular essay assignment, with its two common categories, analytical and argumentative essays. It then examines assignments requiring case study responses , as often encountered in fields such as health or business. This is followed by a discussion of assignments seeking a report (such as a scientific report) and reflective writing assignments, common in nursing, education and human services. The chapter concludes with an examination of annotated bibliographies and literature reviews. The chapter also has a selection of templates and examples throughout to enhance your understanding and improve the efficacy of your assignment writing skills.

Different Types of Written Assignments

At university, an essay is a common form of assessment. In the previous chapter Writing Assignments we discussed what was meant by showing academic writing in your assignments. It is important that you consider these aspects of structure, tone and language when writing an essay.

Components of an essay

Essays should use formal but reader friendly language and have a clear and logical structure. They must include research from credible academic sources such as peer reviewed journal articles and textbooks. This research should be referenced throughout your essay to support your ideas (See the chapter Working with Information ).

An essay requires an introduction, body paragraphs and a conclusion.
Generally, an introduction and conclusion are approximately 10% each of the total word count.
The remaining words can then be divided into sections and a paragraph allowed for each area of content you need to cover.
Use your task and criteria sheet to decide what content needs to be in your plan

An effective essay introduction needs to inform your reader by doing four basic things:

Table 20.1 An effective essay

An effective essay body paragraph needs to:

An effective essay conclusion needs to:

Common types of essays

You may be required to write different types of essays, depending on your study area and topic. Two of the most commonly used essays are analytical and argumentative . The task analysis process discussed in the previous chapter Writing Assignments will help you determine the type of essay required. For example, if your assignment question uses task words such as analyse, examine, discuss, determine or explore, you would be writing an analytical essay . If your assignment question has task words such as argue, evaluate, justify or assess, you would be writing an argumentative essay . Despite the type of essay, your ability to analyse and think critically is important and common across genres.

Analytical essays

The purpose of the analytical essay is to demonstrate your ability to examine the topic thoroughly. This requires you to go deeper than description by considering different sides of the situation, comparing and contrasting a variety of theories and the positives and negatives of the topic. Although in an analytical essay your position on the topic may be clear, it is not necessarily a requirement that you explicitly identify this with a thesis statement, as is the case with an argumentative essay. If you are unsure whether you are required to take a position, and provide a thesis statement, it is best to check with your tutor.

Argumentative essays

Case Study Responses

Case studies are a common form of assignment in many study areas and students can underperform in this genre for a number of key reasons.

Students typically lose marks for not:

Relating their answer sufficiently to the case details
Applying critical thinking
Writing with clear structure
Using appropriate or sufficient sources
Using accurate referencing

When structuring your response to a case study, remember to refer to the case. Structure your paragraphs similarly to an essay paragraph structure but include examples and data from the case as additional evidence to support your points (see Figure 20.5 ). The colours in the sample paragraph below show the function of each component.

Reports are a common form of assessment at university and are also used widely in many professions. It is a common form of writing in business, government, scientific, and technical occupations.

Reports can take many different structures. A report is normally written to present information in a structured manner, which may include explaining laboratory experiments, technical information, or a business case. Reports may be written for different audiences including clients, your manager, technical staff, or senior leadership within an organisation. The structure of reports can vary, and it is important to consider what format is required. The choice of structure will depend upon professional requirements and the ultimate aims of the report. Consider some of the options in the table below (see Table 20.2 ).

Table 20.2 Explanations of different types of reports

Reflective writing.

Reflective writing is a popular method of assessment at university. It is used to help you explore feelings, experiences, opinions, events or new information to gain a clearer and deeper understanding of your learning. A reflective writing task requires more than a description or summary. It requires you to analyse a situation, problem or experience, consider what you may have learnt and evaluate how this may impact your thinking and actions in the future. This requires critical thinking, analysis, and usually the application of good quality research, to demonstrate your understanding or learning from a situation. Essentially, reflective practice is the process of looking back on past experiences and engaging with them in a thoughtful way and drawing conclusions to inform future experiences. The reflection skills you develop at university will be vital in the workplace to assist you to use feedback for growth and continuous improvement. There are numerous models of reflective writing and you should refer to your subject guidelines for your expected format. If there is no specific framework, a simple model to help frame your thinking is What? So what? Now what? (Rolfe et al., 2001).

Table 20.3 What? So What? Now What? Explained.

The Gibbs’ Reflective Cycle

The 4 R’s of reflective thinking

This model (Ryan and Ryan, 2013) was designed specifically for university students engaged in experiential learning. Experiential learning includes any ‘real-world’ activities including practice led activities, placements and internships. Experiential learning, and the use of reflective practice to heighten this learning, is common in Creative Arts, Health and Education.

Annotated Bibliography

What is it.

An annotated bibliography is an alphabetical list of appropriate sources (books, journals or websites) on a topic, accompanied by a brief summary, evaluation and sometimes an explanation or reflection on their usefulness or relevance to your topic. Its purpose is to teach you to research carefully, evaluate sources and systematically organise your notes. An annotated bibliography may be one part of a larger assessment item or a stand-alone assessment piece. Check your task guidelines for the number of sources you are required to annotate and the word limit for each entry.

How do I know what to include?

When choosing sources for your annotated bibliography it is important to determine:

The topic you are investigating and if there is a specific question to answer
The type of sources on which you need to focus
Whether they are reputable and of high quality

What do I say?

Important considerations include:

Is the work current?
Is the work relevant to your topic?
Is the author credible/reliable?
Is there any author bias?
The strength and limitations (this may include an evaluation of research methodology).

Literature Reviews

It is easy to get confused by the terminology used for literature reviews. Some tasks may be described as a systematic literature review when actually the requirement is simpler; to review the literature on the topic but do it in a systematic way. There is a distinct difference (see Table 20.4 ). As a commencing undergraduate student, it is unlikely you would be expected to complete a systematic literature review as this is a complex and more advanced research task. It is important to check with your lecturer or tutor if you are unsure of the requirements.

Table 20.4 Comparison of Literature Reviews

Generally, you are required to establish the main ideas that have been written on your chosen topic. You may also be expected to identify gaps in the research. A literature review does not summarise and evaluate each resource you find (this is what you would do in an annotated bibliography). You are expected to analyse and synthesise or organise common ideas from multiple texts into key themes which are relevant to your topic (see Figure 20.10 ). Use a table or a spreadsheet, if you know how, to organise the information you find. Record the full reference details of the sources as this will save you time later when compiling your reference list (see Table 20.5 ).

Overall, this chapter has provided an introduction to the types of assignments you can expect to complete at university, as well as outlined some tips and strategies with examples and templates for completing them. First, the chapter investigated essay assignments, including analytical and argumentative essays. It then examined case study assignments, followed by a discussion of the report format. Reflective writing , popular in nursing, education and human services, was also considered. Finally, the chapter briefly addressed annotated bibliographies and literature reviews. The chapter also has a selection of templates and examples throughout to enhance your understanding and improve the efficacy of your assignment writing skills.

Not all assignments at university are the same. Understanding the requirements of different types of assignments will assist in meeting the criteria more effectively.
There are many different types of assignments. Most will require an introduction, body paragraphs and a conclusion.
An essay should have a clear and logical structure and use formal but reader friendly language.
Breaking your assignment into manageable chunks makes it easier to approach.
Effective body paragraphs contain a topic sentence.
A case study structure is similar to an essay, but you must remember to provide examples from the case or scenario to demonstrate your points.
The type of report you may be required to write will depend on its purpose and audience. A report requires structured writing and uses headings.
Reflective writing is popular in many disciplines and is used to explore feelings, experiences, opinions or events to discover what learning or understanding has occurred. Reflective writing requires more than description. You need to be analytical, consider what has been learnt and evaluate the impact of this on future actions.
Annotated bibliographies teach you to research and evaluate sources and systematically organise your notes. They may be part of a larger assignment.
Literature reviews require you to look across the literature and analyse and synthesise the information you find into themes.

Gibbs, G. (1988). Learning by doing: A guide to teaching and learning methods. Further Education Unit, Oxford Brookes University, Oxford.

Rolfe, G., Freshwater, D., Jasper, M. (2001). Critical reflection in nursing and the helping professions: a user’s guide . Basingstoke: Palgrave Macmillan.

Ryan, M. & Ryan, M. (2013). Theorising a model for teaching and assessing reflective learning in higher education. Higher Education Research & Development , 32(2), 244-257. doi: 10.1080/07294360.2012.661704

Academic Success Copyright © 2021 by Cristy Bartlett and Kate Derrington is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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AI Teaching Strategies: Transparent Assignment Design

The rise of generative artificial intelligence (AI) tools like ChatGPT, Google Bard, and Jasper Chat raises many questions about the ways we teach and the ways students learn. While some of these questions concern how we can use AI to accomplish learning goals and whether or not that is advisable, others relate to how we can facilitate critical analysis of AI itself.

The wide variety of questions about AI and the rapidly changing landscape of available tools can make it hard for educators to know where to start when designing an assignment. When confronted with new technologies—and the new teaching challenges they present—we can often turn to existing evidence-based practices for the guidance we seek.

This guide will apply the Transparency in Learning and Teaching (TILT) framework to "un-complicate" planning an assignment that uses AI, providing guiding questions for you to consider along the way.

The result should be an assignment that supports you and your students to approach the use of AI in a more thoughtful, productive, and ethical manner.

Plan your assignment.

The TILT framework offers a straightforward approach to assignment design that has been shown to improve academic confidence and success, sense of belonging, and metacognitive awareness by making the learning process clear to students (Winkelmes et al., 2016). The TILT process centers around deciding—and then communicating—three key components of your assignment: 1) purpose, 2) tasks, and 3) criteria for success.

Step 1: Define your purpose.

To make effective use of any new technology, it is important to reflect on our reasons for incorporating it into our courses. In the first step of TILT, we think about what we want students to gain from an assignment and how we will communicate that purpose to students.

The SAMR model , a useful tool for thinking about educational technology use in our courses, lays out four tiers of technology integration. The tiers, roughly in order of their sophistication and transformative power, are S ubstitution, A ugmentation, M odification, and R edefinition. Each tier may suggest different approaches to consider when integrating AI into teaching and learning activities.

For full text of this image, see transcript linked in caption.

Questions to consider:

Do you intend to use AI as a substitution, augmentation, modification, or redefinition of an existing teaching practice or educational technology?
What are your learning goals and expected learning outcomes?
Do you want students to understand the limitations of AI or to experience its applications in the field?
Do you want students to reflect on the ethical implications of AI use?

Bloom’s Taxonomy is another useful tool for defining your assignment’s purpose and your learning goals and outcomes.

This downloadable Bloom’s Taxonomy Revisited resource , created by Oregon State University, highlights the differences between AI capabilities and distinctive human skills at each Bloom's level, indicating the types of assignments you should review or change in light of AI. Bloom's Taxonomy Revisited is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Access a transcript of the graphic .

Step 2: Define the tasks involved.

In the next step of TILT, you list the steps students will take when completing the assignment. In what order should they do specific tasks, what do they need to be aware of to perform each task well, and what mistakes should they avoid? Outlining each step is especially important if you’re asking students to use generative AI in a limited manner. For example, if you want them to begin with generative AI but then revise, refine, or expand upon its output, make clear which steps should involve their own thinking and work as opposed to AI’s thinking and work.

Are you designing this assignment as a single, one-time task or as a longitudinal task that builds over time or across curricular and co-curricular contexts? For longitudinal tasks consider the experiential learning cycle (Kolb, 1984) . In Kolb’s cycle, learners have a concrete experience followed by reflective observation, abstract conceptualization, and active experimentation. For example, students could record their generative AI prompts, the results, a reflection on the results, and the next prompt they used to get improved output. In subsequent tasks students could expand upon or revise the AI output into a final product. Requiring students to provide a record of their reflections, prompts, and results can create an “AI audit trail,” making the task and learning more transparent.
What resources and tools are permitted or required for students to complete the tasks involved with the assignment? Make clear which steps should involve their own thinking (versus AI-generated output, for example), required course materials, and if references are required. Include any ancillary resources students will need to accomplish tasks, such as guidelines on how to cite AI , in APA 7.0 for example.
How will you offer students flexibility and choice? As of this time, most generative AI tools have not been approved for use by Ohio State, meaning they have not been vetted for security, privacy, or accessibility issues . It is known that many platforms are not compatible with screen readers, and there are outstanding questions as to what these tools do with user data. Students may have understandable apprehensions about using these tools or encounter barriers to doing so successfully. So while there may be value in giving students first-hand experience with using AI, it’s important to give them the choice to opt out. As you outline your assignment tasks, plan how to provide alternative options to complete them. Could you provide AI output you’ve generated for students to work with, demonstrate use of the tool during class, or allow use of another tool that enables students to meet the same learning outcomes.

Microsoft Copilot is currently the only generative AI tool that has been vetted and approved for use at Ohio State. As of February 2024, the Office of Technology and Digital Innovation (OTDI) has enabled it for use by students, faculty, and staff. Copilot is an AI chatbot that draws from public online data, but with additional security measures in place. For example, conversations within the tool aren’t stored. Learn more and stay tuned for further information about Copilot in the classroom.

What are your expectations for academic integrity? This is a helpful step for clarifying your academic integrity guidelines for this assignment, around AI use specifically as well as for other resources and tools. The standard Academic Integrity Icons in the table below can help you call out what is permissible and what is prohibited. If any steps for completing the assignment require (or expressly prohibit) AI tools, be as clear as possible in highlighting which ones, as well as why and how AI use is (or is not) permitted.

Promoting academic integrity

While inappropriate use of AI may constitute academic misconduct, it can be muddy for students to parse out what is permitted or prohibited across their courses and across various use cases. Fortunately, there are existing approaches to supporting academic integrity that apply to AI as well as to any other tool. Discuss academic integrity openly with students, early in the term and before each assignment. Purposefully design your assignments to promote integrity by using real-world formats and audiences, grading the process as well as the product, incorporating personal reflection tasks, and more.

Learn about taking a proactive, rather than punitive, approach to academic integrity in A Positive Approach to Academic Integrity.

Step 3: Define criteria for success.

An important feature of transparent assignments is that they make clear to students how their work will be evaluated. During this TILT step, you will define criteria for a successful submission—consider creating a rubric to clarify these expectations for students and simplify your grading process. If you intend to use AI as a substitute or augmentation for another technology, you might be able to use an existing rubric with little or no change. However, if AI use is modifying or redefining the assignment tasks, a new grading rubric will likely be needed.

How will you grade this assignment? What key criteria will you assess?
What indicators will show each criterion has been met?
What qualities distinguish a successful submission from one that needs improvement?
Will you grade students on the product only or on aspects of the process as well? For example, if you have included a reflection task as part of the assignment, you might include that as a component of the final grade.

Alongside your rubric, it is helpful to prepare examples of successful (and even unsuccessful) submissions to provide more tangible guidance to students. In addition to samples of the final product, you could share examples of effective AI prompts, reflections tasks, and AI citations. Examples may be drawn from previous student work or models that you have mocked up, and they can be annotated to highlight notable elements related to assignment criteria.

Present and discuss your assignment.

As clear as we strive to be in our assignment planning and prompts, there may be gaps or confusing elements we have overlooked. Explicitly going over your assignment instructions—including the purpose, key tasks, and criteria—will ensure students are equipped with the background and knowledge they need to perform well. These discussions also offer space for students to ask questions and air unanticipated concerns, which is particularly important given the potential hesitance some may have around using AI tools.

How will this assignment help students learn key course content, contribute to the development of important skills such as critical thinking, or support them to meet your learning goals and outcomes?
How might students apply the knowledge and skills acquired in their future coursework or careers?
In what ways will the assignment further students’ understanding and experience around generative AI tools, and why does that matter?
What questions or barriers do you anticipate students might encounter when using AI for this assignment?

As noted above, many students are unaware of the accessibility, security, privacy, and copyright concerns associated with AI, or of other pitfalls they might encounter working with AI tools. Openly discussing AI’s limitations and the inaccuracies and biases it can create and replicate will support students to anticipate barriers to success on the assignment, increase their digital literacy, and make them more informed and discerning users of technology.

Explore available resources It can feel daunting to know where to look for AI-related assignment ideas, or who to consult if you have questions. Though generative AI is still on the rise, a growing number of useful resources are being developed across the teaching and learning community. Consult our other Teaching Topics, including AI Considerations for Teaching and Learning , and explore other recommended resources such as the Learning with AI Toolkit and Exploring AI Pedagogy: A Community Collection of Teaching Reflections.

If you need further support to review or develop assignment or course plans in light of AI, visit our Help forms to request a teaching consultation .

Using the Transparent Assignment Template

Sample assignment: ai-generated lesson plan.

In many respects, the rise of generative AI has reinforced existing best practices for assignment design—craft a clear and detailed assignment prompt, articulate academic integrity expectations, increase engagement and motivation through authentic and inclusive assessments. But AI has also encouraged us to think differently about how we approach the tasks we ask students to undertake, and how we can better support them through that process. While it can feel daunting to re-envision or reformat our assignments, AI presents us with opportunities to cultivate the types of learning and growth we value, to help students see that value, and to grow their critical thinking and digital literacy skills.

Using the Transparency in Learning and Teaching (TILT) framework to plan assignments that involve generative AI can help you clarify expectations for students and take a more intentional, productive, and ethical approach to AI use in your course.

Step 1: Define your purpose. Think about what you want students to gain from this assignment. What are your learning goals and outcomes? Do you want students to understand the limitations of AI, see its applications in your field, or reflect on its ethical implications? The SAMR model and Bloom's Taxonomy are useful references when defining your purpose for using (or not using) AI on an assignment.
Step 2: Define the tasks involved. L ist the steps students will take to complete the assignment. What resources and tools will they need? How will students reflect upon their learning as they proceed through each task? What are your expectations for academic integrity?
Step 3: Define criteria for success. Make clear to students your expectations for success on the assignment. Create a rubric to call out key criteria and simplify your grading process. Will you grade the product only, or parts of the process as well? What qualities indicate an effective submission? Consider sharing tangible models or examples of assignment submissions.

Finally, it is time to make your assignment guidelines and expectations transparent to students. Walk through the instructions explicitly—including the purpose, key tasks, and criteria—to ensure they are prepared to perform well.

Checklist for Designing Transparent Assignments
TILT Higher Ed Information and Resources

Winkelmes, M. (2013). Transparency in Teaching: Faculty Share Data and Improve Students’ Learning. Liberal Education 99 (2).

Wilkelmes, M. (2013). Transparent Assignment Design Template for Teachers. TiLT Higher Ed: Transparency in Learning and Teaching. https://tilthighered.com/assets/pdffiles/Transparent%20Assignment%20Templates.pdf

Winkelmes, M., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., Weavil, K. (2016). A Teaching Intervention that Increases Underserved College Students’ Success. Peer Review.

Six Characteristics of a Model Assignment

How many times have you had a student submit an assignment with few sources, poorly written and several days late? Probably happens more times than not. There are six characteristics of a model assignment which will not only alleviate instructor frustration, but also strengthen student writing and time management skills.

Create assignments which directly relate to accomplishing the course objective. A model assignment maintains a clear goal toward accomplishing a course objective. For adult online learners, course goals relate less to theory or original research and more to practical approaches for day-to-day application or career advancement.
More details equals higher quality of student final product. Since adult online learners come from diverse backgrounds, do not assume students will understand the purpose of the assignment. Be prepared to tell students what you expect (e.g. word count, citation format, number of sources, etc.) and how it should be done (e.g. upload to Moodle versus email attachment).
Give incremental due dates. Large comprehensive assignments due at the course finality leads to unfocused, or even plagiarized, writing. Break down a large assignment into several smaller assignments due sporadically throughout the term. In turn, students receive valuable feedback incrementally as they progress throughout the course.
Allow students to brainstorm for topics. Allow students to brainstorm topics or share with other students using the Moodle Discussion Board form. Or consider offering students a choice among 3-4 essay questions, case scenarios, or case studies. By allowing student choice, students will find a greater connection in their writing which in turn will lead to better final submissions.
Give examples. In addition to clear directions, students also appreciate a visual piece of the final product. If you decide to use another student’s work, be sure to ask permission to use from the student. Post model assignments on your Moodle course shell.
Share student evaluation tools. Share rubrics, or other evaluation tool, early in the assignment rather than at the end so students may clarify expectations firsthand. Post rubrics or evaluation tools on your Moodle course shell so students may refer to it when necessary.

Blogs @Oregon State University

Ecampus Course Development and Training

Providing inspiration for your online class.

The Power of an Assignment’s Purpose Statement

An illustration of a person kneeling and question marks around

Have you ever been assigned a task but found yourself asking: “What’s the point of this task? Why do I need to do this?” Very likely, no one has informed you of the purpose of this task! Well, it likely was because that activity was missing to show a critical element: the purpose. Just like the purpose of a task can be easily left out, in the context of course design, a purpose statement for an assignment is often missing too.

Creating a purpose statement for assignments is an activity that I enjoy very much. I encourage instructors and course developers to be intentional about that statement which serves as a declaration of the underlying reasons, directions, and focus of what comes next in an assignment. But most importantly, the statement responds to the question I mentioned at the beginning of this blog… why… ?

Just as a purpose statement should be powerful to guide, shape, and undergird a business (Yohn, 2022), a purpose statement for an assignment can guide students in making decisions about using strategies and resources, shape students’ motivation and engagement in the process of completing the assignment, and undergird their knowledge and skills. Let’s look closer at the power of a purpose statement.

What does “purpose” mean?

Merriam-Webster defines purpose as “ something set up as an object or end to be” , while Cambridge Dictionary defines it as “why you do something or why something exists”. These definitions show us that the purpose is the reason and the intention behind an action.

Why a purpose is important in an assignment?

The purpose statement in an assignment serves important roles for students, instructors, and instructional designers (believe it or not!).

For students

The purpose will:

answer the question “why will I need to complete this assignment?”
give the reason to spend time and resources working out math problems, outlining a paper, answering quiz questions, posting their ideas in a discussion, and many other learning activities.
highlight its significance and value within the context of the course.
guide them in understanding the requirements and expectations of the assignment from the start.

For instructors

guide the scope, depth, and significance of the assignment.
help to craft a clear and concise declaration of the assignment’s objective or central argument.
maintain the focus on and alignment with the outcome(s) throughout the assignment.
help identify the prior knowledge and skills students will be required to complete the assignment.
guide the selection of support resources.

For instructional designers

guide building the structure of the assignment components.
help identify additional support resources when needed.
facilitate an understanding of the alignment of outcome(s).
help test the assignment from the student’s perspective and experience.

Is there a wrong purpose?

No, not really. But it may be lacking or it may be phrased as a task. Let’s see an example (adapted from a variety of real-life examples) below:

Project Assignment:

“The purpose of this assignment is to work in your group to create a PowerPoint presentation about the team project developed in the course. Include the following in the presentation:

Purpose of project
Target audience
Application of methods
Recommendations
Sources (at least 10)
Images and pictures

The presentation should be a minimum of 6 slides and must include a short reflection on your experience conducting the project as a team.”

What is unclear in this purpose? Well, unless the objective of the assignment is to refine students’ presentation-building skills, it is unclear why students will be creating a presentation for a project that they have already developed. In this example, creating a presentation and providing specific details about its content and format looks more like instructions instead of a clear reason for this assignment to be.

A better description of the purpose could be:

“The purpose of this assignment is to help you convey complex information and concepts in visual and graphic formats. This will help you practice your skills in summarizing and synthesizing your research as well as in effective data visualization.”

The purpose statement particularly underscores transparency, value, and meaning. When students know why, they may be more compelled to engage in the what and how of the assignment. A specific purpose statement can promote appreciation for learning through the assignment (Christopher, 2018).

Examples of purpose statements

Below you will find a few examples of purpose statements from different subject areas.

Example 1: Application and Dialogue (Discussion assignment)

Courtesy of Prof. Courtney Campbell – PHL /REL 344

Example 2: An annotated bibliography (Written assignment)

Courtesy of Prof. Emily Elbom – WR 227Z

Example 3: Reflect and Share (Discussion assignment)

Courtesy of Profs. Nordica MacCarty and Shaozeng Zhang – ANTH / HEST 201

With the increased availability of language learning models (LLMs) and artificial intelligence (AI) tools (e.g., ChatGPT, Claude2), many instructors worry that students would resort to these tools to complete the assignments. While a clear and explicit purpose statement won’t deter the use of these highly sophisticated tools, transparency in the assignment description could be a good motivator to complete the assignments with no or little AI tools assistance.

“ Knowing why you do what you do is crucial ” in life says Christina Tiplea. The same applies to learning, when “why” is clear, the purpose of an activity or assignment can become a more meaningful and crucial activity that motivates and engages students. And students may feel less motiavted to use AI tools (Trust, 2023).

Note : This blog was written entirely by me without the aid of any artificial intelligence tool. It was peer-reviewed by a human colleague.

Christopher, K. (02018). What are we doing and why? Transparent assignment design benefits students and faculty alike . The Flourishing Academic .

Sinek, S. (2011). Start with why . Penguin Publishing Group.

Trust, T. (2023). Addressing the Possibility of AI-Driven Cheating, Part 2 . Faculty Focus.

Yohn, D.L. (2022). Making purpose statements matter . SHR Executive Network.

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Clinical trial basics: intervention models in clinical trials, what are intervention models in clinical trials.

In clinical trials, intervention model refers to the general structure used for dividing study participants into groups to compare outcomes. These groups are also known as interventional or treatment arms.

What are the different types of intervention models in clinical trials?

Intervention models generally fall under four types: single-group assignments, parallel assignments, cross-over assignments, and factorial assignments.[ 1 ]

The model that is most appropriate for a trial depends on several factors, such as:

The medical condition being tested
The research goals of the trial
The availability of eligible participants

Single group assignment

In single-group assignment, participants are not divided into groups at all. Instead, all participants are assigned to the therapy arm of the trial and receive the same treatment, therapy, or drug, with the same route of administration, dosage, and frequency.

An example of a single-group study is a phase 4 clinical study observing the long-term effects of a newly approved drug in all participants enrolled in the study.

Parallel assignment

Parallel assignment is the most common type of intervention model used in clinical research, wherein trial participants are divided into two or more groups, each receiving a different medical intervention throughout the duration of the study.[ 2 ] Participants are given one type of treatment, remaining in the same treatment arm for the entire study, so such studies are also known as non-crossover studies.

An example of a clinical study that uses parallel assignment is a phase III clinical trial comparing the investigational product (drug X) against the standard treatment (drug A) for the condition:

Group 1 (experimental treatment arm) receives drug X
Group 2 (standard treatment control arm) receives drug A

Different dosages of the same drug can also be studied in a parallel group study, for example:

Group 1 receives 50 mg of drug X
Group 2 receives 100 mg of drug X

Cross-over assignment

In a cross-over assignment design, researchers divide trial participants into groups that receive the same experimental treatment(s) but at different times. In other words, participants are switched from one study arm to the other at a given point in time. Sometimes also referred to as a cross-over longitudinal study, this type of intervention model attempts to reduce patient variation for more accurate results.[ 3 ] It may also have ethical benefits as all participants are given a chance to benefit from the investigational treatment, which may further encourage patient enrollment and retention.

To reduce any carryover effect from the previous treatments, studies conducted under this model usually include a washout period so the previous treatment can be fully eliminated from the participant's system. Cross-over assignments are generally used when studying chronic conditions, because symptoms are long-term so investigators have enough time to change treatments and study the effects.

A clinical trial employing a cross-over assignment might assign participants to study arms as follows:

Group 1 receives drug X for the first 6 weeks, then drug Y for 6 weeks, with a 6-week washout period in between
Group 2 receives drug Y for the first 6 weeks, then drug X for 6 weeks, with a 6-week washout period in between

Or, another example:

First 2 months: Group 1 receives the experimental intervention while group 2 receives placebo
2 week washout period
Last 2 months: Group 2 receives the experimental intervention while group 1 receives placebo

Factorial assignment

Factorial assignment designs are used when there is more than one intervention to be tested. Trial participants are divided into groups or arms receiving different combinations of two or more interventions/drugs.

The simplest factorial design is a so-called 2x2 factorial assignment, in which two drugs, X and Y, might be tested in four study groups/arms as follows:

Group 1 receives drug X and drug Y together
Group 2 receives drug X and placebo (control)
Group 3 receives drug Y and placebo (control)
Group 4 receives two placebos

The appeal of such a design is that it is essentially similar to conducting two parallel group studies (drug X versus placebo and drug Y versus placebo) on the same study population, allowing comparison of the safety and/or efficacy of drug X versus drug Y. Further, potential interaction (synergy or antagonism) between the two interventions might be elucidated, although this is not the goal of such a study. A general assumption underlying such designs is that the two interventions do not interact with one another, in which case the statistical power of such a study design is greater than that of a multi-arm parallel group trial.[ 4 ] However, it is often difficult to be sure that there was no interaction, which can lead to difficulty in interpreting results.

Other Trials to Consider

Arm 1: 3D printed model

Intermittent theta burst stimulation, breast mri screening for high risk patients, single arm longitudinal assessment, 3d model of heart, cbt (surgerypal), standard of care + cbt4cbt+ rc, popular categories.

Tymlos Clinical Trials

Paid Clinical Trials in Kansas City, MO

Paid Clinical Trials in Dallas, TX

Paid Clinical Trials in Tucson, AZ

Paid Clinical Trials in Spokane, WA

Paid Clinical Trials in Glendale, AZ

Paid Clinical Trials in Madison, WI

Paid Clinical Trials in New Mexico

Pheochromocytoma Clinical Trials 2023

Adrenal Insufficiency Clinical Trials 2023

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Traffic Assignments to Transportation Networks
Section 3.1 introduces the assignment problem in transportation as the distribution of traffic in a network considering the demand between locations and the transport supply of the network. Four trip assignment models relevant to transportation are presented and characterized. Section 3.2 covers traffic assignment to uncongested networks based ...
PDF Chapter 5: Linear Programming: Transportation and Assignment Models
transportation model can be extended to other areas of operation, including, among others, inventory control, employment scheduling, and personnel assignment. The transportation problem (model) seeks the determination of a transportation plan of a single commodity from a number of sources (origins) to a number of destinations. It involves the
Designing Assignments for Learning
Communicate about the assignment Share the purpose, task, audience, expectations, and criteria for the assignment. Students may have expectations about assessments and how they will be graded that is informed by their prior experiences completing high-stakes assessments, so be transparent. Tell your students why you are asking them to do this ...
Assignment Model in Quantitative Techniques (QT)
The Assignment Model is a valuable tool in the field of Operations Research with several important applications. Here are some key reasons highlighting its importance: 1. Optimal resource allocation: The Assignment Model is used to solve allocation problems where a set of tasks or resources needs to be assigned to a set of individuals or entities.
How Do I Create Meaningful and Effective Assignments?
First, you will need to articulate the purpose of the assignment. Even though you know why the assignment is important and what it is meant to accomplish, you cannot assume that your students will intuit that purpose. ... Do consider providing models- both successful and unsuccessful models (Miller, 2007). These models could be provided by ...
Designing Assignments
The purpose of this assignment is to help you synthesize some difficult political theory and identify the profound differences among some key theorists. The best papers will focus on a single shared aspect of the theorists' respective political ideologies, such as how property is distributed, whether it should be owned, or what role it serves ...
PDF How Do I Create Meaningful and Effective Assignments?
models could be provided by past students, or models you have created yourself. You could even ask students to evaluate the models themselves using the determined evaluation criteria, helping them to visualize the final product, think critically about how to complete the assignment, and ideally, recognize success in their own work.
Knowledge‐Based Assignment Model for Allocation of Employees in
The purpose of this model is to assign knowledge workers to every task and arrange them (the tasks) in order to minimize the total time required to finish all projects. ... There are also worker assignment models originating in production layout and shifts. McDonald et al. developed a worker assignment model to evaluate a lean manufacturing ...
Patient Assignment Models in the Emergency Department
Patient Assignment Models in the ED 609. PATIENT ASSIGNMENT TO FAST-TRACK AND VERTICAL PATHWAYS Another model in which patient assignment plays a role is in the streaming of patients into different areas of the ED, staffed by separate providers, based on their initial pre-sentation. This most common version of this model is a "fast-track."
4.3: Types of Assignments
By recognising different types of assignments and understanding the purpose of the task, you can direct your writing skills effectively to meet task requirements. ... The chapter begins by exploring the popular essay assignment, with its two common categories, ... This model (Ryan and Ryan, 2013) was designed specifically for university ...
Assignment Method: Examples of How Resources Are Allocated
Assignment Method: A method of allocating organizational resources. The assignment method is used to determine what resources are assigned to which department, machine or center of operation in ...
Types of Assignments
Types of Assignments Cristy Bartlett and Kate Derrington. Figure 20.1 By recognising different types of assignments and understanding the purpose of the task, you can direct your writing skills effectively to meet task requirements. Image by Armin Rimoldi used under CC0 licence. Introduction. As discussed in the previous chapter, assignments are a common method of assessment at university.
AI Teaching Strategies: Transparent Assignment Design
Consider sharing tangible models or examples of assignment submissions. Finally, it is time to make your assignment guidelines and expectations transparent to students. Walk through the instructions explicitly—including the purpose, key tasks, and criteria—to ensure they are prepared to perform well.
Six Characteristics of a Model Assignment
A model assignment maintains a clear goal toward accomplishing a course objective. For adult online learners, course goals relate less to theory or original research and more to practical approaches for day-to-day application or career advancement. ... Since adult online learners come from diverse backgrounds, do not assume students will ...
The Power of an Assignment's Purpose Statement
The purpose will: guide the scope, depth, and significance of the assignment. help to craft a clear and concise declaration of the assignment's objective or central argument. maintain the focus on and alignment with the outcome (s) throughout the assignment. help identify the prior knowledge and skills students will be required to complete ...
Assignment Method
Assignment Method Explained. The assignment method in operation research is a strategy for allocating organizational resources to tasks to increase profit via efficiency gains, cost reductions, and improved handling of operations that might create bottlenecks.It is an operations management tool that, by allocating jobs to the appropriate individual, minimizes expenses, time, and effort.
Clinical Trial Basics: Intervention Models in Clinical Trials
Parallel assignment is the most common type of intervention model used in clinical research, wherein trial participants are divided into two or more groups, each receiving a different medical intervention throughout the duration of the study.[2] Participants are given one type of treatment, remaining in the same treatment arm for the entire ...

Assignment Model | Linear Programming Problem (LPP) | Introduction

For example:

How Assignment Problem is related to LPP? OR Write mathematical formulation of Assignment Model.

Types of Assignment Problem:

(ii) Unbalanced Assignment Problem

Methods to solve Assignment Model:

(ii) Transportation Methods:

(iii) Enumeration Method:

(iv) Hungarian Method:

Suggested Notes:

Modified Distribution Method (MODI) | Transportation Problem | Transportation Model

Stepping Stone | Transportation Problem | Transportation Model

Vogel’s Approximation Method (VAM) | Method to Solve Transportation Problem | Transportation Model

Transportation Model - Introduction

North West Corner Method | Method to Solve Transportation Problem | Transportation Model

Least Cost Method | Method to Solve Transportation Problem | Transportation Model

Tie in selecting row and column (Vogel's Approximation Method - VAM) | Numerical | Solving Transportation Problem | Transportation Model

Crashing Special Case - Multiple (Parallel) Critical Paths

Crashing Special Case - Indirect cost less than Crash Cost

Basics of Program Evaluation and Review Technique (PERT)

Numerical on PERT (Program Evaluation and Review Technique)

Network Analysis - Dealing with Network Construction Basics

Construct a project network with predecessor relationship | Operation Research | Numerical

Graphical Method | Methods to solve LPP | Linear Programming

Basics of Linear Programming

Linear Programming Problem (LPP) Formulation with Numericals

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Transportation and Assignment Models in Operations Research

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13 Last Step of Four Step Modeling (Trip Assignment Models)

Introduction

Link Performance Function

Performance Function Example

Traffic Assignment Models

All-or-nothing Model

All-or-nothing Example

User Equilibrium

UE Example

System Optimum Assignment

Incremental Increase Example

Capacity Restraint Assignment

Feedback Loop Model (Combined Traffic Assignment and Trip Distribution)

Stochastic User Equilibrium Traffic Assignment

Stochastic User Equilibrium Example

Dynamic Traffic Assignment

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Designing Assignments for Learning

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Reflect On Your Assignment Design

Examples from the Columbia University Classroom

​Tips for Designing Assignments for Learning

Align the assignment with your course learning objectives

Identify an authentic meaning-making task

Determine the evaluation criteria and create a rubric

Build in metacognition

Provide students with opportunities to practice

Communicate about the assignment

Plan to iterate and to keep the focus on learning

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Tips for Designing Assignments for Learning