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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.

On this page:

Rethinking traditional tests, quizzes, and exams.

• Examples from the Columbia University Classroom
• Tips for Designing Assignments for Learning

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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Teaching Commons > Teaching Guides > Assignment Design

Assignment Design

Here's a short list of some general assignment design strategies that apply to a wide variety of disciplines.

A number of strategies for deterring plagiarism are discussed, including asking your students to write about current topics relevant to your course and staging essay assignments throughout the quarter.

​Integrative learning occurs when students make connections among ideas and experiences in order to transfer learning to new contexts.​

Eberly Center

Teaching excellence & educational innovation, creating assignments.

Here are some general suggestions and questions to consider when creating assignments. There are also many other resources in print and on the web that provide examples of interesting, discipline-specific assignment ideas.

Consider your learning objectives.

What do you want students to learn in your course? What could they do that would show you that they have learned it? To determine assignments that truly serve your course objectives, it is useful to write out your objectives in this form: I want my students to be able to ____. Use active, measurable verbs as you complete that sentence (e.g., compare theories, discuss ramifications, recommend strategies), and your learning objectives will point you towards suitable assignments.

Design assignments that are interesting and challenging.

This is the fun side of assignment design. Consider how to focus students’ thinking in ways that are creative, challenging, and motivating. Think beyond the conventional assignment type! For example, one American historian requires students to write diary entries for a hypothetical Nebraska farmwoman in the 1890s. By specifying that students’ diary entries must demonstrate the breadth of their historical knowledge (e.g., gender, economics, technology, diet, family structure), the instructor gets students to exercise their imaginations while also accomplishing the learning objectives of the course (Walvoord & Anderson, 1989, p. 25).

Double-check alignment.

After creating your assignments, go back to your learning objectives and make sure there is still a good match between what you want students to learn and what you are asking them to do. If you find a mismatch, you will need to adjust either the assignments or the learning objectives. For instance, if your goal is for students to be able to analyze and evaluate texts, but your assignments only ask them to summarize texts, you would need to add an analytical and evaluative dimension to some assignments or rethink your learning objectives.

Name assignments accurately.

Students can be misled by assignments that are named inappropriately. For example, if you want students to analyze a product’s strengths and weaknesses but you call the assignment a “product description,” students may focus all their energies on the descriptive, not the critical, elements of the task. Thus, it is important to ensure that the titles of your assignments communicate their intention accurately to students.

Consider sequencing.

Think about how to order your assignments so that they build skills in a logical sequence. Ideally, assignments that require the most synthesis of skills and knowledge should come later in the semester, preceded by smaller assignments that build these skills incrementally. For example, if an instructor’s final assignment is a research project that requires students to evaluate a technological solution to an environmental problem, earlier assignments should reinforce component skills, including the ability to identify and discuss key environmental issues, apply evaluative criteria, and find appropriate research sources.

Think about scheduling.

Consider your intended assignments in relation to the academic calendar and decide how they can be reasonably spaced throughout the semester, taking into account holidays and key campus events. Consider how long it will take students to complete all parts of the assignment (e.g., planning, library research, reading, coordinating groups, writing, integrating the contributions of team members, developing a presentation), and be sure to allow sufficient time between assignments.

Check feasibility.

Is the workload you have in mind reasonable for your students? Is the grading burden manageable for you? Sometimes there are ways to reduce workload (whether for you or for students) without compromising learning objectives. For example, if a primary objective in assigning a project is for students to identify an interesting engineering problem and do some preliminary research on it, it might be reasonable to require students to submit a project proposal and annotated bibliography rather than a fully developed report. If your learning objectives are clear, you will see where corners can be cut without sacrificing educational quality.

Articulate the task description clearly.

If an assignment is vague, students may interpret it any number of ways – and not necessarily how you intended. Thus, it is critical to clearly and unambiguously identify the task students are to do (e.g., design a website to help high school students locate environmental resources, create an annotated bibliography of readings on apartheid). It can be helpful to differentiate the central task (what students are supposed to produce) from other advice and information you provide in your assignment description.

Establish clear performance criteria.

Different instructors apply different criteria when grading student work, so it’s important that you clearly articulate to students what your criteria are. To do so, think about the best student work you have seen on similar tasks and try to identify the specific characteristics that made it excellent, such as clarity of thought, originality, logical organization, or use of a wide range of sources. Then identify the characteristics of the worst student work you have seen, such as shaky evidence, weak organizational structure, or lack of focus. Identifying these characteristics can help you consciously articulate the criteria you already apply. It is important to communicate these criteria to students, whether in your assignment description or as a separate rubric or scoring guide . Clearly articulated performance criteria can prevent unnecessary confusion about your expectations while also setting a high standard for students to meet.

Specify the intended audience.

Students make assumptions about the audience they are addressing in papers and presentations, which influences how they pitch their message. For example, students may assume that, since the instructor is their primary audience, they do not need to define discipline-specific terms or concepts. These assumptions may not match the instructor’s expectations. Thus, it is important on assignments to specify the intended audience http://wac.colostate.edu/intro/pop10e.cfm (e.g., undergraduates with no biology background, a potential funder who does not know engineering).

Specify the purpose of the assignment.

If students are unclear about the goals or purpose of the assignment, they may make unnecessary mistakes. For example, if students believe an assignment is focused on summarizing research as opposed to evaluating it, they may seriously miscalculate the task and put their energies in the wrong place. The same is true they think the goal of an economics problem set is to find the correct answer, rather than demonstrate a clear chain of economic reasoning. Consequently, it is important to make your objectives for the assignment clear to students.

Specify the parameters.

If you have specific parameters in mind for the assignment (e.g., length, size, formatting, citation conventions) you should be sure to specify them in your assignment description. Otherwise, students may misapply conventions and formats they learned in other courses that are not appropriate for yours.

A Checklist for Designing Assignments

Here is a set of questions you can ask yourself when creating an assignment.

• Provided a written description of the assignment (in the syllabus or in a separate document)?
• Specified the purpose of the assignment?
• Indicated the intended audience?
• Articulated the instructions in precise and unambiguous language?
• Provided information about the appropriate format and presentation (e.g., page length, typed, cover sheet, bibliography)?  
• Indicated special instructions, such as a particular citation style or headings?  
• Specified the due date and the consequences for missing it?
• Articulated performance criteria clearly?
• Indicated the assignment’s point value or percentage of the course grade?
• Provided students (where appropriate) with models or samples?

Adapted from the WAC Clearinghouse at http://wac.colostate.edu/intro/pop10e.cfm .

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Center for Teaching Innovation

Ai in assignment design.

Using generative artificial intelligence (AI) can be both productive and limiting—it can help students to create and revise content, yet it also has the potential to undermine the process by which students create. When incorporated effectively into assignments, generative AI can be leveraged to stimulate students' ability to apply essential knowledge and develop critical thinking skills. 

As you explore the possible uses of generative AI in your course, note that establishing a general familiarity with generative AI and being mindful of accessibility and ethical concerns will be helpful. 

The following process may help you determine how to best incorporate generative AI into your course assignments.

Affirm What You Actually Want to Assess

As you decide how you might incorporate AI into your course, it’s important to revisit your current course assessment plan, most importantly your course learning outcomes —that is, the skills and knowledge you want students to learn and demonstrate by the end of your course. Once you have a clear idea of the specific skills/knowledge you want to assess, the following questions can help determine whether or not your current assignments are effective and assessing what you want them to assess:

• Does my assignment call for the same type of thinking skills that are articulated in my class outcomes? For example, if my course learning outcome calls for students to analyze major themes in a work, is there risk of my final assignment prompting students to do more (e.g., synthesize multiple themes across multiple works) or to do less (e.g., merely identify a theme) than this outcome? If so, there may be a misalignment that can easily be addressed.
• Does my assignment call for the same type of thinking skills that students have actually practiced in class? For example, if I am asking students to generate a research prospectus, have I given them adequate opportunity to develop—and receive feedback on—this skill in class?
• Depending on your discipline, is there a need for an additional course outcome that honors what students now need to know about the use of generative AI in your course/field?

Explore When & How Generative AI Can Facilitate Student Learning

Once you have affirmed your learning outcomes and ensured that your assignments are properly aligned with those outcomes, think about if, when, and how it might make sense to incorporate generative AI. Is there a way to leverage generative AI to engage students in deeper learning, provide meaningful practice, or help scaffold your assignments?

Consider the usefulness of generative AI to serve as:

• Have students analyze AI-generated texts to articulate what constitutes “good” (and not so good) responses to prompts.
• Have students analyze AI-generated texts and engage in error analysis to develop more nuanced and discipline-specific writing skills.
• Leverage the use of generative AI platforms to help students become more discerning. This can help students develop the critical thinking and information literacy skills required to effectively and responsibly use such platforms.
• Have students revise AI-generated texts to develop critical thinking skills.
• Have students engage with a generative AI platform as a tutor. 
• Facilitate students’ responsible, self-guided use of generative AI to develop select discipline specific skills (e.g., coding in computer science courses)
• Have students use generative AI to off-load repetitive tasks.
• Have students use generative AI to conduct preliminary analysis of data sets to confirm broad takeaways and affirm that their more nuanced analysis is heading in the right direction.

Identify When Generative AI Cannot Facilitate Student Learning

It is often the case that students cannot—or should not—leverage generative AI to promote or demonstrate their own learning. To help ensure that your assignment design highlights students’ unique perspectives and underscores the importance of a (non-generative AI informed) discipline-specific process, consider how to emphasize metacognition, authentic application, thematic connection, or personal reflection.  

Even if another part of an assignment calls for the use of generative AI, the following strategies may supplement the uses of AI highlighted above and foster deep and meaningful learning:

• Have students identify the successes and challenges they experienced throughout the completion of a project.
• Have students set incremental goals throughout a project, highlighting next steps of a discipline-specific process, resources they used, and the steps about which they are enthusiastic/nervous.
• Have students self-assess their work, identifying strengths and weaknesses of their product/effort.
• Have students engage in problem-based learning projects, ideally in authentic settings (e.g., problems that focus on our local community, real-world challenges, real-world industries, etc.).
• Have students present projects (and engage with) authentic audiences (e.g., real stakeholders, discipline-specific research partners, native-speaking language partners, etc.)
• Have students connect select reading(s) to course experiences (e.g., labs, field experiences, class discussions). 
• Leverage Canvas-based tools that promote student-to-student interactions (e.g., Hypothesis for social annotation or FeedbackFruits for peer review and feedback).
• Have students provide a reflective rationale for choices made throughout the completion of a class project (e.g., an artist statement, response to a reflection prompt about personal relevance of source selections)
• Have students connect course experiences/motivations to their own lived experiences.

Create Transparent Assignment Materials

Once you have thought about whether or not generative AI can be effectively incorporated into your assignments, it is important to create assignment materials that are transparent (Winkelmes, et al., 2019). Specifically, this means creating ways to communicate to students the task you are are requiring, along with its purpose and evaluative criteria:

• Task. Students will benefit from having a clear and accessible set of directions for the project or assignment you are asking them to complete. 
• Purpose. Students are often more motivated when they understand why a particular task is worth doing and what specific knowledge or skills they will develop by completing the assigned task.
• Evaluative Criteria. Students benefit from having a clear sense of how their work will be evaluated and a full understanding of what good work looks like.

Communicate Your Expectations for Generative AI Use 

Regardless of the extent to which you incorporate the use of generative AI into your assignment design, it is essential to communicate your expectations to students. Sharing clear directions for assignments, communicating how students can be successful in your class, and promoting academic integrity serves both you and your students well. 

Example Assignment Policy Language for Generative AI Use

The following language on the use of generative AI may be helpful as you create directions for specific assignments. Please note that the following sample language does not reflect general, course-level perspectives on the use of generative AI tools. For sample course-level statements, see AI & Academic Integrity .

Prohibiting AI Use for a Specific Assignment

Allowing the use of generative ai for a specific assignment with attribution.

For full details on how to properly cite AI-generated work, please see the APA Style article, How to Cite ChatGPT . "

Encouraging the Use of Generative AI for a Specific Assignment with Attribution

For full details on how to properly cite AI- generated work, please see the APA Style article, How to Cite ChatGPT ."

Confer with Colleagues

There is almost always a benefit to discussing an assessment plan with colleagues, either within or beyond your department. Remember, too, that CTI offers consultations on any topic related to teaching and learning, and we are delighted to collaboratively review your course assessment plan. Visit our Consultations page to learn more, or contact us to set up a consultation.

2023 EducaUse Horizon Report | Teaching and Learning Edition. (2023, May 8). EDUCAUSE Library. https://library.educause.edu/resources/2023/5/2023-educause-horizon-report-teaching-and-learning-edition

Antoniak, M. (2023, June 22). Using large language models with care - AI2 blog. Medium. https://blog.allenai.org/using-large-language-models-with-care-eeb17b0aed27

Dinnar, S. M., Dede, C., Johnson, E., Straub, C. and Korjus, K. (2021), Artificial Intelligence and Technology in Teaching Negotiation. Negotiation Journal, 37: 65-82. https://doi.org/10.1111/nejo.12351

Jensen, T., Dede, C., Tsiwah, F., & Thompson, K. (2023, July 27). Who Does the Thinking: The Role of Generative AI in Higher Education. YouTube. International Association of Universities. Retrieved July 27, 2023.

OpenAI. (2023, February 16.). How should AI systems behave, and who should decide? https://openai.com/blog/how-should-ai-systems-behave

Winkelmes, M. A., Boye, A., & Tapp, S. (2019). Transparent design in higher education 

teaching and leadership: A guide to implementing the transparency framework institution-wide to improve learning and retention. Sterling, VA: Stylus Publishing .

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Designing assignments.

Making a few revisions to your writing assignments can make a big difference in the writing your students will produce. The most effective changes involve specifying what you would like students to do in the assignment and suggesting concrete steps students can take to achieve that goal.

Clarify what you want your students to do…and why they’re doing it

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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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. 

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.

Related Teaching Topics

Ai considerations for teaching and learning, ai teaching strategies: having conversations with students, designing assessments of student learning, search for resources.

3 Learning Theories: Understanding How People Learn

Introduction.

Learning theories describe the conditions and processes through which learning occurs, providing teachers with models to develop instruction sessions that lead to better learning. These theories explain the processes that people engage in as they make sense of information, and how they integrate that information into their mental models so that it becomes new knowledge. Learning theories also examine what motivates people to learn, and what circumstances enable or hinder learning.

Sometimes people are skeptical of having to learn theory, believing those theories will not be relevant in the real world, but learning theories are widely applicable. The models and processes that they describe tend to apply across different populations and settings, and provide us with guidelines to develop exercises, assignments, and lesson plans that align with how our students learn best. Learning theories can also be engaging. People who enjoy teaching often find the theories interesting and will be excited when they start to see connections between the theory and the learning they see happening in their own classrooms.

General Learning Theories

With a basic understanding of learning theories, we can create lessons that enhance the learning process. This understanding helps us explain our instructional choices, or the “why” behind what and how we teach. As certain learning theories resonate with us and we consciously construct lessons based on those theories, we begin to develop a personal philosophy of teaching that will guide our instructional design going forward. This chapter provides a bridge from theory to practice by providing specific examples of how the theories can be applied in the library classroom. These theories provide a foundation to guide the instructional design and reflective practices presented in the rest of this textbook.

As you read, you might consider keeping track of the key points of each theory and thinking about how these theories could be applied to your practice. Figure 3.1 provides you with an example of a graphic organizer, one of the instructional materials that will be discussed in Chapter 11, that you could use to take notes as you read this chapter.  In addition to the examples in practice that are provided in this chapter, you might add some of your own.

Figure 3.1: Graphic Organizer for Major Learning Theories

Behaviorism

Behaviorism is based largely on the work of John B. Watson and B. F. Skinner. Behaviorists were concerned with establishing psychology as a science and focused their studies on behaviors that could be empirically observed, such as actions that could be measured and tested, rather than on internal states such as emotions (McLeod, 2015). According to behaviorists, learning is dependent on a person’s interactions with their external environment. As people experience consequences from their interactions with the environment, they modify their behaviors in reaction to those consequences. For instance, if a person hurts their hand when touching a hot stove, they will learn not to touch the stove again, and if they are praised for studying for a test, they will be likely to study in the future

According to behavioral theorists, we can change people’s behavior by manipulating the environment in order to encourage certain behaviors and discourage others, a process called conditioning (Popp, 1996). Perhaps the most famous example of conditioning is Pavlov’s dog. In his classic experiment, Pavlov demonstrated that a dog could be conditioned to associate the sound of a bell with food, so that eventually the dog would salivate whenever it heard the bell, regardless of whether it received food. Watson adapted stimulus conditioning to humans (Jensen, 2018). He gave an 11-month-old baby a rat, and the baby seemed to enjoy playing with it. Over time, Watson caused a loud, unpleasant sound each time he brought out the rat. Eventually, the baby associated the rat with the noise and cried when he saw the rat. Although Watson’s experiment is now considered ethically questionable, it did establish that people’s behavior could be modified through control of environmental stimuli.

Skinner (1938) examined how conditioning could shape behavior in longer-term and more complex ways by introducing the concept of reinforcement. According to Skinner, when people receive positive reinforcement, such as praise and rewards for certain behaviors, those behaviors are strengthened, while negative reinforcement will deter behaviors. According to Skinner, by carefully controlling the environment and establishing a system of reinforcements, teachers, parents, and others can encourage and develop desired behaviors (Jensen, 2018). A simple example of behaviorism in the classroom is a point system in which students are awarded points for good behavior and deducted points for unwanted behavior. Eventually, accumulated points might be traded in for rewards like small gifts or homework passes. This approach assumes that motivation is external, in that students will engage in certain behaviors in order to gain the rewards.

Because it emphasizes the external environment, behaviorism largely ignores or discounts the role of internal influences such as prior knowledge and emotion (Popp, 1996).  To an extent, behaviorists view learners as blank slates and emphasize the role of the teacher in the classroom. In this teacher-centered approach, instructors hold the knowledge, decide what will be learned, and establish the rewards for learning. Since their experience and prior knowledge are not considered relevant, learners are passive participants simply expected to absorb the knowledge transmitted by the teacher. While the idea of learners as blank slates has fallen out of favor, many of the conditioning aspects of behaviorism remain popular. As almost any student can attest, behavioral methods of reinforcement, such as the point system described above, are still common, especially in younger grades. Recent trends toward gaming in the classroom, where certain behaviors are rewarded with points and leveling up, are based in a behaviorist approach to learning. See Activity 3.1 for a brief activity on behaviorism.

Activity 3.1: Reflecting on Behaviorism

Think of some of your own learning experiences, whether they were in a traditional classroom, through professional development training, or related to personal interests, such as dance or photography lessons. Try to identify a few examples of behaviorism from those experiences and reflect on the following questions:

• How did your instructors use behavioral practice in their classrooms?
• Did you find those practices motivating? Why or why not?
• If you can think of examples of behaviorism from several different learning experiences, were they more appropriate in some situations than others? How so?
• Have you ever used, or can you imagine using, behaviorism in your own teaching practice? How so?

Humanism recognizes the basic dignity and worth of each individual and believes people should be able to exercise some control over their environment. Although humanism as an educational philosophy has its roots in the Italian Renaissance, the more modern theorists associated with this approach include John Dewey, Carl Rogers, Maria Montessori, Paolo Freire, and Abraham Maslow. Humanist learning theory is a whole-person approach to education that centers on the individual learners and their needs, and that considers affective as well as cognitive aspects of learning. At its essence, “humanism in education traditionally has referred to a broad, diffuse outlook emphasizing human freedom, dignity, autonomy, and individualism” (Lucas, 1996). Within this broader context, humanism is also characterized by the following tenets (Madsen & Wilson, 2012; Sharp, 2012):

• Students are whole people, and learning must attend to their emotional as well as their cognitive state.
• Teachers should be empathetic.
• Learners are self-directed and internally motivated.
• The outcome of learning is self-actualization.

Humanism centers the individual person as the subject and recognizes learners as whole beings with emotional and affective states that accompany their cognitive development. Recognizing the role of students’ emotions means understanding how those emotions impact learning. Student anxiety, say around a test or a research paper, can interfere with the cognitive processes necessary to be successful. Empathetic teachers recognize and try to understand students’ emotional states, taking steps to alleviate negative emotions that might detract from learning by creating a supportive learning environment.

In a library context, Mellon (1986) identified the phenomenon of library anxiety, or the negative emotions that some people experience when doing research or interacting with library tools and services. This anxiety can distract learners and make it difficult to engage in the processes necessary to search for, evaluate, and synthesize the information they need to complete their task. Similarly, in her Information Search Process, Kuhlthau (1990) describes the affective states as well as the cognitive processes students engage in when doing research, acknowledging that their emotions fluctuate among anxiety, optimism, and, ultimately, satisfaction or disappointment.

A humanist approach to education recognizes these affective states and seeks to limit their negative impact. For instance, we can acknowledge that feelings of anxiety are common so learners recognize that they are not alone. We can also explain how the skills students learn are relevant to their lives in and outside of the classroom.

Because humanists see people as autonomous beings, they believe that learning should be self-directed, meaning students should have some choice in what and how they learn. Humanistic education is often connected with student-centered pedagogical approaches such as differentiated curricula, self-paced learning, and discovery learning (Lucas, 1996). Self-directed learning can take many forms, but it generally means that the instructor acts as a guide, and learners are given the freedom to take responsibility for their own learning. Teachers will provide the materials and opportunities for learning, but students will engage with the learning on their own terms. In a library classroom, we can give students choices about the topics they will research or offer learners different types of activities to practice skills and demonstrate what they have learned.

Humanists also believe that learning is part of a process of self-actualization. They maintain that learning should be internally motivated and driven by students’ interests and goals, rather than externally motivated and focused on a material end goal such as achievement on tests, or employment (Sharp, 2012). The expectation is that when students are allowed to follow their interests and be creative, and when learning takes place within a supportive environment, students will engage in learning for its own sake. This emphasis on self-actualization is largely based on Maslow’s (1943) hierarchy of needs. Maslow identified five levels of needs: basic physiological needs such as food, water, and shelter; safety and security needs; belongingness and love needs, including friends and intimate relationships; esteem needs, including feelings of accomplishment; and self-actualization, when people achieve their full potential. Importantly, these needs are hierarchical, meaning a person cannot achieve the higher needs such as esteem and self-actualization until more basic needs such as food and safety are met. The role of the humanist teacher is to facilitate the student’s self-actualization by helping to ensure needs such as safety and esteem are met through empathetic teaching and a supportive classroom.

In his book, Pedagogy of the Oppressed , Freire (2000) brings together many of the student-centered elements of humanistic education, with a strong emphasis on social justice aspects of learning and teaching. In contrast to behaviorist approaches, Freire emphasizes the importance of students’ life experience to their learning. He criticizes what he describes as the “banking model” of education, in which students are viewed as passive and empty vessels into which teachers simply deposit bits of knowledge that students are expected to regurgitate on exams or papers without any meaningful interaction. Freire insists that learning must be relevant to the student’s life and the student should be an active participant in order for learning to be meaningful. Freire also emphasized the emancipatory role of education, arguing that the purpose of education was for learners to gain agency to challenge oppressive systems and improve their lives, and praxis, in which learners put abstract and theoretical knowledge into practice in the real world.

While a student-centered approach and choice can be introduced in any classroom, observers note that in an age of curriculum frameworks and standardized tests, where teachers are often constrained by the material, the ability to provide students with choice and allow for exploration is limited (Sharp, 2012; Zucca-Scott, 2010). Librarians often face similar constraints. School librarians also must meet state and district curriculum standards. Academic librarians generally depend on faculty invitations to conduct instruction and need to adapt their sessions to fit the content, time frame, and learning objectives of the faculty member. Nevertheless, we can always find ways to integrate some self-direction. For instance, rather than using planned examples to demonstrate searches, we might have students suggest topics to search. If we plan hands-on practice activities, we could allow learners to explore their own interests as they engage in the activity, rather than limiting them to preselected topics.

Cognitivism

Cognitivism, or cognitive psychology, was pioneered in the mid-twentieth century by scientists including George Miller, Ulric Neisser, and Noam Chomsky. Whereas behaviorists focus on the external environment and observable behavior, cognitive psychologists are interested in mental processes (Codington-Lacerte, 2018). They assert that behavior and learning entail more than just response to environmental stimuli and require rational thought and active participation in the learning process (Clark, 2018). To cognitivists, learning can be described as “acquiring knowledge and skills and having them readily available from memory so you can make sense of future problems and opportunities” (Brown et al., 2014, p. 2).

Cognitivists view the brain as an information processor somewhat like a computer that functions on algorithms that it develops in order to process information and make decisions. According to cognitive psychology, people acquire and store knowledge, referred to as schema, in their long-term memory. In addition to storing knowledge, people organize their knowledge into categories, and create connections across categories or schema that help them retrieve relevant pieces of information when needed (Clark, 2018). When individuals encounter new information, they process it against their existing knowledge or schema in order to make new connections. Cognitivists are interested in the specific functions that allow the brain to store, recall, and use information, as well as in mental processes such as pattern recognition and categorization, and the circumstances that influence people’s attention (Codington-Lacerte, 2018).

Because cognitivists view memory and recall as the key to learning, they are interested in the processes and conditions that enhance memory and recall. According to cognitive psychology research, traditional methods of study, including rereading texts and drilling practice, or the repetition of terms and concepts, are not effective for committing information to memory (Brown et al., 2014). Rather, cognitivists assert that activities that require learners to recall information from memory, sometimes referred to as “retrieval practice,” lead to better memory and ultimately better learning. For example, they suggest that language learners use flash cards to practice vocabulary words, rather than writing the words out over and over or reading and rereading a list of words, because the flash cards force the learner to recall information from memory.

While testing has fallen out of favor with many educators and education theorists, cognitivists find tests can be beneficial as both a retrieval practice and a diagnostic tool. They view tests not only as a way to measure what has been learned but as a way to practice retrieval of important concepts, and as a way to identify gaps or weaknesses in knowledge so that learners know where to concentrate their efforts (Brown et al., 2014). Cognitivists encourage “spaced practice,” or recalling previously learned information at regular intervals, and “interleaving,” or learning related concepts together to establish connections among them. Their research has found that retrieval is more effective when the brain is forced to recall information after some time has passed, and when the recall involves two or more related subjects or concepts. Finally, cognitivists also promote problem-based learning, maintaining that “trying to solve a problem before being taught the solution leads to better learning, even when errors are made in the attempt” (Brown et al., 2014, p.4).

These processes that enhance memory and recall, and thus learning, have some implications for instructors in creating an optimal environment for learning. Gagné (1985) proposed nine conditions for learning, referred to as the external conditions of learning, or the nine events of instruction:

• Gain attention. Engage students’ attention by tying learning to relevant events in their lives and asking stimulating questions.
• Inform the learner of the objective.  Begin by sharing the learning goals with the students, thus setting expectations and providing a map of the learning.
• Stimulate recall of prior learning.  Encourage students to remember previously learned relevant skills and knowledge before introducing new information.
• Present the stimulus.  Share new information. This step depends on the content of the lesson. For instance, a lesson on Boolean operators might begin with a Venn diagram and examples of the uses of and , or , and not .
• Provide learner guidance.  Facilitate learning by demonstration and explanation.
• Elicit performance.  Allow time for students to practice skills and demonstrate their abilities. Ideally, students would be given low-stakes opportunities for practice, so they feel comfortable if they do not succeed immediately.
• Provide feedback.  Offer students input on what they are doing well and where they can improve.
• Assess performance.  Employ measures such as assignments, activities, and projects to gauge whether learning has occurred.
• Enhance retention and transfer.  Give students opportunities to practice skills in new contexts, which improves retention and helps students see how the skills are applied to different areas.

Cognitivism remains a popular approach to learning. However, one criticism of cognitive psychology is that, unlike humanism, it does not account for the role of emotions in learning (Codington-Lacerte, 2018). Further, some critics believe that cognitivism overemphasizes memorization and recall of facts to the detriment of higher-order skills such as creativity and problem solving. However, cognitivists argue that the ability to recall facts and concepts is essential to higher-order thinking, and therefore the two are not mutually exclusive but actually interdependent (Brown et al., 2014). Finally, cognitivism is considered teacher-centered, rather than learner-centered, since it emphasizes the role of the instructor in organizing learning activities and establishing the conditions of learning (Clark, 2018). Activity 3.2 is a brief exercise on cognitivism.

Activity 3.2: Reflecting on Cognitivism

Cognitive scientists recommend retrieval practice, including spaced practice and interleaving, over drilling.

Questions for Reflection and Discussion:

• What kind of study practices do you tend to use? Do your practices vary depending on the content or material you are studying? How so?
• Can you think of ways to integrate retrieval practices into your work for this class?
• Spaced practice involves returning to previously learned concepts at later times, but information professionals often teach one-shot sessions. Can you think of ways to integrate spaced practice into a one-shot session?

Constructivism

Constructivism posits that individuals create knowledge and meaning through their interactions with the world. Like cognitivism, and as opposed to behaviorism, constructivism acknowledges the role of prior knowledge in learning, believing that individuals interpret what they experience within the framework of what they already know (Kretchmar, 2019a). Social constructs, such as commonly held beliefs, and shared expectations around behavior and values provide a framework for knowledge, but people “do not just receive this knowledge as if they were empty vessels waiting to be filled. Individuals and groups interact with each other, contributing to the common trove of information and beliefs, reaching consensus with others on what they consider is the true nature of identity, knowledge, and reality” (Mercadal, 2018). Cognitivism and constructivism overlap in a number of ways. Both approaches build on the theories of Jean Piaget, who is sometimes referred to as a cognitive constructivist. However, while cognitivism is considered teacher-centered, constructivism centers the learner by recognizing their role in engaging with content and constructing meaning. Constructivist teachers act as guides or coaches, facilitating learning by developing supportive activities and environments, and building on what students already know (Kretchmar, 2019b).

Piaget discusses the concepts of assimilation, accommodation, and disequilibrium to describe how people create knowledge. In his early work as a biologist, Piaget noticed how organisms would adapt to their environment in order to survive. Through such adaptation, the organism achieved equilibrium. Extending these observations to cognitive science, he posited that human beings also seek equilibrium (Kretchmar, 2019a).

When they encounter new situations, or new information, human beings must find a way to deal with the new information. Similar to the processes described in the section on cognitivism, people will examine their existing knowledge, or schema, to see if the new information fits into what they already know. If it does, they are able to assimilate the information relatively easily. However, if the new information does not fit into what people already know, they experience disequilibrium or cognitive conflict, and must adapt by accommodating the new information. For example, once children learn what a dog is, they might call any four-legged creature they see a dog. This is assimilation, as the children are fitting new information into their existing knowledge. However, as children learn the differences between, say, a dog and cat, they can adjust their schema to accommodate this new knowledge (Heick, 2019).

Disequilibrium and accommodation can be uncomfortable. People might be confused or anxious when they encounter information that does not fit their existing schema, and they might struggle to accommodate that new information, but disequilibrium is crucial to learning (Kretchmar, 2019a). During assimilation, people might be adding new bits of information to their knowledge store, but they are not changing their understanding of the world. During accommodation, as people change their schema, construct new knowledge, and draw new connections among existing areas of knowledge, actual learning occurs, and accommodation requires disequilibrium.

Acknowledging the role of disequilibrium is important for both instructors and students. People naturally want to avoid discomfort, but that can also mean avoiding real learning. As instructors, we can facilitate accommodation by acknowledging that the process might be challenging, and by creating conditions that allow students to feel safe exploring new information. We can reassure learners that feelings of discomfort or anxiety are normal and provide them with low-stakes opportunities to engage with new information.

Social Constructivism

Social constructivism builds on the traditions of constructivism and cognitivism; whereas those theories focus on how individuals process information and construct meaning, social constructivists also consider how people’s interactions with others impact their understanding of the world. Social constructivists recognize that different people can have different reactions and develop different understandings from the same events and circumstances, and are interested in how factors such as identity, family, community, and culture help shape those understandings (Mercadal, 2018).While cognitivists and constructivists view other people as mostly incidental to an individual’s learning, social constructivists see community as central. Social constructivism can be defined as “the belief that the meanings attached to experience are socially assembled, depending on the culture in which the child is reared and on the child’s caretakers” (Schaffer, 2006). Like constructivism, social constructivism centers on the learners’ experiences and engagement, and sees the role of the instructor as a facilitator or guide. Two of the major theorists associated with social constructivism are Pierre Bourdieu and Lev Vygotsky.

Vygotsky built on the work of Piaget and believed knowledge is constructed, but felt that prior theories overemphasized the role of the individual in that construction of knowledge. Instead, he “was most interested in the role of other people in the development and learning processes of children,” including how children learn in cooperation with adults and older or more experienced peers who can guide them with more complex concepts (Kretchmar, 2019b). Vygotsky was also interested in how language and learning are related. He postulated that the ways in which people communicate their thoughts and understandings, even when talking themselves through a concept or problem, are a crucial element of learning (Kretchmar, 2019b). For Vygotsky, interaction and dialogue among students, teachers, and peers are key to how learners develop an understanding of the world and of the socially constructed meanings of their communities.

Bourdieu examined the way in which social structures influence people’s values, knowledge, and beliefs, and how these structures often become so ingrained as to be invisible. People within a society become so enculturated into the systems and beliefs of that society that they often accept them as “normal” and do not see them as imposed structures (Roth, 2018). As a result, individuals might not question or challenge those structures, even when they are unfair or oppressive. In addition to examining how community and culture help shape knowledge, Bourdieu was interested in how issues of class impact learning. He observed that over time, schools developed to reflect the cultures of wealthier families, which enabled their children to succeed because they inherently understood the culture of the classroom and the system of education. We continue to see such issues today, and as discussed more in Chapter 5 and Chapter 6, part of our critical practice is to ensure that our classrooms and instructional strategies are inclusive of and responsive to all students.

Activity 3.3 explores how we can use theory to guide our practice.

Activity 3.3: Using Learning Theory to Plan Lessons

While learning theories can be interesting on their own, our goal as instructors is to apply them to classroom practice. Imagine that you are a high school librarian working with a class that has just been assigned a research paper. Your goal for this session is for students to brainstorm keywords and synonyms for their topics, and to learn how to string those words together using the Boolean operators and , or , and not . You want to be sure the students understand the function of the Boolean operators and can remember how to use them for future searches.

Choose one of the learning theories outlined in this chapter and design a brief lesson to teach Boolean operators from the perspective of that theory. Concentrate less on what you would teach but rather on how you would teach it in keeping with the chosen theory:

• How would you introduce the topic?
• What sort of learning activities would you use?
• What would you be doing during the lesson? What would you expect students to do?
• How might any of your answers to these questions change if you were to use a different theory as your guide?

Developmental Stages

The learning theories outlined above discuss various cognitive processes involved in learning, as well as some of the motivators and conditions that facilitate learning. While these theories attempt to describe how people learn, it is important to note that individuals are not born ready to engage in all of these processes at once, nor do they necessarily all engage in the same processes at the same time. Rather, more complex processes develop over time as people experience the world and as their brain matures. In addition to studying how people learn, some theorists have also proposed theories or frameworks to describe developmental stages, or the various points in human development when different cognitive processes are enabled, and different kinds of learning can occur.

Piaget outlined four hierarchical stages of cognitive development: sensorimotor, preoperational, concrete operational, and formal operational (Clouse, 2019), illustrated in Table 3.1. In the sensorimotor stage, from birth to about two years, infants react to their environment with inherent reflexes such as sucking, swallowing, and crying. By about age two, they begin problem solving using trial and error. The preoperational stage, also sometimes called the intuitive intelligence stage, lasts from about ages two to seven. During this time, children develop language and mental imagery. They are able to use their imagination, but they view the world only from their own perspective and have trouble understanding other perspectives. Their understanding of the world during this stage is tied to their perceptions. Children are in the operational stage from about ages seven to 12, during which time they begin to think more logically about the world, can understand that objects are not always as they appear, and begin to understand other people’s perspectives. The final stage, formal operationalism, begins around age 12. At this point, individuals can think abstractly and engage in ideas that move beyond the concrete world around them, and they can use deductive reasoning and think through consequences (Clark, 2018; Clouse, 2019).

Table 3.1: Piaget’s Four Stages of Cognitive Development

Perry’s (1970) Scheme of Intellectual and Moral Development offers another useful framework for understanding the developmental stages of learning. Perry proposed four stages of learning. In the first stage, dualism, children generally believe that all problems can be solved, and that there are right and wrong answers to each question. At this stage, children generally look to instructors to provide them with correct answers. The second stage is multiplicity, where learners realize that there are conflicting views and controversies on topics. Learners in the multiplicity stage often have trouble assessing the authority and credibility of arguments. They tend to believe that all perspectives are equally valid and rely on their own experiences to form opinions and decide what information to trust. In the next stage, referred to as relativism, learners begin to understand that there are different lenses for understanding and evaluating information. They learn that different disciplines have their own methods of research and analysis, and they can begin to apply these perspectives as they evaluate sources and evidence. At this point, learners can understand that not all answers or perspectives are equal, but that some answers or arguments might be more valid than others. In the final stage, commitment, students integrate selected information into their knowledge base. You might notice connections between Perry and the cognitivists and constructivists described above in the way they each describe people making sense of information by comparing new information to existing knowledge. However, Perry organizes the processes into developmental stages that outline a progression of learning.

Understanding the stages laid out by Piaget and Perry, we can develop lessons that are appropriate to learners at each stage. For example, in presenting a lesson on climate change to preoperational students using Piaget’s framework, an instructor could gather pictures of different animal habitats, or take children on a nature walk to observe the surrounding environment. Instructors could ask these children to describe what they see and reflect on their personal experiences with weather, while older children could be asked to imagine how the changes are impacting other people and organisms, anticipate consequences of the impact of climate change, and perhaps use problem solving to propose steps to improve their environment. Considering Perry’s Scheme, instructors might guide students from multiplicity to relativism by explaining scientific methods for measuring climate, and challenging learners to evaluate and compare different sources of information to determine which presents the strongest evidence.

Piaget and Perry offer developmental models that outline stages broadly aligned with a person’s age. Both models assume a relatively linear chronological development, with children and young adults passing through different stages at roughly the same time. Vygotsky, on the other hand, describes a model that focuses more on the content being mastered rather than the age of the student. According to Vygotsky’s theory, known as Zone of Proximal Development (ZPD), as learners acquire new knowledge or develop new skills, they pass through three stages, often illustrated as concentric circles, as in Figure 3.2. The center circle, or first zone, represents tasks that the learner can do on their own. The second zone, or the Zone of Proximal Development, represents an area of knowledge or set of tasks that the learner can accomplish with assistance. The tasks and knowledge in this zone require students to stretch their abilities somewhat beyond their current skill level but are not so challenging as to be completely frustrating. The outermost circle, or third zone, represents tasks that the learner cannot yet do. Vygotsky posits that by working within the ZPD, learners can continue to grow their skills and abilities and increase their knowledge (Flair, 2019).

Figure 3.2: The Zone of Proximal Development

Whereas Piaget and Perry’s theories suggest that learners pass through the same stages at roughly the same time, Vygotsky maintains that the ZPD, or the zone of learning that will appropriately challenge the learner, is different for each student, depending on their background knowledge, experience, and ability (Flair, 2019). The same individual can experience different ZPDs in different subject areas; they might be advanced in math and able to take on material above their grade level but might find languages more challenging. Like with social constructivism, interaction with others is central to ZPD. According to Vygotsky, learning takes place when students interact with others who are more knowledgeable, including peers and instructors, who can provide guidance in the ZPD (Schaffer, 2006).

Math can provide a good example of working within the ZPD. Once students are comfortable with addition, they can probably learn subtraction with some help from a teacher or other peers but are probably not ready to learn long division. Our challenge as instructors is to identify the ZPD for each student so that we are neither boring learners with material that is too easy nor overwhelming them with material that is too hard. Chapter 7 discusses methods for assessing learners’ background knowledge to help determine the appropriate level of learning.

Most of the educational theories and frameworks outlined in this chapter were developed with a focus on children and young adults. While many of the principles can apply to an adult audience, they do not necessarily account for the specific issues, challenges, and motivations of adult learners. Yet, many information professionals will work mostly or even exclusively with adults. Academic librarians and archivists largely work with students who are at least 17 years old and, as the numbers of nontraditional students continue to increase, will find themselves increasingly working with older learners. Likewise, information professionals in corporations and medical and legal settings work almost exclusively with adults. Public librarians see a range of patrons, and many public libraries are increasing educational programming for their adult patrons. This section presents the educational concept of andragogy, which addresses teaching and learning for adults.

Knowles proposed andragogy as “the art and science of helping adults learn” (1988, p. 43). Andragogy is based on a set of assumptions about the ways in which adult learners’ experience, motivations, and needs differ from those of younger students, and suggests that traditional classroom approaches developed with younger students in mind will not necessarily be successful with adult learners. Perhaps one of the biggest differences between child and adult learners, according to Knowles (1988), is that adults are interested in the immediate applicability of what they are learning and are often motivated by their social roles as employees, parents, and so on. As Knowles notes, in traditional classrooms, children are usually taught discrete subjects like math, reading, and history, and their learning is focused on building up knowledge for the future. Young students might not use geometry in their everyday lives, but it forms a foundation for more complex math and for future job or life tasks like measuring materials for home repairs.

Adults, on the other hand, are already immersed in the social roles for which younger students are only preparing, and they want to see how their learning applies to those roles. Thus, Knowles suggests that adults will be interested in a competency-based, rather than a subject-based, approach to learning. Further, as autonomous individuals, adults are likely to be more self-directed in their learning. That is, they will want to, and should be encouraged to, take an active part in the design and planning of lessons, providing input on content and goals. Finally, Knowles also argues that adults’ wider experience and larger store of knowledge should be a resource for learning.

Knowles (1988, p. 45) organized his approach around four assumptions of adult learners:

• Their self-concept moves from one of being a dependent personality toward a self-directed human being.
• They accumulate a growing reservoir of experience that becomes an increasingly rich resource for learning.
• Their readiness to learn becomes oriented increasingly to the developmental tasks of their social roles.
• Their time perspective changes from one of postponed application of knowledge to immediacy of application, and, accordingly, their orientation toward learning shifts from one of subject-centeredness to one of performance-centeredness.

Later, he elaborated with two additional assumptions, summed up by Merriam et al. (2007):

• The most potent motivations are internal rather than external.
• Adults need to know why they need to learn something.

Certain understandings follow from Knowles’ assumptions that we can use to guide our practice with adult learners. To begin with, we should recognize and respect adults’ tendency to be self-motivated and self-directed learners. After all, in most states, school attendance is compulsory up to a certain age, and relatively strict curriculum standards are set by each state, meaning that children have little choice about attending school in some form or about what content they learn. At least in theory, adults have a choice about whether to attend college or engage in other kinds of learning opportunities such as workshops and professional development and continuing education courses. Presumably, adults are motivated to pursue these opportunities for a specific reason, whether out of personal curiosity, to advance in their careers, or to gain a new skill. These adult learners will likely have opinions and ideas about what they want to learn and perhaps even how they want to engage with the content, so Knowles suggests we provide adult learners with choices and opportunities for input to help shape the curriculum.

Adult learners also have a larger store of knowledge and experience than their younger counterparts. From a cognitivist or constructivist point of view, adults have a larger schema against which to compare new information and make new connections. As instructors, we should recognize this store of knowledge and find ways to integrate it into the classroom, by providing ample opportunity for reflection and using guiding questions to encourage learners to draw on that knowledge. We can approach adult learners as peers or co-learners, acting more as coaches or facilitators in the learning process than as the more directive teacher associated with a traditional school classroom. This focus on learner-centered approaches and a democratic environment overlaps with humanistic and constructivist approaches to teaching.

Points three, four, and six in Knowles’ list of assumptions underscore the importance of relevance and transparency for adult learners. Knowles suggests that adults have different priorities in learning, perhaps in part because they are learning by choice and are in a better position to direct their own learning. Adult learners also tend to have more demands on their time than younger students; they may have families and jobs that impact the time they have to devote to their studies. Thus, adult learners want to see the applicability of what they are learning and might be resistant to work or information that seems incidental. We should be transparent with our adult students, both about what they will learn and how that learning is important and relevant. Sharing learning goals is an important step toward transparency, as it can help set expectations so that students understand the purpose of the lesson and activities. To illustrate relevance, we can provide concrete examples of how the learning can be applied in practice. One could argue that all students, not just adults, deserve transparency and to see the relevance of lesson goals and learning. Knowles’ point is that adults are more likely to expect, and perhaps appreciate, such transparency.

While some controversy exists over whether andragogy really constitutes a theory per se or is more a set of guiding principles or best practices, the assumptions provide helpful guidance to instructors not just in how they organize content but also in how they frame the lesson and its purposes. Based on these assumptions, we can take certain steps to set an appropriate environment for adult education (Bartle, 2019):

• Set a cooperative learning climate.
• Create mechanisms for input.
• Arrange for a diagnosis of learner needs and interests.
• Enable the formulation of learning objectives based on the diagnosed needs and interests.
• Design sequential activities for achieving the objectives.
• Execute the design by selecting methods, materials, and resources.
• Evaluate the quality of the learning experience while rediagnosing needs for further learning.

As noted above, andragogy overlaps with other theories such as humanism and constructivism, and some of the principles of andragogy, like transparency, would benefit all learners. Still, this framework is useful in reminding instructors that adult learners likely have different priorities and motivations, and thus some differences in classroom approach might be warranted.

In addition to how people learn, we should also know something about why people learn. What motivates a student to put the time and effort into learning a skill or topic, and what can we do to cultivate that motivation? Svinicki (2004) offers an intriguing model that amalgamates some of the prevailing theories of motivation in learning. She suggests that motivation is a factor of the perceived value of the learning, along with students’ belief in their own self-efficacy, or their belief in their ability to achieve the goal. As Svinicki explains, “motivation involves a constant balancing of these two factors of value and expectations for success” (2004, p. 146). Most of the learning theories outlined above address motivation implicitly or explicitly. For instance, behaviorists talk in terms of reinforcement, or external motivators, as students strive to avoid negative consequences and achieve the rewards of good work. Humanists, on the other hand, focus on the internal motivation of self-actualization. As instructors, we can create environments to increase our learners’ motivation or their perception of the value of the goal and their self-efficacy:

• Emphasize the relevance of the material.  As outlined in the section on andragogy, learners are motivated when they see the benefits of learning and understand why the material is important. Instructors should explain how the effort individuals put into learning can help them achieve personal goals, such as getting a good grade on a paper or finding a job.
• Make the material appropriately challenging.  Reminiscent of the Zone of Proximal Development, material that is too easy will be boring for learners, while material that is too challenging will be overwhelming and frustrating.
• Give learners a sense of choice and control.  Choice allows learners to have a stake in the class, while control helps them determine the level of risk they will take and thus increase their confidence. We can foster choice and control by allowing learners options in the types of activities and assignments they engage in, or in the topics they research.
• Set learners up for success. Clear expectations for the class or the assignment help learners understand what a successful performance or project looks like. By providing meaningful feedback, we can guide learners toward success.
• Guide self-assessment.  When learners accurately assess their current level of knowledge and skill, they can make reasonable predictions of the likelihood of their success with the current material.

Activity 3.4 offers an opportunity to reflect on motivation in learning.

Activity 3.4: What Motivates You?

Think back on learning experiences such as courses or workshops where you felt more or less motivated as a learner. These experiences could be related to academics, hobbies, sports, or other interests.

• In the experiences in which you felt motivated, what steps did the instructor take that helped you feel motivated?
• In the experiences where you felt less motivated, what could the instructor have done differently?
• In each case, what role did self-efficacy, or your confidence in your own abilities, play?

Growth Mindset

Dweck’s (2016) mindset theory has gained much attention in the field of education over the last few decades and has some implications for student motivation. Although this theory is somewhat different in its conceptualizations than those described in the rest of this chapter, it is included here both because of its popularity and because it provides interesting insight into how instructors can coach learners to understand and build on their potential. Dweck’s theory is less about how people learn and more about how their attitude toward learning and their self-concept can impact their ability and willingness to learn. According to Dweck, people tend to approach learning with a fixed mindset or a growth mindset. Those with more of a fixed mindset tend to believe that ability is innate; either people are born with a certain talent and ability, or they are not. If individuals are not born with natural ability in a certain area, they would waste time working on that area because they will never truly be successful. People with more of a growth mindset, on the other hand, tend to believe that ability is the outcome of hard work and effort. These people see value in working at areas in which they are not immediately successful because they believe they can improve. Even when they are good at something, they are willing to continue to work at it because they believe they can continue to get better (Dweck, 2016).

These mindsets can have a profound impact on how a person approaches learning (Dweck, 2016). People with a fixed mindset will view low grades or poor test performance as a sign of their lack of natural ability and are likely to become discouraged. They might try to avoid that subject altogether or resign themselves to failure because they do not believe that practice or study will help them improve. Instead, they will tend to stick to subjects in which they already perform well. People with a growth mindset take an opposite view. They tend to view low grades or poor performance as a diagnostic tool that helps them see where they need to concentrate their efforts in order to get better. They are willing to put in extra effort because they believe that their hard work will lead to improved performance. They are also willing to take risks because they understand that failure is just part of the process of learning. We can see connections between Dweck’s theory and Piaget’s argument that the discomfort of disequilibrium is necessary to learning.

Understandably, people with a growth mindset are usually more successful learners because they believe in their own ability to learn and grow. Luckily, Dweck maintains that these mindsets themselves are not necessarily immutable. That is, a person with a fixed mindset can be coached to adopt a growth mindset. Learners can begin by recognizing when they are engaging in fixed mindset thinking, for instance when getting anxious about mistakes or telling themselves that they are “no good” at something. Once learners understand that this thinking is counterproductive, they can change their thinking to adopt a more encouraging voice.

Importantly, Dweck notes that encouraging a growth mindset in the classroom does not mean lowering standards for learning. She maintains that instructors should have high standards but also create a supportive and nurturing atmosphere. To begin with, instructors themselves must believe that learning and growth are possible, and not give up on students who are struggling. Instructors can model this belief for students by replacing fixed mindset feedback with growth mindset feedback. For example, Dweck suggests that if learners are struggling, instructors can respond by telling them they have not succeeded yet. The word “yet” implies that they will achieve the necessary learning; they just need to keep working at it. In that way, instructors can reframe mistakes and struggles as opportunities to learn rather than as failures. Instructors should encourage and appreciate effort as well as learning. In other words, rather than focusing only on a student’s achievement, instructors can praise the effort and hard work that led to that achievement. At the same time, Dweck (2015) notes that a growth mindset is not just about effort. In addition to putting in the work, learners must also be willing to try different strategies and be open to feedback on their performance. The goal is to help students view challenges as part of the learning process and to work with them rather than to fear or avoid them.

Learning theories are meant to help instructors understand the processes and circumstances that enable learning and, by extension, offer guidance in developing activities and environments that best support learning. But what to make of the fact that there are so many different theories and that some contradict each other? The truth is that the human brain and its cognitive processes are incredibly complex and not yet fully understood. Learning theorists do their best to describe how people learn based on careful observation and experimentation, but no learning theory is perfect. Indeed, each theory has its critics, and the various theories go in and out of favor over time. Even so, the theories provide us with an empirically based understanding of how learning occurs.

Further, these theories are not mutually exclusive. We do not have to strictly adhere to one theory but can combine elements across theories in ways that resonate with our teaching styles and reflect our best understanding of our students. For instance, a teacher might draw on elements of cognitivism to enhance students’ retention and recall but also develop group activities that promote social constructivism through peer-to-peer communication. Especially with younger children, instructors might draw on behaviorism by using rewards and positive reinforcement to motivate student engagement with the content, but also integrate humanism by empathizing with students and use constructive feedback to encourage a growth mindset. We can use our understanding of developmental stages to create lessons and activities that provide an appropriate level of challenge to help students grow in their understanding. Ultimately, we should view learning theories as guidelines, not rules, and draw on them in ways that reflect our own values and understandings.

Keeping this idea of learning across theories in mind, we can sum up the key takeaways from this chapter:

• Learning is the change in knowledge, behavior, or understanding that occurs when people make connections between new information and their existing knowledge. Various theories attempt to describe the factors that enable the learning process.
• Learning does not happen in the same way or at the same time for all students. Understanding developmental stages can help instructors align instruction with student readiness. Adult learners may have needs and constraints that differ from younger learners.
• The learning process is influenced by internal factors such as the student’s level of motivation and feelings of self-efficacy, and external factors such as the classroom environment and the adults and peers with whom the learner interacts.
• Creating a democratic, empathetic, and supportive learning environment
• Assisting students in becoming self-directed learners and enhancing their motivation by offering a sense of control and choice in their learning
• Acknowledging that learning can be challenging, and helping students develop the mindset and self-efficacy that will support their persistence
• Offering regular and meaningful feedback

Suggested Readings

Brown, P. C., Roediger, H. L. III, & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Belknap Press.

Brown, Roediger, and McDaniel present an engaging and accessible overview of current research in cognitive psychology. In addition to the science, the authors offer clear examples of how recommended recall and retrieval practices can be integrated into teaching.

Cooke, N. A. (2010). Becoming an andragogical librarian: Using library instruction as a tool to combat library anxiety and empower adult learners. New Review of Academic Librarianship, 16 (2), 208-227. https://doi.org/10.1080/13614533.2010.507388

This article offers a thorough overview of andragogy and the characteristics and motivators of adult learners and offers library-specific advice for teaching adult students.

Curtis, J. A. (2019). Teaching adult learners: A guide for public librarians . Libraries Unlimited.

Curtis provides a clear introduction to andragogy to contextualize instruction in public libraries. She also addresses issues of culture and generational differences in teaching adults. Covering many aspects of instruction, including developing learning objects and teaching online, this book is valuable as one of the few to focus exclusively on issues of teaching and learning in public libraries.

Dweck, C. S. (2016). Mindset: The new psychology of success (Updated ed.). Penguin Random House.

In this book, Dweck defines fixed and growth mindsets and how they can influence people’s feelings of motivation and self-efficacy in learning. She also offers guidance on how to facilitate the development of a growth mindset for better learning.

Freire, P. (2000). Pedagogy of the oppressed (30th Anniversary Edition). Bloomsbury.

In this foundational work, Freire presents the concept of the banking model of education. This book provides a social justice foundation for a humanistic approach to education.

Merriam, S. B., & Bierema, L. L. (2014).  Adult learning: Linking theory and practice . Jossey-Bass.

The authors provide a clear, concise, and engaging overview of both traditional and current theories of adult learning. The book includes activities and concrete examples for implementing the theories in the classroom.

Roy, L., & Novotny, E. (2000). How do we learn? Contributions of learning theory to reference services and library instruction. Reference Librarian, 33 (69/70), 129-139. https://doi.org/10.1300/J120v33n69_13

The authors provide an overview of some of the major learning theories, followed by specific ideas and advice for applying the theory to reference and library instruction.

Svinicki, M. D. (2004). Learning and motivation in the postsecondary classroom . Bolton, MA: Anker Publishing.

This book takes a student-centered approach to describing learning theory. Chapter 7 provides an excellent overview of motivation and self-efficacy, including implications for practice.

Bartle, S. M. (2019). Andragogy. In Salem press encyclopedia . EBSCO.

Brown, P. C., Roediger, H. L. III, & McDaniel, M.A. (2014). Make it stick: The science of successful learning . Belknap Press.

Clark, K. R. (2018). Learning theories: Cognitivism. Radiologic Technology, 90 (2), 176-179.

Clouse, B. (2019). Jean Piaget. In Salem press biographical encyclopedia . EBSCO.

Codington-Lacerte, C. (2018). Cognitivism. Salem press encyclopedia . EBSCO.

Dweck, C. S. (2015, September 22). Carol Dweck revisits the “growth mindset.” Education Week, 35 (5), 20-24. https://www.edweek.org/ew/articles/2015/09/23/carol-dweck-revisits-the-growth-mindset.html

Flair, I. (2019). Zone of proximal development (ZPD). Salem press encyclopedia . EBSCO

Gagné, R. M. (1985). The conditions of learning and theory of instruction . Wadsworth Publishing.

Heick, T. (2019, October 28). The assimilation vs accommodation of knowledge. teachthought . https://teachthought.com/learning/assimilation-vs-accommodation-of-knowledge/

Jensen, R. (2018). Behaviorism. Salem press encyclopedia of health . EBSCO.

Knowles, M. S. (1988). The modern practice of adult education: From pedagogy to andragogy. Revised and updated . Cambridge, The Adult Education Company.

Kretchmar, J. (2019a). Constructivism. Salem press encyclopedia . EBSCO.

Kretchmar, J. (2019b). Gagné’s conditions of learning. Salem press encyclopedia . EBSCO.

Kuhlthau, C. C. (1990). The information search process: From theory to practice. Journal of Education for Library and Information Science, 31 (1), 72-75. https://doi.org/10.2307/40323730

Lucas, C. J. (1996). Humanism. In J. J. Chambliss (Ed.),  Philosophy of education: An encyclopedia . Routledge.

Madsen, S. R., & Wilson, I. K. (2012). Humanistic theory of learning: Maslow. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning . Springer.

Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50 (4), 370-396.

McLeod, S. A. (2015). Cognitive approach in psychology . Simply Psychology . http://www.simplypsychology.org/cognitive.html

Mellon, C. A. (1986). Library anxiety: A grounded theory and its development. College & Research Libraries, 47 (2), 160-165. https://doi.org/10.5860/crl.76.3.276

Mercadal, T. (2018). Social constructivism. Salem press encyclopedia . EBSCO.

Merriam, S. B., Caffarella, R. S., & Baumgartner, L. M. (2007). Learning in adulthood: A comprehensive guide (3rd edition) . Wiley.

Perry, W. G., Jr. (1970). Forms of intellectual and ethical development in the college years; A scheme. Holt.

Popp, J. A. (1996). Learning, theories of. In J. J. Chambliss (Ed.),  Philosophy of education: An encyclopedia . Routledge.

Roth, A. L. (2018). Pierre Bourdieu. Salem press biographical encyclopedia . EBSCO.

Shaffer, R. H. (2006). Key concepts in developmental psychology . Sage UK.

Sharp, A. (2012). Humanistic approaches to learning. In N.M. Seel (Ed.), Encyclopedia of the Sciences of Learning . Springer.

Skinner, B. F. (1938).  The Behavior of organisms: An experimental analysis . Appleton-Century.

Svinicki, M. D. (2004). Learning and motivation in the postsecondary classroom . Anker Publishing.

Zucca-Scott, L. (2010). Know thyself: The importance of humanism in education. International Education, 40 (1), 32-38.

Instruction in Libraries and Information Centers Copyright © 2020 by Laura Saunders and Melissa A. Wong is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Differentiated Instruction Strategies: Tiered Assignments

Janelle cox.

• September 23, 2014

Many teachers use differentiated instruction strategies  as a way to reach all learners and accommodate each student’s learning style. One very helpful tactic to employ differentiated instruction is called tiered assignments—a technique often used within flexible groups.

Much like flexible grouping—or differentiated instruction as a whole, really—tiered assignments do not lock students into ability boxes. Instead, particular student clusters are assigned specific tasks within each group according to their readiness and comprehension without making them feel completely compartmentalized away from peers at different achievement levels.

There are six main ways to structure tiered assignments: challenge level, complexity, outcome, process, product, or resources. It is your job, based upon the specific learning tasks you’re focused on, to determine the best approach. Here we will take a brief look at these techniques.

Ways to Structure Tiered Assignments

Challenge level.

Tiering can be based on challenge level where student groups will tackle different assignments. Teachers can use Bloom’s Taxonomy as a guide to help them develop tasks of structure or questions at various levels. For example:

• Group 1:  Students who need content reinforcement or practice will complete one activity that helps  build  understanding.
• Group 2:  Students who have a firm understanding will complete another activity that  extends  what they already know.

When you tier assignments by complexity, you are addressing the needs of students who are at different levels using the same assignment. The trick here is to vary the focus of the assignment based upon whether each group is ready for more advanced work or simply trying to wrap their head around the concept for the first time. You can direct your students to create a poster on a specific issue—recycling and environmental care, for instance—but one group will focus on a singular perspective, while the other will consider several points of view and present an argument for or against each angle.

Tiering assignments by differentiated outcome is vaguely similar to complexity—all of your students will use the same materials, but depending on their readiness levels will actually have a different outcome. It may sound strange at first, but this strategy is quite beneficial to help advanced students work on more progressive applications of their student learning.

This differentiated instruction strategy is exactly what it sounds like—student groups will use different processes to achieve similar outcomes based upon readiness.

Tiered assignments can also be differentiated based on product. Teachers can use the Howard Gardner’s multiple intelligences to form groups that will hone particular skills for particular learning styles . For example, one group would be bodily/kinesthetic, and their task is to create and act out a skit. Another group would be visual/spatial, and their task would be to illustrate.

Tiering resources means that you are matching project materials to student groups based on readiness or instructional need. One flexible group may use a magazine while another may use a traditional textbook. As a tip, you should assign resources based on knowledge and readiness, but also consider the group’s reading level and comprehension.

How to Make Tiering Invisible to Students

From time to time, students may question why they are working on different assignments, using varied materials, or coming to dissimilar outcomes altogether. This could be a blow to your classroom morale if you’re not tactful in making your tiers invisible.

Make it a point to tell students that each group is using different materials or completing different activities so they can share what they learned with the class. Be neutral when grouping students, use numbers or colors for group names, and be equally enthusiastic while explaining assignments to each cluster.

Also, it’s important to make each tiered assignment equally interesting, engaging, and fair in terms of student expectations. The more flexible groups and materials you use, the more students will accept that this is the norm.

Tiering assignments is a fair way to differentiate learning. It allows teachers to meet the needs of all students while using varying levels of tasks. It’s a concept that can be infused into homework assignments, small groups, or even learning centers. If done properly, it can be a very effective method to differentiate learning because it challenges all students.

• #DifferentiatedInstruction , #TieredAssignments

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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:

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

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Basics of Linear Programming

Linear Programming Problem (LPP) Formulation with Numericals

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Education Lessons

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How to Use the 5E Model in Your Science Classroom

An inquiry-focused method gives students a way to connect scientific ideas to their experiences and apply their learning.

Try to recall the last science class you taught. It probably had many components over the course of 50 minutes, such as a video, a mini-lecture, an assessment, discussion, or a demonstration. What were all the components? Did your students discuss a question in small groups? Was it a whole-class discussion? Did you introduce a new concept? In what order did you introduce these components? How did you decide what came first? What is the optimal order of activities in an inquiry-based science classroom?

Engage Students’ Curiosity

Teaching science using an inquiry approach requires instructional planning that encourages students to engage their curiosity to ask questions, explore solutions to socio-scientific issues, use evidence-based explanations to justify their reasoning, elaborate on possible effects, evaluate their findings, and predict potential outcomes based on different variables. In inquiry science, students are cognitively challenged as they engage in authentic problems while learning content, practicing reasoning skills, and communicating their ideas.

One approach to inquiry science is the 5E instructional model (Engage, Explore, Explain, Elaborate, Evaluate). The 5E model is a planning tool for inquiry teaching that provides a structure for students to connect science ideas with their experiences and apply their learning to new contexts. The 5E model comprises five phases that help teachers build a sequence of coherent and engaging learning experiences for students.

How the 5E Model Works

1. Engage. The teacher uses short activities to promote curiosity. The activity must connect prior knowledge to new learning experiences in order to expose any misconceptions and prepare students for new learning.

Novel questions, discrepant events, demonstrations, or a powerful visual are ideal ways to engage students and ascertain their prior knowledge or any misconceptions that might interfere with constructing new knowledge. For new concepts to become meaningful, students use their prior knowledge to connect their past and present learning experiences.The engagement phase doesn’t have to be part of the class time. It can be structured as a homework assignment where students can read an article related to the new topic to be introduced, explore a website, watch a video, or answer a question related to their prior knowledge.

For example: Why are acidic drinks stored in a cold place?

2. Explore. A lab investigation or hands-on activities are usually introduced in this phase as students attempt to investigate a problem. Conflicting ideas, questions, and confusion are common and help students identify what they need to know before new terms or concepts are introduced in the Explain phase.

Encourage students to think of the following questions:

• What is the problem I am trying to solve?
• What do I need to find out?
• What do I know already?

Students are provided with two identical soda cans, a bottle opener, hot water, and an ice bath. Students perform the activity in pairs or groups, write down their observations, and discuss their results in the group.

For example: Which soda, the warm or the cold one, had more dissolved carbon dioxide? List all the ways that you know.

3. Explain. With the teacher’s guidance, students explain the concepts they explored in the previous phase and demonstrate their understanding of the new terms that were introduced. Depending on the topic and the grade level, teacher-led instruction might be necessary to address any confusion and questions that came up in the Explore phase. Questions can make learning more meaningful, interactive, and participatory.

Students provide a tentative explanation as they present their results to the whole class. The teacher may pose additional questions to support the discussion. It is recommended that the teacher introduce a formative assessment in this phase to identify if the students are ready to start the Elaborate phase and determine if additional instruction or assistance is needed.

For example: What is the best way to store an opened bottle of soda so that it doesn’t go flat quickly?

4. Elaborate. Students apply their knowledge to new experiences and extend their conceptual understanding as they solve a problem in a new context before evaluation in the last phase of the 5E model. Elaboration activities can take place during classroom time, or they can be a homework assignment.

For example: Decompression sickness (DCS) occurs when divers swim to the surface too quickly (rapid ascent). What causes DCS to occur?

5. Evaluate. Students evaluate their learning and demonstrate their understanding and mastery of key concepts. Evaluation doesn’t have to be limited to a quiz or test. It can be a product such as a presentation, a poster, a pamphlet, a journal article, or a final paper.

For example: In your opinion, why do fish wash ashore on hot summer days?

Many power plants condense steam by pumping cool river or lake water around the steam pipes. The steam cools and condenses as its heat is transferred to the water, which is then returned to the river or lake. What impact does this warm water have on the fish in the lake or river?

You may already be using these elements without being familiar with the 5E model’s formal structure. Consider the phases discussed above and reflect on the activities you planned for the last science lesson you taught. Which component would you classify as an Engage activity? Explore? Explain? How well does the order you have planned reflect the 5E model?

The goal isn’t to plan every science lesson according to the 5E model—it’s to consider the order and sequence of activities to align with the model to maximize student learning.

Here are some final suggestions:

Start small. You might design a lesson with only two components of the 5E model. If your lesson already has a hands-on activity, you may want to start with Engage to get the students thinking about the hands-on activity they’re going to complete in the Explore phase. A simple 3- to 5-minute activity such as a current events story, a video, an advertisement, a problem scenario, or a challenge statement based on a common misconception can engage students.

Explore before Explain. Lab investigations or hands-on activities in the Explore phase, no matter how simple, may be time-consuming. Yet, you can allow at least some exploration before explanation to prepare students to receive new information. They might attempt to solve a problem, make a prediction about an experiment or demonstration, or answer a complex question. Consider starting instruction (Explain) in the middle of the class session, as opposed to the beginning, after students have had time to do some exploration.

Copyright © 2003 by Robert Fourer, David M. Gay and Brian W. Kernighan

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Learning Styles: Dunn and Dunn Model

From learning and training wiki.

Bibliography

• ↑ Dunn, R., Dunn K., & Price, G.E. (1985). Learning Styles Inventory (LSI): An Inventory for the Identification of How Individuals in Grades 3 through 12 Prefer to Learn. Lawrence, KS: Price Systems.
• ↑ Dunn, Rita, & Honigsfeld. (2009). Differentiating Instruction for At-Risk Students: What to Do and How to Do It. Lanham, Maryland: Rowman & Littlefield.
• ↑ Dunn, Rita, & Griggs, Shirley A. (Eds.). (2000). Practical Approaches to Using Learning Styles in Higher Education. Connecticut: Bergin & Garvey.
• ↑ Dunn, R. (2000). Learning styles: Theory, research, and practice. National Forum of Applied Educational Research Journal, 13, (1), 3-22.
• ↑ Dunn, R., & Griggs, S. (1998). Learning styles: Link between teaching and learning. In Dunn, R. & Griggs, S. (Eds.), Learning styles and the nursing profession (pp. 11-23). New York: NLN Press.
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Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

• First, prefix name of what this variable represents.
• Second is the list of all the variables.
• Third is the lower bound on this variable.
• Fourth variable is the upper bound.
• Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

• Solving Blending Problem in Python using Gurobi
• Transshipment Problem in Python Using PuLP

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sklearn.linear_model .LinearRegression ¶

Ordinary least squares Linear Regression.

LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

If True, X will be copied; else, it may be overwritten.

The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly n_targets > 1 and secondly X is sparse or if positive is set to True . None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

When set to True , forces the coefficients to be positive. This option is only supported for dense arrays.

New in version 0.24.

Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.

Rank of matrix X . Only available when X is dense.

Singular values of X . Only available when X is dense.

Independent term in the linear model. Set to 0.0 if fit_intercept = False .

Number of features seen during fit .

Names of features seen during fit . Defined only when X has feature names that are all strings.

New in version 1.0.

Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization.

The Lasso is a linear model that estimates sparse coefficients with l1 regularization.

Elastic-Net is a linear regression model trained with both l1 and l2 -norm regularization of the coefficients.

From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares (scipy.optimize.nnls) wrapped as a predictor object.

Fit linear model.

Training data.

Target values. Will be cast to X’s dtype if necessary.

Individual weights for each sample.

New in version 0.17: parameter sample_weight support to LinearRegression.

Fitted Estimator.

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

A MetadataRequest encapsulating routing information.

Get parameters for this estimator.

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Parameter names mapped to their values.

Predict using the linear model.

Returns predicted values.

Return the coefficient of determination of the prediction.

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Deep generative clustering methods based on disentangled representations and augmented data

• Original Article
• Published: 28 April 2024

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• Kunxiong Xu 1 ,
• Wentao Fan   ORCID: orcid.org/0000-0001-6694-7289 2 &
• Xin Liu 1  

This paper presents a novel clustering approach that utilizes variational autoencoders (VAEs) with disentangled representations, enhancing the efficiency and effectiveness of clustering. Traditional VAE-based clustering models often conflate generative and clustering information, leading to suboptimal clustering performance. To overcome this, our model distinctly separates latent representations into two modules: one for clustering and another for generation. This separation significantly improves clustering performance. Additionally, we employ augmented data to maximize mutual information between cluster assignment variables and the optimized latent variables. This strategy not only enhances clustering effectiveness but also allows the construction of latent variables that synergistically combine clustering information from original data with generative information from augmented data. Through extensive experiments, our model demonstrates superior clustering performance without the need for pre-training, outperforming existing deep generative clustering models. Moreover, it achieves state-of-the-art clustering accuracy on certain datasets, surpassing models that require pre-training.

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Data Availability

The MNIST data sets is available at: [ http://yann.lecun.com/exdb/mnist/ ]. The USPS data set is available at: [ https://www.kaggle.com/datasets/bistaumanga/usps-dataset ]. The GTSRB data set is available at: [ https://benchmark.ini.rub.de/gtsrb_news.html ] The YTF data set is available at: [ https://www.cs.tau.ac.il/~wolf/ytfaces/ ] The F-MNIST data set is available at: [ https://www.kaggle.com/datasets/zalando-research/fashionmnist ].

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Acknowledgements

The completion of this work was supported by the National Natural Science Foundation of China (62276106), the Guangdong Provincial Key Laboratory IRADS (2022B1212010006, R0400001-22) and the UIC Start-up Research Fund (UICR0700056-23).

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Xu, K., Fan, W. & Liu, X. Deep generative clustering methods based on disentangled representations and augmented data. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02173-9

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Researchers from Google DeepMind and Google Research have introduced the Gecko framework, designed to significantly refine the evaluation process of T2I models. Unique to Gecko is its use of a QA-based auto-evaluation metric, which correlates more accurately with human judgments than prior metrics. This approach allows for a nuanced assessment of how well images align with textual prompts, making it possible to identify specific areas where models excel or fail.

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Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.

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Title: few-shot calligraphy style learning.

Abstract: We introduced "Presidifussion," a novel approach to learning and replicating the unique style of calligraphy of President Xu, using a pretrained diffusion model adapted through a two-stage training process. Initially, our model is pretrained on a diverse dataset containing works from various calligraphers. This is followed by fine-tuning on a smaller, specialized dataset of President Xu's calligraphy, comprising just under 200 images. Our method introduces innovative techniques of font image conditioning and stroke information conditioning, enabling the model to capture the intricate structural elements of Chinese characters. The effectiveness of our approach is demonstrated through a comparison with traditional methods like zi2zi and CalliGAN, with our model achieving comparable performance using significantly smaller datasets and reduced computational resources. This work not only presents a breakthrough in the digital preservation of calligraphic art but also sets a new standard for data-efficient generative modeling in the domain of cultural heritage digitization.

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