philippine science high school research projects

PSHS System Science Research Summit 2019

Congratulations to the winners of dost-pshs science research summit 2019.

CAPPING OFF THIS YEAR’S SCIENCE RESEARCH SUMMIT, the PSHS System recognises the hard work and diligence of the scholars in conducting their researches. After a meticulous deliberation among judges, the winners were concluded and announced on August 30, after the community fair.

3rd Place – Identification of candidate transcriptomic markers for the prognostication of Leptospirosis related complications (Feliz, Reneez Aiyana G.; Lipa, Aubrey Mae D – Main Campus)

2nd place – Genotyping of PTRVs for potential sources of Novel Tungro Resistance Genes (Medilla, Djoey Bianca C. – Main Campus)

1st Place – Diversity of Mangrove Foliar Endophytic Fungi from Matalom, Leyte (Costillas, Gwyneth Vera M.; Hillario, Tricia May T.; Mascariñas, Rian Lorenzo V. – Eastern Visayas Campus)

3rd Place – Tinospora crispa Leaf powder and Jatropha curcas Wood Ash against Sitophilus oryzae (Chio, Reigna Maxine W.; Gulles, Jane M.; Illustrisimo, Chrizha C.; Gabais, Cyrene B. – SOCCSKSARGEN Region Campus)

2nd Place – Polyaniline-based Putrescine Sensor through Digital Imaging Colorimetry (Mejares, Sadao Jr.; Paguio, Nicky Mariele M.; Roque, Joseph Ronald S. – Central Luzon Campus)

1st Place – Alternative Pharmaceutical Liquid Preservative from Tamarindus indica (Tamarind) Pulp Extracts (Aglugub, Marian Maxine W.; Padillo, Anne Felize P.; Villena, Julianne Jesh D. – Southern Mindanao Campus)

COMPUTATIONAL SCIENCE

3rd Place – Portable Arduino-based Integrated Water Quality Analyzer with Real-time Data Transmitter (Alave, Estelle Nicole L.; Lim, Harvey Andrew G.; Ronquillo, Jericho L,; Tugade, Marjun M. – SOCCSKSARGEN Region Campus)

2nd Place – Development of an iOS Application for Real-Time Spectral Classification of Stars Based on a Convolutional Neural Network (De los Santos, Mark John Paul M.; Evangelista , Paul Emmanuel D.; Ordonio, Juan Samuel A. – Main Campus)

1st Place – Machine-Learning Based Rainfall-induced Landslide Prediction System for Benguet First Engineering District (Tuguinay, Nica Magdalena A.; Omadiao, Zanya Reubenne D.; Saturay, Ricardo Jr. M – PSHS-Cordillera Administrative Region Campus)

ENGINEERING

3rd Place – Development of Cacao Seed Depulper (Arcilla, Jed C.; Imperial, Jerome C.; Penetrante, Joben Peter R. – Bicol Region Campus)

3rd Place – Project WaFLE: Development of an Arduino-based Water Flow and Level Encoder (Gonzaga, Hans; Adel, Jhoe; Kempis, Joshua – Eastern Visayas Campus)

2nd Place – Project LINIS: onLine waste management Integration of a Network of Interconnected Systems (Osorio, Justin Benedict R. – Central Visayas Campus)

1st Place – Development of a Mahogany (Swietenia macrophylla) Seed-Inspired Horizontal-axis Wind Turbine (Gonzales, Kaira Maxine V.; Javier, Timothy L.; Marquez, John Henry T.; Tuazon, Alejandro Jose A. – Main Campus)

ENVIRONMENTAL SCIENCE 3rd Place – Macroinvertebrate Assessment and Stable Isotope Investigation of Mussels from Lake Danao, Ormoc (Elias, Remy Annen T.; Llave, Ara Marie C.; Rentuaya, Elejune Ace B. – Eastern Visayas Campus)

3rd Place – Tree Diversity, Aboveground Biomass, and Carbon Analysis in the Mangrove Forest of Brgy. Luna, San Jose, Dinagat Islands (Ajoc, Ronnie Rey D.; Asotigue, Carl Marlo D.; Epanes, Elejune Ace B. – CARAGA Region Campus)

3rd Place – Chlorella vulgaris Chlorophyll a Fluorescence as a Potential Biomaker for Zinc Detection (Cabanlig, Lydia Vittoria T.; Gepte, Bianca Victoria P.; Taguba, Mira Nicole J. – Main Campus)

2nd Place – Human Population Density Effects on the Variability of δ 15N and δ 13C of Seagrasses in Tacloban City, Philippines (Tomacas, Leila L.; Boonrayat, Vic Dam S.; Garay, Ysabela Denise P. – Eastern Visayas Campus)

1st Place – Composition, Abundance and Diversity of Zooplankton in Sarangani Bay, Sarangani Province (Cañete, Jeter Q.; Estrella, Noreen Joyce M.; Madelo, Yedda Sachi Patrice B.; Requeiron, Elani A. – SOCCSKSARGEN Region Campus)

MATERIALS SCIENCE

3rd Place – Carbon-modified Photocatalytic Porous Concrete for Water Purification (Baluyo, Jeremy A.; Sumpay, John Mark Nouwen S.; Villarin, Princess Willen F. – Bicol Region Campus)

2nd Place – Characterization of (Solanun tuberosum) Potato – Red Cabbage (Brassica oleracea var. Capitata F. rubra) Composite Film for Intelligent Packaging Applications (Aquino, Mikaela Andrea Paula; Aquino, Patricia Nicole; Saljay, Erica Nicole – Southern Mindanao Campus)

1st Place – Feasibility of Circulating Fluidized Bed Coal Bottom Ash as a Clay Soil Conditioner (Diola, Bertrand Michael L.; Pre, Sophia Francesca B.; Ricaforte, Caithleen Anne U. – Main Campus)

DOST Pisay scholars banner innovative researches in health and environment-friendly packaging

Press Release-Pisay-SRS-2019-03

SUBIC BAY , Zambales – The importance of finding solutions to pressing health problems like dengue and cancer were the inspiration of most of the students from the Philippine Science High School who attended the 3 rd Science Research Summit from 28-30 August 2019 held in Subic, Zambales. 

This year’s summit that carries the theme, “Breaking Gender Barriers through Science, Technology, and Innovation” is an annual event for the country’s top scholars to present their research papers on various fields like Biology, Chemistry, Computational Science, Material Science, Engineering, and Environmental Science.

During the technical poster presentation last 29 August, subject matter experts and judges were given the opportunity to ask further questions and it also served as an exhibit of innovative ideas of the students from different Pisay campuses all over the country.

Philippine Science High School-Cordillera Administrative Region Campus Grade 12 students Bryne Benedict C. Fawayan, Enna Gabrielle Marie A. Joven, and Patricia Bea E. Palaroan, all residing in Baguio City, thought of doing a research using one of the most abundant trees in the summer capital; the pine tree.

The title of their research project was, “The Efficiency of Pinus kesiya Needles in repelling Aedes aegypti Mosquitoes”. Their theory revolved on the idea that there were substances or properties in pine needles that can be potential mosquito repellent.

“When I was in elementary, we also had research with pine needles but it’s towards cockroaches, so naisip namin po na mas relevant naman po ngayon pag mosquitoes kasi tumataas ang dengue cases,” said Patricia Bea E. Palaroan.

Palaroan further said that based on the reports from the Department of Health (DOH), the incidence of dengue in CAR had increased by 100% that resulted in some casualties and that really became a problem in the area.

According to the students, they need to find a solution to the problem and so they proceeded with their research and lucky enough, they encountered less difficulty since the material they were working on was literally found everywhere in Baguio City. Further they said that their advisers and the DOH where they got their supplies were very helpful. 

“For the pine needles, we collected both dried and fresh; the dried ones we just picked them up from the ground and the fresh ones we got from the trees,” said Bryne Benedict C. Fawayan.

On the other hand, Grade 12 students from the Philippine Science High School-Cagayan Valley Region campus, namely Hermogenes B. Chioco IV, Shantal D. Uy, and Shanelle Rio Andrea T. Sadama presented their research study about the possible pharmaceutical use of the Kudit or the Split Gill mushroom ( Schizophyllum commune ) for breast cancer. 

According to the students, the said mushroom specie that is abundant in Sanchez Mira, Cagayan province in Northern Luzon is known to have antioxidant capacity and antidiabetic property. 

The student-researchers considered the health problem of breast cancer where three out of 100 Filipino women will likely develop the disease in their lifetime according to data from the Department of Health in 2014. Also, the incidence of breast cancer patients becoming drug resistant to commercially available treatments is real, thus prompting researchers to find for an alternative treatment using natural ingredients. 

“Yung mga studies na nakita po namin ay nagsasabi na mayroon silang (mushrooms) antioxidant, anti-breast cancer property, so why not na gawin siyang potential na anti-breast cancer agent, so we tested anti-breast cancer activity niya…nakita po namin sa results na nag-shrink siya gamit yun extract namin,” said Sadama. 

Likewise, if the research is validated to be successful, it offers a great relief to cancer patients as well as offer great potential for increased agricultural productivity that will encourage farmers to grow mushrooms as another source of income. 

In the field of materials science there was a research project that practically addresses two basic problems when it comes to the use of plastics; one is pollution and the other one is food safety when plastic is used for food packaging. 

Student-researchers Mikaela Andrea Paula Aquino, Patricia Nicole Aquino, and Erica Nicole Saljay from the Southern Mindanao Campus in Davao conducted their experiment on potato to solve the problems connected to the use of plastics. 

Their research work titled, “Characterization of (Solanun tuberosum) Potato – Red Cabbage (Brassica var. oleracea F. rubra) Composite Film for Intelligent Packaging Applications” is one of the 48 research papers that passed the screening and made the final cut. 

“The primary problem we want to address is the issue of plastic pollution and aside from that we want to tackle the issue of food spoilage brought about food infection or more of the ingestion of spoiled food and with this package, we want to make it easier for people to see that this food is already spoiled without actually having to be in contact with the food, for food packaging po talaga ang goal namin ,” said Patricia Nicole Aquino.

The three-day event was held from 28-30 August 2019 at the Subic Bay Travelers Hotel and Convention Center inside the Subic Bay Freeport Zone in Subic, Zambales. The annual event is being conducted to encourage students in the STEM strand to actively work on research studies and strengthen their skills and acumen in doing basic research that will address specific problems of society.

The Philippine Science High School System is under the wings of the Department of Science and Technology. The campuses represented were those in Quezon City (Main campus), Ilocos Region in San Ildefonso, Ilocos Sur; Cagayan Valley Region in Bayombong, Nueva Vizcaya; Cordillera Administrative Region in Irisan, Baguio City; Central Luzon Region in Clark Freeport Zone, Angeles, Pampanga; CALABARZON Region in Sampaga, Batangas City, MIMAROPA Region in Odiongan, Romblon; Bicol Region in Goa, Camarines Sur; Western Visayas Region in Jaro District, Iloilo; Central Visayas Region in Argao, Cebu; Eastern Visayas Region in Palo, Leyte; Central Mindanao Region in Balo-I Lanao, del Norte; Southern Mindanao Region in Tugbok District, Davao City; SOCCSKSARGEN Region in Koronadal, South Cotabato;  Caraga Region in Butuan City, Agusan del Norte; and Zamboanga Peninsula Region in Dipolog City, Zamboanga del Norte.  (Rudy de Guzman, S&T Media Service)

Building a culture of Science in Central Luzon

By Armi Flor Santos, PSHS

DESIGN THINKING WORKSHOP was conducted on August 29, during the DOST-PSHS Science Research Summit 2019 at Subic Bay Travelers Hotel. Building a culture of science is the aim of the PSHS in conducting community activities like this. In the design thinking workshop, aside from harnessing the younger PSHS scholars’ capacities in doing research, the PSHS teachers reached out to the communities to teach students from other schools in Central Luzon. 

This workshop catered to 90 grade 10 scholars from the 16 PSHS campuses and 48 high school students from the Department of Education schools in Central Luzon. It was held in two sessions, morning and afternoon, and in two classes. PSHS Main Campus teachers Mr. Alejandro Jose A. Tuazon and Mr. Jejomar O. Bongat lectured about the basics of research, techniques, and strategies.

Research is part of the PSHS curriculum, taught in Grades 10-12. In grade 12, PSHS students refine their researches, and one way is by joining the science research summit. A panel of judges, comprised of field experts, critiques the scholars’ works through an oral and poster presentation. A second version of the research posters, re-designed to attract elementary students, are displayed for public viewing during the community fair.

The DOST-PSHS Science Research Summit 2019 wraps up on August 30 with a Community Fair, which is open to the public from 8:00 A.M. to 2.00 P.M. at Subic Bay Travelers Hotel.

48 research projects ibinandera ng mga Pisay students

Balitang RapiDOST # 1-2019 Pisay SRS

By Rudy de Guzman, S&T Media Service

SUBIC BAY , Zambales — Kasalukuyang ginaganap sa Subic Bay, Zambales ang 3 rd Science Research Summit ng Philippine Science High School System kung saan may mahigit sa 200 mga matatalino at mga malikhaing estudyante ang magpapakitang gilas sa kanilang mga nagawang pananaliksik.

Ngayong araw, ika-29 ng Agosto ay magkakaroon ng Technical Poster Exhibit sa Verona Hall ng Subic Bay Travelers Hotel and Convention Center kung saan huhusgahan ng mga katangi-tanging mga hurado ang pinakamahusay na research project sa larangan ng Biology, Chemistry, Material Science, Environmental Science, Computational Science, at Engineering. May 130 research projects ang isinumite galing sa 13 Pisay campuses sa bansa na gawa ng mga Grade 12 students at 48 sa mga ito ang kasama sa summit.

Dito makikita ang mga iba’t-ibang research projects na ihahayag ang mga kakaibang nadiskubre na may pakinabang sa ating pamayanan. Ang ilan ditto ay ang mga sumusunod: ang pine needles at balat ng kalamansi na maaaring panlaban sa dengue; ang Kudit na isang uri ng kabute na posibleng maging panlaban sa breast cancer; ang paggamit ng napipiling mga herbs upang mapatagal ang shelf life ng mga karne; ang paggamit ng low-cost alert system para malaman ang mga lugar na madalas magkaroon ng landslide; at ang paggamit ng pili para mapatibay ang biodegradable plastic.

Ang naturang summit ay mayroon din Community Fair na gaganapin sa ika-30 ng Agosto kung saan masisilayan ng mga Grades 4-6 na mag-aaral sa elementarya mula sa mga eskwelahan sa rehiyon para sila rin ay mahimok na pagbutihin ang pag-aaral sa siyensya, teknolohiya at inobasyon.

DOST, PSHS top brass emphasize thrust on creating S&T culture

By Harold V. Gallo, PSHS

“Hindi tayo nagpapaaral sa mga tao (scholars) para maging scientists or researcher lang, ang ating talagang gusto ay magkaroon ng science and technology culture sa lahat ng Pilipino”, thus said Dr. Renato U. Solidum, OIC – Office of the Undersecretary for Scientific and Technological Services, DOST emphasized in a press conference at the Subic Bay Travelers Hotel in Subic Bay Freeport Zone, Philippines, August 28, 2019 during the conduct of the Science Research Summit (SRS) of PSHS System.

Dr. Solidum emphasized that the culture of science should be appreciated and mainstreamed in all of the professions.

USEC Solidum also pointed that the main thrust of PSHS and DOST is to link the academe, government and industry, and activities like SRS is an exposure for the scholars to learn new knowledge.

USEC Solidum, Executive Director Ms. Lilia T. Habacon and regional campus directors heralded the contributions of PSHS to the nation citing their respective accomplishments.

Director Habacon said that among the 20,136 graduates since 1964, 98% enrolled in S&T courses but through time, some have changed careers.

She mentioned that some graduates became entrepreneurs and already providing employment to people.

“Yun ang dream namin for Philippine Science High School graduates that they provide employment to the Filipino people because of their exposure and opportunities and in turn sana po sila ay magprovide ng employment at hindi lang basta empleyado,” said Director Habacon.

She also mentioned that around 30% of graduates are working outside the country, some are working in government and others are in private sectors and in their own ways they contribute through exchange of technical knowhow, laboratories, and expertise, etc.

Dr. Solidum and Ms. Habacon cited that through the combined efforts of the government agencies including DOST and PSHSS, the Global Innovation Index of the Philippines improved from rank 73 to 54.

Anchored on its theme breaking gender barriers through science and technology and innovation, the 2019 SRS is hosted by PSHSS which featured best researches of PSHS scholars.

DOST’s fault finder Usec. Solidum, hinimok ang mga Pisay students na gumawa ng mga research projects na makabubuti sa komunidad

Balitang RapiDOST # 2-2019 Pisay SRS

SUBIC BAY , Zambales – Sa kanyang mensahe sa may mahigit na 200 mag-aaral ng Philippine Science High School System sa 3 rd Science Research Summit ay hinimok ni Dr. Renato U. Solidum Jr., Undersecretary for Disaster Risk Reduction and Climate Change at OIC ng Office of the Undersecretary for Scientific and Technological Services ng Department of Science and Technology ang mga taga-Pisay na gumawa ng mga pananaliksik o research projects na makabubuti sa kalagayan ng mg lokal na komunidad.

Sinabi ni Usec. Solidum na gamitin ng mga mag-aaral sa Pisay ang kanilang angking talino sa siyensya, teknolohiya at inobasyon upang makagawa ng mga pananaliksik sa larangan ng biology, chemistry, engineering, material science, environmental science at computational science upang mapabuti ang pagsasaka at tumaas ang ani, ang pagdebelop ng mga environment-friendly na pang-araw-araw na mga kagamitan, mga lunas sa mga iba’t-ibang sakit, at mga paraan upang maging ligtas sa sakuna at kalamidad.  

Ang naturang summit ay nagbukas noon ika-28 ng Agosto at tatagal hanggang ika-30 ng Agosto. Mayroon mga Technical Poster Exhibit sa Verona Hall ng Subic Bay Travelers Hotel and Convention Center. May 130 research projects ang isinumite galing sa 13 Pisay campuses sa bansa na gawa ng mga Grade 12 students.

Dito makikita ang mga iba’t-ibang research projects na ihahayag ang mga kakaibang nadiskubre na may pakinabang sa ating pamayanan. Ang ilan dito ay ang mga sumusunod: ang research sa paglaban sa dengue at leptospirosis gamit ang pine needles at balat ng kalamansi; ang Kudit na isang uri ng kabute na posibleng maging panlaban sa breast cancer; ang paggamit ng fish protein powder mula sa fish waste; paggamit ng kakaibang systema sa Metromanila railway network; at ang paggamit ng low-cost alert system para malaman ang mga lugar na madalas magkaroon ng landslide. (Article by Rudy de Guzman, S&T Media Service)

Be the next international Pinoy scientists, PSHS tells youth

In a press conference for DOST-PSHS Science Research Summit 2019 on August 28, DOST Undersecretary Renato U. Solidum tells media, “it’s not only about money, it’s about the contentment and how the community would feel your worth”.

The DOST-PSHS System holds the Science Research Summit (SRS) 2019 at Subic Bay Travelers Hotel on August 28-30, with the aim to harness the scholars’ abilities in doing researches and to promote science to the communities. In line with intensifying its information campaign, the PSHS conducted a press conference on the first day of the summit, which convened communicators from DOST-Science and Technology Information Institute, Philippine Information Agency Region III and the local media in Central Luzon.

PSHS System executive director Lilia T. Habacon mentions that to date, PSHS has 20,136 graduates since its inception, and 98% of these graduates pursued science and technology (S&T) courses. She further mentioned, through time, some of these graduates became entrepreneurs, and provided employment to their fellow Filipinos.

Dr. Jessamyn Marie O. Yazon, Head of SRS 2019 and PSHS System chief science research specialist gave the press an overview of this summit while the PSHS campus directors took turns in introducing their respective campuses to the press.

As mandated by law, the PSHS System shall establish one campus in every region in the country. With this, the PSHS is able to extend its globally-competitive services to more Filipinos in the different parts of the country.

Executive Director Habacon announces that four regional campuses are accepting 120 scholars per year level: Central Luzon Campus in Clark Freeport Zone; CALABARZON Region Campus in Batangas City; Western Visayas Campus in Iloilo City; and Southern Mindanao Campus in Davao City. This increase in scholarship slots is brought about by the high number of applicants to these campuses every year.

Currently, the other 11 regional campuses accept 90 scholars per year level. PSHS is working towards increasing the number of scholarship slots in these campuses. Main Campus, the first PSHS campus which opened in 1964, accepts 240 scholars per year level.

PSHS is accepting applicants for Grade 7 in school year 2020-2021. Download the application form at www.pshs.edu.ph or visit any PSHS campuses/DOST offices/Provincial S&T offices nearest you. Deadline for filing of application is on 13 September 2019. Examination date is on 19 October 2019.

DOST PISAY SCHOLARS PARTICIPATE IN TEAM BUILDING ACTIVITIES DURING THE 3RD SCIENCE RESEARCH SUMMIT

By rudy de guzman, dost-science and technology information institute.

SUBIC BAY , Zambales – Some 226 Philippine Science High School (PSHS) scholars coming from all 16 regional campuses participated in the teambuilding activities of the 3 rd Science Research Summit (SRS) held on 27-30 August 2019 at the Subic Bay Travelers Hotel inside the Subic Bay Freeport Zone in Subic, Zambales.

The student-delegates were led by their teacher-chaperones during the pre-event activity where they were able to meet new friends from the different campuses, thus fostering stronger camaraderie among the future scientists.

This year’s summit that officially opens on 28 August 2019, carries the theme, “Breaking Gender Barriers through Science, Technology, and Innovation” where the country’s top scholars will present their research papers in the fields of Biology, Chemistry, Computational Science, Material Science, Engineering, and Environmental Science.

During the 3-day summit, the students will also mount an exhibit of their research works presented in two versions. The first will be the Technical Poster presentation on 29 August for the appreciation of the distinguished panel of judges while the second version will be the Community Fair Poster presentation on 30 August. The latter is a simpler version of the former presented with more colorful graphics and common words for viewing by Grades 4-6 students from the nearby schools in Subic and is also open to the general public.

The event will be capped by the awarding ceremony where the best research papers in each category will be given recognition with medals for the first, second and third placers. 

The Philippine Science High School System is under the wings of the Department of Science and Technology. The campuses include those in Quezon City (Main campus), Ilocos Region in San Ildefonso, Ilocos Sur; Cagayan Valley Region in Bayombong, Nueva Vizcaya; Cordillera Administrative Region in Irisan, Baguio City; Central Luzon Region in Clark Freeport Zone, Angeles, Pampanga; CALABARZON Region in Sampaga, Batangas City, MIMAROPA Region in Odiongan, Romblon; Bicol Region in Goa, Camarines Sur; Western Visayas Region in Jaro District, Iloilo; Central Visayas Region in Argao, Cebu; Eastern Visayas Region in Palo, Leyte; Central Mindanao Region in Balo-I Lanao, del Norte; Southern Mindanao Region in Tugbok District, Davao City; SOCCSKSARGEN Region in Koronadal, South Cotabato;  Caraga Region in Butuan City, Agusan del Norte; and Zamboanga Peninsula Region in Dipolog City, Zamboanga del Norte.  (S&T Media Service)

PSHS scholars prep up for science research summit in Subic

August 27, 2019 in Subic, Zambales. More than 200 Grade 12 PSHS students from 13 PSHS campuses around the country convene in Subic to present their researches to a panel of experts, and to the public in this year’s science research summit. A total of 48 research projects will be subjected for critiquing of experts during the oral presentation. These researches will also be displayed for public viewing during the community fair on August 30.

In the morning, PSHS students started setting up their research posters at the designated area in Subic Bay Travelers Hotel. To get the ball rolling, the PSHS System organizers involved the students in student leadership and team building activities with the aim to harness their leadership skills and camaraderie. Then, they were engaged in technical rehearsals for their upcoming oral presentation.

The DOST-PSHS System Science Research Summit 2019 carries the theme Breaking Gender Barriers through Science, Technology, and Innovation. It will run from August 28-30 at Subic Bay Travelers Hotel. In addition to harnessing the capacity of the PSHS students in understanding their researches, this summit targets to increase the awareness and interest of the communities in science, technology, and innovation.

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DOST-PCAARRD helps future-proof the Philippine Science High School System through capacity-building on intellectual property and technology transfer

The Department of Science and Technology’s Philippine Science High School System (DOST-PSHSS) is currently collaborating with the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (DOST-PCAARRD) to develop  staff’s technology transfer capacity. This is being done through the project, “Developing Technology Transfer Capacity of the Philippine Science High School System (PSHSS).”

DOST-PSHSS is the premier science high school in the Philippines where students are prepared for a career in science, technology, and mathematics (STEM).

The two-year project, under the leadership of Ms. Jodi Mylene Lopez, aims to enhance the intellectual property (IP) and technology transfer system among the 16 PSHS campuses and enhance its technology commercialization programs.

DOST-PSHSS Executive Director Lilia T. Habacon said that the project was proposed to DOST-PCAARRD as it was inspired by the System’s vision to future-proof amidst the changing demands of the industry. At PSHSS, students are trained to develop a high aptitude in science and mathematics  and apply these skills in conducting research with results expected to benefit the general society. To better manage these research outputs, the project will conduct capacity-building activities and create a framework that will guide every campus to commercialize technologies developed by the students.

The PSHS training participants with APP Vice President Atty. Misael F. Costes, Vice President Atty. Editha R. Hechanova, and DOST-PCAARRD TTPD’s Dir. Noel A. Catibog and staff during the Intellectual Property Master Class (IPMC) which kicked off on June 14, 2023.

With this, a more holistic PSHSS that will empower the youth and science community to develop and commercialize world-class technologies, is expected.

 A project inception meeting was held on June 13, 2023 at the R.D. Guerrero III Room, DOST-PCAARRD Innovation and Technology Center (DPITC), Los Baños, Laguna to discuss the implementation plans and ensure the accomplishment of the project’s goals and objectives. The meeting was attended by DOST Undersecretary for Scientific and Technical Services Maridon O. Sahagun, DOST-PCAARRD Executive Director Reynaldo V. Ebora, DOST-PSHSS Executive Director Lilia T. Habacon, DOST-PCAARRD Officer-in-Charge for Research and Development (R&D) Juanito T. Batalon, and Technology Transfer and Promotion Division (TTPD) Director Noel A. Catibog. Likewise, as the project's training services provider and partner, Association of PAQE Professionals (APP) President Misael F. Costes and Vice President Editha R. Hechanova also participated in the meeting.

Being the first training under the project, the DOST-PCAARRD Intellectual Property Master Class (IPMC) kicked off with the first module focused on the introduction to IP management, prior art search report, and invention spotting.

The five-module, 15-day DOST-PCAARRD IPMC was conducted from June to July 2023 via hybrid learning mode. Trainees used the research outputs of students from their respective campuses as training material during prior art search and claim drafting up to actual IP application. Moreover, the IPMC graduation ceremony was conducted on July 28, 2023 to mark its completion and to recognize the dedication and hard work of the trainees and the training provider  APP. The ceremony conferred 32 IPMC graduates and awarded the topnotchers of the mock exams with cash prizes worth P2,000 (Top 3), P3,000 (Top 2) and P5,000 (Top 1), by APP.

Topnotchers of the Chemical Patenting Mock Exam:

Topnotchers of the Non-Chemical/Mechanical patenting Mock Exam:

This batch of IP Master Class graduates filed and drafted a total of 30 IP applications including 20 patents and 10 utility models. However, more than these numbers, Dr. Ebora emphasized that this comprehensive mentorship program equipped these aspiring patent agents with five P-I-S-A-Y important traits: 1.) Proficient communicators who are proficient in using words with a versatile communication style as translators of technology, business, and law; 2.) Investigative thinkers who find the best way to protect the technology; 3.) Steadfast or simply diligent; 4.) Analytical thinkers who are critical of data and evidence and comprehensive in processing legal, commercial, and scientific information; and 5.) With an ‘eYe’ for detail in preparing IP applications.

Dr. Ebora congratulated the graduates especially for being P roficient communicators, I nvestigative-thinkers, S teadfast, and A nalytical thinkers with an e Y e for detail or simply P-I-S-A-Y technology transfers agents.

The IPMC will be followed by the DOST-PCAARRD Technology Commercialization Mentorship Series (TCMS), which will focus on the strategies to promote and commercialize technologies generated by PSHSS. It will run from October to December 2023. 

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The research program of the Philippine Science High School

1989, Journal of Chemical Education

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In this thesis, it defined a dichotomy with e-learning particular application layers on top and grid-based support layers below. This will also show a very useful and helpful approach for enabling the education sector boost in the Philippines. The model still requires an efficient solution to integrate with sharable and collaboration contents of each diverse e-Learning platform, so in this thesis an integration solution is proposed to answer the difficulty across the e-Learning platforms by utilizing grid service technology and grid architecture. In this thesis, Grid Utilization in Learning Objects (GURÓ) solves the problem in learning resources integration on each varied learning management system. Users or learners could send an e-Learning request through a unified Web interface, create a service vigorously and dynamically, learning content presentation and a safety run-time environment was provided. Utilizing the standard toolkits was used to generate and access the learning objects. Grid services were used to integrate the learning objects among the various systems and manage the available resources in the grid environment. When learners or users proposed a request, system can allocate distributed resources to build a safe e-Learning grid environment. In addition, grid services were open protocols and standards based, in order that the distributed various resources could be integrated efficiently. Utilizing the grid computing technology ideas strengthen the capabilities and flexibilities of the learning objects in e-Learning environment or the so-called distance education. Magnificent advances in ICT technology and the evolution of the Internet or web have directed to new styles and approaches in learning and training which are condensed under the term e-Learning. Enabling the concept of learning objects in the e-Learning setting in the context of Grid technology with the utilization of Next Generation Internet or the Web 2.0 will prompt this thesis to demonstrate the usefulness of e-Learning in the Philippine society. Again, this thesis will present and explain what can be expected from e-Learning with the help of learning objects although especially focused on the technological basis, will examine all kinds of requirements for e-Learning environments: academic, functional and non-functional requirements with the advent of Web 2.0. Distance Education plays an essential part in linking the application of e-learning. In whatever circumstances, it can be used in academic courses in the universities or learning institutes or in training courses within the enterprises. e-Learning is a very effective learning method these days. In the other way, conventional and traditional teaching methods, learners must simultaneously and physically present. Dealing with the conventional way, when learning institutions have numerous learners, excessive expenditures of money and time are often necessary and required. The advantage of Distance Education is that it can solve the problem of geographical location and enable students at remote sites to feel as if they are attending classes in a conventional classroom. Furthermore, it can conserve money and eliminate the time and energy consumed in traveling beyond the boundaries. Although e-Learning has many advantages, its biggest drawback is the required investment in equipment. In order to help more enterprises and schools embrace Electronic Learning, this study adopted Grid Computing technologies to build an e-learning platform that features expandability and resource-sharing capability. In this paper also describes all relevant standards in the field of e-Learning due to its importance for increasing interoperability, cutting costs and gaining acceptance worldwide. In this work of art also will offer cost and benefit analysis for the Philippine government to decide whether Grid computing concepts in the context of e-Learning environment will give them a high level decision to come-up with the implementation of such project, “The utilization of Grid computing in the Philippines: A Cost and Benefit Analysis and Focus on e-Learning Capability”.

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• Published: 03 May 2023

Profiling low-proficiency science students in the Philippines using machine learning

• Allan B. I. Bernardo   ORCID: orcid.org/0000-0003-3938-266X 1 ,
• Macario O. Cordel II 1 ,
• Marissa Ortiz Calleja 1 ,
• Jude Michael M. Teves 1 ,
• Sashmir A. Yap 1 &
• Unisse C. Chua 1  

Humanities and Social Sciences Communications volume  10 , Article number:  192 ( 2023 ) Cite this article

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Filipino students’ performance in global assessments of science literacy has always been low, and this was confirmed again in the PISA 2018, where Filipino learners’ average science literacy scores ranked second to last among 78 countries. In this study, machine learning approaches were used to analyze PISA data from the student questionnaire to test models that best identify the poorest-performing Filipino students. The goal was to explore factors that could help identify the students who are vulnerable to very low achievement in science and that could indicate possible targets for reform in science education in the Philippines. The random forest classifier model was found to be the most accurate and more precise, and Shapley Additive Explanations indicated 15 variables that were most important in identifying the low-proficiency science students. The variables related to metacognitive awareness of reading strategies, social experiences in school, aspirations and pride about achievements, and family/home factors, include parents’ characteristics and access to ICT with internet connections. The results of the factors highlight the importance of considering personal and contextual factors beyond the typical instructional and curricular factors that are the foci of science education reform in the Philippines, and some implications for programs and policies for science education reform are suggested.

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Introduction

Global concerns such as the ongoing COVID pandemic and climate change crisis underscore the importance of science and technology for providing sustainable and responsible strategies for global development. Yet in many parts of the world, students’ interest and achievement in science continue to decline (Fensham, 2008 ). The Philippines is one of those countries where students are observed to have low levels of science literacy for many years now (Martin et al., 2004 ; Talisayon et al., 2006 ). This pattern was confirmed when the Philippines participated for the first time in the Program for International Student Assessment (PISA) in 2018, where the results found Filipino 15-year-olds near the bottom of the ranking among 78 countries and territories (Organisation for Economic Cooperation and Development [OECD], 2019 a, 2019 b). Some Philippine studies have tried to understand low science achievement by looking at the curriculum (Belmi and Mangali, 2020 ; Cordon and Polong, 2020 ) and instruction (Sumardani, 2021 ). In this study, we used machine learning approaches to determine the most accurate predictive models that can identify the poorest-performing science students in the PISA 2018 sample. For the variables in the predictive model, we consider a range of variables in the student questionnaire of PISA that refer to the student’s home and family background, beliefs, goals, attitudes, perceptions, and school experiences. We focus on non-instructional and non-curriculum variables with the view of understanding the variables that identify the Filipino students who are most vulnerable to poor science learning.

Filipino students’ science literacy in PISA

The Philippines participated in PISA for the first time in 2018, with students’ answering the assessments in reading, mathematics, science, and global competencies. For science literacy assessment, the PISA 2018 Framework broadly defines science literacy as “the ability to engage with science-related issues, and with the ideas of science, as a reflective citizen” (OECD, 2019 a, 2019 b, p. 100). According to the PISA science framework, scientific literacy relies on a combination of knowledge and competencies that are applied to different contexts. Student performance was reported using seven levels of proficiency, with Level 6 being the highest level of proficiency and Level 2 as the minimum level of proficiency. Students who achieve Level 2 proficiency are able to demonstrate the ability to use basic or everyday knowledge to explain scientific phenomena in familiar contexts and to interpret simple data sets. This level of proficiency serves as a baseline or minimum evidence for science literacy.

There were 7233 15-year-old Filipino students who participated in the PISA 2018 cycle (OECD, 2019 a, 2019 b), where the Philippines ranked as one of the poor-performing countries in science. The country had an average score of 357 which is significantly below the OECD average score of 489 with boys and girls performing similarly (355 and 359 average performance, respectively). Only about 22% of these students achieved Science Literacy scores at Level 2 or higher. In comparison, an average of 78% of students from OECD countries reached Level 2 or higher in the science literacy assessment. Students at Level 2 or higher can recognize the correct interpretation for familiar scientific phenomena and can use such knowledge to identify, in simple cases, whether a conclusion is valid based on the data provided. The poor performance of Filipino students is reflected in the fact that around 77% of them did not reach the minimum proficiency level. At the lowest proficiency levels (1A and 1B), students are only able to use everyday content and procedural knowledge to explain simple or familiar phenomena. Their ability to understand data and to design scientific inquiry is highly limited (OECD, 2019 a, 2019 b).

The pattern of Filipino students’ performance in PISA 2018 matches their achievement in another international assessment, the Trends in International Mathematics and Science Study (TIMSS). Similar to PISA, TIMSS measures students’ ability to apply their knowledge in different content areas of science. Performance was evaluated using benchmarks, each with a corresponding scale score: Low (400), Intermediate (475), High (550), and Advanced (625) (Mullis et al., 2020 ). Fourth-grade Filipino students who participated in the TIMSS 2019 cycle achieved an average scale score of 249, the lowest in 58 participating countries with an overall average score of around 491. Only 19% of Filipino students achieved scores in the Low benchmark or higher, which implies that the overwhelming majority of Filipino students “show limited understanding of scientific concepts and limited knowledge of foundational science facts” (Mullis et al., 2020 , p. 107).

Such consistently poor achievement levels in science are very likely the results of a wide range of interacting factors. Previous research using PISA data has attempted to identify important factors that differentiate the performance of high and low high and low scorers in PISA. For example, to determine which factors contribute to the gap between high and low PISA science scores, Alivernini and Manganelli ( 2015 ) considered factors coming from country, school, and student levels. They applied a classification and regression tree analysis to the PISA 2006 data from 25 countries to identify the factors that predicted high (above Level 4) or low (below Level 2) proficiency. The strongest country-level predictor was teacher salary. At the school level, parental pressure on the school’s standards (for low teacher salaries) and school size (for high teacher salaries) predicted students’ PISA performance. At the student level, science self-efficacy and awareness of environmental issues determined whether a student would be a low or high performance in the PISA science assessment.

In this study, we employ a similar approach to studying the variables that might explain the poor performance of most Filipino students. We compare the group of poor-performing students with the group of better-performing students and consider variables related to the student’s family/home backgrounds, beliefs, goals, attitudes, perceptions, and school experiences. Instead of using statistical approaches, we use machine learning approaches to test models that best identify and distinguish the group of poor-performing students from the better-performing ones. Machine learning approaches have been proposed as complementary to statistical approaches (Lezhnina and Kismihok, 2022 ), particularly for purposes of handling very large numbers of variables in high-dimensional datasets (like those in the PISA) while avoiding convergence problems and for developing multidimensional complex models that may feature nonlinear relationships (Hilbert et al., 2021 ; Yarkoni and Westfall, 2017 ). Such machine learning approaches have been used to study science achievement in PISA 2015 (Chen et al., 2021 ), but the study focused on identifying the top performers, not the poor performers. Such approaches have been used to study the PISA 2018 data in other countries like China (Lee, 2022 ), Singapore (Dong and Hu, 2019 ), and the Philippines (Bernardo et al., 2021 , 2022 ), but these studies focused on predicting either performance in reading, mathematics, or the average across domains, and none so far, have focused on the PISA 2018 science results. The analytic approaches are discussed in the methods section. But we first consider the range of possible predictor variables suggested by the relevant literature and that were available in the PISA student questionnaire the Filipino students answered.

Predictors of science learning and achievement

Most studies on science education in the Philippines have focused on curriculum (Balagtas et al., 2019 ; Ely, 2019 ; Morales, 2017b ), knowledge, beliefs, and practices of science teachers (Bug-os et al., 2021 ; Macugay and Bernardo, 2013 ; Orbe et al., 2018 ; Walag et al., 2020 ), and beliefs and perceptions of science learning (Alonzo and Mistades, 2021 ; Bernardo et al., 2008 ; Magalong and Prudente, 2020 ; Montebon, 2014 ); typically such studies do not empirically establish any relationship with Filipino students’ science learning or achievement. But there are some studies that do identify some predictors of Filipino students learning and achievement in chemistry, biology, physics, or some specific science lessons. And these typically fall into two types of inquiries: (a) those that investigate the learning outcomes of particular instructional strategies (Antonio and Prudente, 2021 ; Francisco and Prudente, 2022 ; Magwilang, 2016 ; Morales, 2016 , 2017a ; Orozco and Yangco, 2016 ), and (b) those that looked into student motivations and other non-cognitive student level variables as predictors of learning and achievement (Bernardo, 2021 ; Bernardo et al., 2015 ; Ganotice and King, 2014 ; King and Ganotice, 2013 , 2014 ). In this study, we worked with variables from the student self-report questionnaire of PISA 2018, so we could not study instructional strategies (i.e., the first set of studies above), but we are able to study student-level variables similar to the latter group of studies that include motivation, self-beliefs and a host of other variables that relate to students family and home backgrounds, perceptions and attitudes related to their classroom and school experiences, and their goals and aspirations for after they finish high school. We consider what the research literature suggests about such variables below, starting with student-level variables that were included in the PISA 2018 self-report survey and that were found to be important predictors of science literacy in previous PISA research in different countries.

Student factors

Certain student characteristics have been shown to influence their performance in science or scientific literacy. Gender appears to be associated with scientific literacy, with boys performing better than girls in the 2015 PISA cycle (OECD, 2016 ), but the results of numerous other studies are mixed (Cutumisu and Bulut, 2017 ; Lam and Lau, 2014 ; Sun et al., 2012 ). Affective and motivational factors seem to be important correlates of science achievement in PISA; these factors include students’ enjoyment of science and perceived value of science (Ozel et al., 2013 ), positive motivations, interest, more sophisticated epistemic beliefs (Hofverberg et al., 2022 ; She et al. 2019 ), self-efficacy, intrinsic and instrumental motivations for learning science (Kartal and Kutlu, 2017 ; Mercan, 2020 ), having a growth mindset (Bernardo, 2021 , 2022 ; Bernardo et al., 2021 ), among others. Other motivation-related processes are also associated with science literacy achievement. These include students’ projective self-assessments of their own abilities and their future aspirations (Lee and Stankov, 2018 ), perseverance and willingness to solve problems (Cutumisu and Bulut, 2017 ), and use of metacognitive strategies (Akyol et al., 2010 ; Callan et al., 2016 ). Interestingly, students’ reading skills and reading strategies have also been associated with science achievement (Barnard-Brak et al., 2017 ; Caponera et al., 2016 ). The role of reading strategies is proposed to be important as science learning depends to an extent on students’ comprehension of scientific text (Cano et al., 2014 ; Kolić-Vrhovec et al., 2011 ) and this association seems particularly important when the students are learning science in a second language instead of their home language (Van Laere et al., 2014 ), which is the case with the Filipino students who participated in PISA 2018.

Family and home factors

The socioeconomic status (SES) of students’ families has been a consistent predictor of scores in PISA (Lam and Zhou, 2021 ; Lee and Stankov, 2018 ), and this is true in the domain of science (Sun et al., 2012 ). This variable has been unpacked and many other factors associated with SES have been identified as predictors of achievement in PISA. These SES-related factors include the educational attainment and occupation of their parents (Chen et al., 2021 ; Schulze and Lemmer, 2017 ). In one such study, researchers found that parents’ education had the largest indirect effect on children’s PISA test scores (Burhan et al., 2017 ). The influence of each parent’s education, however, appears to differ. A study that analyzed the PISA 2000 performance of 30 countries found that the mother’s educational attainment had a greater impact on students’ scores than the father’s educational attainment (Marks, 2008 ). Similar to education, parents’ occupations also predicted students’ learning outcomes. Students whose parents had a higher level of occupation were found to have higher scientific competencies than students whose parents were low-skilled workers (Chi et al., 2017 ). Another variable related to SES is the students’ access to information and communication technologies (ICT) at home, particularly ICT with access to the Internet. ICT availability and use positively predicted performance in various PISA assessments (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ). We also note that studies indicate SES seems to be associated with some student-level factors. For example, SES is strongly associated with feelings of school belonging (King et al., 2022 ).

Other than SES-related factors at home, parental involvement and family investment in children’s education also appear to influence students’ academic performance (Ho and Willms, 1996 ). Using data from a national survey of Chinese students’ science literacy, Wang et al. ( 2012 ) found that students’ high scores were associated with parents’ investment in their children’s education through the purchase of educational materials and other resources at home. A study of ninth-grade students in South Africa found that family experiences, such as the learning environment at home, were related to the student’s motivation to learn science (Schulze and Lemmer, 2017 ).

School factors

The school characteristics that have been shown to influence students’ scientific literacy performance include SES (or SES composition), school enrollment size, and location. Wang et al. ( 2012 ) found that certain school characteristics, namely school standing, having libraries and computer laboratories, good relationships between teachers and students, and funding for teacher training were associated with higher science literacy scores. School SES composition was found to be strongly associated with high scientific literacy scores of Australian students (McConney and Perry, 2010 ). Analysis of Hong Kong students’ PISA scores revealed that school SES composition partly explained differences in science achievement (Sun et al., 2012 ). Class size (Bellibaş, 2016 ; although see Lam and Lau, 2014 ) and school location (Topçu et al., 2014 ) are also predictors of science achievement.

Other than these school characteristics, students’ experiences and perceptions of their classroom and school environments also predict their achievement in PISA. In a study of the performance of Chinese students in the 2015 PISA, Huang ( 2020 ) found that reported experience of bullying in school was associated with achievement scores, and this relationship was medicated by the student’s sense of belonging in school. School disciplinary climate and quality of student-teacher relationship were significant predictors in particular countries (Shin et al., 2009 ); with the effect of disciplinary climate possibly having a more positive impact on students from low SES groups but the evidence across countries is mixed (Chi et al., 2018 ; Scherer, 2020 ).

The current study

The extant literature suggests that a wide range of factors at the student, family/home, and school level are likely predictors of science literacy, although some of these factors were shown to be important predictors in some countries but not all. In this study, we explore a range of such factors to inquire which best identifies the poor-performing Filipino students in contrast to the better-performing ones. The factors explored in the study are among those in the PISA 2018 student self-report survey that Filipino students answered.

Most education research that examines relationships among such variables applies statistical approaches. In such studies, correlations can show the linear relationship between each variable of poor and better-performing groups. In the current Philippines PISA 2018 dataset where we examine 85 variables as predictors, the possible correlations are over 7000 in number. For a more complex, nonlinear system with hundreds of variables that are not independent, we believe that it is best to use machine learning models. In contrast to the standard statistical approach, machine learning models capture the high-dimensional, possibly nonlinear, interrelations among a very large number of predictors (Hilbert et al., 2021 ; Yarkoni and Westfall, 2017 ), while identifying those most relevant to prediction. And, in order for the analysis to be more valid, we argue that the model should be optimal, in this case, the model with the best accuracy. For this study, we try out different machine-learning approaches to determine the best model to uncover the relationships between these variables.

The specific objective is to use machine learning approaches to determine the most accurate model that best identifies the Filipino students who performed at the lowest levels in the science domain of PISA 2018. We sought the best model that will indicate the factors that identify the students who are vulnerable to poor learning in science in the hope that the model will call the attention of Filipino educators to the non-instructional and non-curricular factors that contribute to poor learning in science among Filipino learners. The variables that were considered included student factors (e.g., motivations, self-beliefs, goals, aspirations), family/home factors (e.g., family SES, parents’ education and occupations, learning resources at home), and classroom/school factors (e.g., instruction time for science, teacher behaviors, perceived school environment, self-reported social experiences in school).

Our methodology for determining the best model that features the most important variables that identify the low-performing Filipino student in science is summarized in Fig. 1 , which shows the different phases of our data analysis. The first step is data preparation which entails data cleaning, that is, removal of variables with 100% missing data, identification, and imputation of entries with missing values, and variable scaling. Next is feature selection which involves the careful refinement of the list of variables that may contain negative suppressors. Then, machine learning model training follows to search for the best nonlinear prediction model. Finally, the machine learning model evaluation describes quantitatively the model performance and reports variable importance.

To find the optimal computational model, the whole data processing pipeline is performed for different sets of hyperparameters, for different machine learning approaches.

The dataset

The data we used in the analysis were from the Philippine sample in the PISA 2018 data (publicly accessible at https://www.oecd.org/pisa/data/2018database/ ). PISA 2018 test items for the science subject measure the ability to engage with science-related issues as a thoughtful citizen (OECD, 2019 a, 2019 b). To assess this, the questions given are related to contexts , e.g. personal, local and global issues, both current and historical that require understanding in science and technology; to knowledge , e.g. content, procedural, and epistemic; and to competencies that exhibit the ability to explain phenomena scientifically, evaluate and design scientific inquiry, and interpret data and evidence scientifically. In addition to these, students answered background questionnaires about themselves, their homes, and the school and learning experiences. As discussed, these variables were considered in this study. The performance of students is estimated and reported as 10 plausible values with 0.88 reliability for the Philippine science data.

A two-stage stratified sampling design was followed to obtain the nationally representative sample: (a) 187 schools were randomly selected from the country’s 17 regions, with the number of schools selected proportional to the regional distribution of schools, (b) students were then randomly selected from each school. As mentioned in the introduction, the final sample was 7233 15-year-old students. From the database, we identified 85 variables that referred to student, home/family, classroom/school factors suggested by the extant literature as possible predictors of science literacy, which we measured using the first plausible value of science literacy (PV1SCIE).

Data preparation

As reported earlier, over 80% of the Filipino students who participated in PISA 2018 were found to have less than Level 2 proficiency in science. The detailed distribution of participants across the different proficiency levels is shown in Fig. 2a . Because our goal is to identify the variables that are potentially influential in identifying the extreme poor performers in science, we decided to train a binary classification model that identifies these students and to study the variables that are important in this model prediction. For the binary classification, the sample data was divided into two categories; the (1) poor-performing students, who have proficiency at Level 1b and below, and (2) better-performing students, who have proficiency at Levels 1a, 2, and above. The data distribution for the two groups is shown in Fig. 2b .

Normalized Science proficiency level distribution of students ( a ) and distribution of students with poor and better performance in science ( b ). Poor performance category is for those students who belong to Level 1b and below proficiency levels, otherwise, the students are assigned to the better performance category.

The samples were further trimmed down as part of the data preparation. Students with more than 50% of the total variables missing were dropped from the dataset, obtaining the final data distribution in Table 1 . Sampled randomly, around 80% of the data were used as the training samples for the model training while the unseen or remaining data were used as the test data.

To avoid bias in training the model, data balancing was conducted by oversampling the poor-performance samples and undersampling the better-performance samples. For the poor-performing category with 2419 samples, the Synthetic Minority Oversampling Technique or SMOTE algorithm (Chawla et al., 2002 ) was applied to increase the samples. The SMOTE first chooses a random sample from the minority class, for example, sample A. Then, it looks for its nearest neighbor of the same class, for example, sample B. The algorithm performs a convex combination of the two samples to produce the synthetic sample. For the better-performing category with 3297 samples, the Tomek Links (Tomek, 1976 ) algorithm is used to undersample the majority class. The algorithm removes the ambiguous samples from the majority class, which is the data from the majority class that is closest to a minority class data. The final number of training samples for each of the poor and better performance categories is 3214.

The list of variables was further refined by removing variables with 100% missing values (i.e., the questions were not included in the set of questions asked for Filipino students). Those remaining variables with missing values were imputed using the k -nearest neighbor algorithm, where k  = 7. Also, initial experiments showed the occurrences of negative suppressors. To minimize the number of suppressors, we removed variables with high correlation with other factors, i.e. | ⍴ | > 0.75. Finally, normalization by scaling was performed per variable. In summary, 13 variables were removed because these variables have missing values only and 20 variables were removed because they have high correlation with other variables. The final number of variables is 72 plus the scientific literacy score of PV1SCIE.

Machine learning modeling

Our approach to determining the key variables that identify Filipino students with poor performance in science used machine learning, aiming to come up with a computational model that relates the input variables to the target variables. The design for the computational model is evaluated in terms of training and test accuracy to measure the model performance in both seen and unseen data and the Area under the Region-of-Convergence curve (AUC) to determine how well the separation of data is in the model space.

Exhaustive hyperparameter search on the following computational models: support vector machines (SVM), logistic regression, multilayer perceptron (MLP), decision tree, and random forest (RF). Performance in terms of accuracy revealed that the best model is the RF classifier, having 500 estimators, maximum decision tree depth equal to 20, and maximum features equal to ceiling(log 2 71) = 7 variables per individual tree, which is the best classifier. (Please refer to Supplementary File for the performance summary of the machine learning models considered.) To illustrate the RF model, please refer to Fig. 3 . The RF model is composed of several independent decision trees that are trained independently on a random subset of data. To measure the quality of a split, entropy is used to measure the information gain.

It is composed of n  = 500 decision trees called estimators with a maximum tree depth of 20. Each input to the estimator uses only a subset of variables equal to ceil(log 2 71) or 7 variables. This minimizes the model overfitting due to the original large number of variables.

Model performance

The summary of the model performance is shown in Fig. 4 . The positive class for this study refers to the poor-performing class while the negative class refers to the better-performing class. Since the test dataset is not balanced, three performance metrics were observed: classification accuracy, precision, and recall. Accuracy is the ratio of correctly classified students, whether poor-performing or better-performing students, over the total number of students. Precision is defined as the ratio of the number of correctly predicted poor-performing students and the total number of predicted poor-performing students. Recall is the ratio of correctly predicted poor-performing students and the total number of poor performing. High precision and recall show that the model is returning accurate results (high precision), and returning a majority of all positive results (high recall).

The confusion matrix ( a ) and the ROC ( b ) summarizing the performance of the RF model in classifying the PISA 2018 Science Proficiency of Filipino students. The average accuracy is 0.74 and the area under the ROC being equal to 0.83.

The RF Classifier returned a good balance of precision and recall on the training data with values equal to 0.74, and 0.79, respectively. In addition to this balance, among the different classifiers considered, the grid-search accuracy (see Fig. 5 ), shows that the RF classifier returned the best performance with final accuracy equal to 0.74, considering the precision and recall balance. The final precision, recall and accuracy using the test data are 0.73, 0.66, and 0.74, respectively. The area under the receiver operating curve (AUC) is 0.83 which implies a fairly good-fit model. A perfect classifier has AUC = 1.0 which implies that the model was able to separate the two classes, i.e. positive and negative, of data. The worst classifier, i.e. chance level accuracy, has AUC = 0.5.

The scatterplot illustrating the range of test accuracies during the cross-validation on best machine learning models shows that the RF returned the best accuracies.

Model Interpretation

We investigated the feature importance learned during training by the RF classifier. We used Shapley additive explanations (SHAP) which is a scheme based on cooperative game theory to interpret the contributions of features in the prediction. For the RF classifier in this study, these top 15 key features or variables are shown in Fig. 6 . Footnote 1 The important variables can positively affect or negatively affect the prediction of poor performance class ( y  = 1). Particularly, one student with higher values for the variables BELONG, WORKMAST and BEINGBULLIED will negatively affect the prediction of identifying the poor performers in science. Similarly, for students with high ST164Q05IA, BSMJ, and HISEI values, the prediction of identifying poor performers in science is higher since these values positively impact this prediction. We describe the 15 variables in meaningful groupings below.

Blue bars represent variables that negatively affect the prediction of poor-performing students while red bars indicate that a variable positively affects the prediction of poor-performing students.

The largest cluster of variables relates to students’ metacognitive awareness in reading or their perceptions about the usefulness of particular metacognitive strategies when reading texts in their classes. These variables are related items, where students were asked to indicate whether the indicated strategy is useful for understanding and memorizing the texts they read. Three of the variables positively identified the poor-performing students: (ST164Q05IA) “I summarize the text in my own words,” (ST164Q04IA) “I underline important parts of the text,” and (ST164Q02IA) “I quickly read through the text twice.” These three reading strategies involve relatively low metacognitive skills and are often ineffective, and poor-performing Filipino science students tend to see them as useful. On the other hand, two of the variables negatively identified poor-performing students: (ST164Q01IA) “I concentrate on the parts of the text that are easy to understand,” and (ST164Q03IA) “After reading the text, I discuss its content with other people.” The poor-performing Filipino science students tend to perceive these strategies as not useful.

The next largest cluster of variables relates to the student’s classroom and school experiences. Sense of belonging (BELONG) and perceived cooperation among students (PERCOOP) both negatively identify poor-performing students; that is, students who perform poorly in science report a low sense of belonging and perceive less cooperation among students. These two variables suggest negative social relations experienced by poor performers in science. Fortunately, self-report of experiencing bullying (BEINGBULLIED) was also negatively identified as the poor performers in science, so they tended to report less experiences of bullying in school. The last variable related to classroom experiences was how often “Students don’t listen to what the teacher says” (ST097Q01TA), which negatively identified the poor performers in science. The poor-performing science students were less likely to say that students often do not listen to the teacher. We should clarify that the item refers to teachers who use English in their classes, which refers to teachers in several subjects including science, mathematics, and English.

Three variables relate to the students’ affective or motivational experiences. The student’s motivation to master assigned learning tasks (WORKMAST) negatively identify poor-performing students, which means they tend to have low work mastery motivation. On the other hand, the student’s expected occupational status (BSMJ) and feeling proud about the things they accomplished (ST188Q02HA) both positively identified the poor-performing students. So the students who scored very low scores in science also tended to report higher job aspirations and being proud of their accomplishments compared to others. It seems that the student’s low achievement in science is unrelated to their future occupational plans and their present sense of accomplishment.

Finally, the remaining variables relate to the student’s family and home learning resources. Having smartphones with internet access at home (ST012Q05NA) negatively identified the poor-performing students, which means they were less likely to have this learning resource. But interestingly, the mother’s education (ST005Q01TA) negatively identified the poor-performing students, but the parents’ occupational status (HISEI) positively identified the students. This means that having mothers with lower educational attainment but having parents with high-status occupations also identified the students who were performing poorly in science. We could be seeing a pattern where low achievement in science is probably not viewed or experienced as a hindrance to higher-status professions. We explore this point and other results in the discussion section.

We used machine learning approaches to explore the best model for identifying the poorest-performing Filipino students in science using the PISA 2018 data. The Random Forest model was found to have the highest accuracy performance and the SHAP analysis indicated 15 variables that identified the poorest-performing science students.

Caveats and limitations

Before we discuss the meaning and implications of the details of the results, we need to underscore some important limitations in our study. First, our study cannot speak to the instructional and curricular factors that are typically the subject of discussions on improving science education in the Philippines. Second, the predictors in the model were limited to the variables in the PISA student self-report questionnaire. While there was a wide range of variables in the student questionnaire, many of the questions referred to reading (because the 2018 cycle of PISA was focused on reading), and thus, could not be included in our study. We also did not include variables from the school-head questionnaire about school characteristics, resources, and practices; nor could we include other potentially important predictors of science achievement that were not included in the PISA. Thus, there are possibly other variables that identify poor-performing students that are beyond the scope of this inquiry.

One important caveat relates to the predictive nature of the machine learning approaches, which treat variables equally without any theoretical presuppositions. Machine learning approaches focus on prediction accuracy and is not used to test explanatory models that specify theoretical relationships among variables (Shmueli, 2010 ; Yarkoni and Westfall, 2017 ). As such, the variables identified in the most accurate model may not have any obvious theoretical connection. These caveats notwithstanding, there are useful insights revealed by the analysis, which we discuss below.

Reading strategies for learning science

Metacognitive awareness regarding five different strategies identified the poor performers in science. It may seem surprising that reading strategies play an important role in identifying poor science performers, but the results make sense if one considers that much of science learning might be based on reading science textbooks (instead of doing laboratory experiments or field projects). Research with Italian students, for example, showed a difference in science achievement between good and bad readers, regardless of whether the science items involved low or high reading demand (Caponera et al., 2016 ). There were similar associations between reading comprehension and science achievement in a study of Spanish (Cano et al., 2014 ) and Filipino students (Imam et al., 2014 ). We note that our results do not actually involve reading comprehension, but metacognitive awareness of reading strategies, similar to a study of Croatian students that established a relationship between students’ reading strategies and comprehension of scientific texts (Kolić-Vrhovec et al., 2011 ). It is plausible that poor achievers in science are those that might be adopting the wrong reading strategies in reading their science textbooks.

Families’ and students’ resources and aspirations

High social, cultural, and economic resources in the students’ families (Lam and Zhou, 2021 ; Sun et al., 2012 ) and higher professional aspirations (Lee and Stankov, 2018 ) are typically associated with better achievement. But in our results, the poor-performing students were identified by higher job aspirations and stronger pride about one’s achievements. It is as if low achievement in science was not a consideration when students think about their future occupations nor when they assess their self-worth and pride. If we consider that the lower educational attainment of the mothers and higher occupational status of parents also predicted the poor-performing, it may be that students view their poor achievement in science as not relevant to their future occupational prospects, as their parents enjoy good occupations, even if their mothers are not highly educated. This interpretation asserts that science achievement might not be valued in pragmatic terms by the students based on what they see in their elders, which might also explain the role of low work mastery in school in identifying poor-performing students. Indeed, it is possible that many high-status occupations in the Philippines do not require knowledge of science, and as such, persevering and doing well in science might not be an important motivation among the students. This interpretation will need to be verified in future studies.

Negative social experiences

It was interesting to note that experience of bullying was a negative factor in the model, so it was not the case that experience of bullying was positively linked to poorer science achievement, as was found in Chinese students (Huang, 2020 ). However, two factors that indicate relational issues in school are identified with the poor performers: reporting a low sense of belonging and low cooperation among students in school. These factors suggest that a lack of connectedness and a collective spirit might be associated with poor science performance. Trinidad ( 2020 ) found that school-level and student-level measures of school climate were predictors of Filipino students’ mathematics achievement; such social factors might also have similar roles in Filipino learners’ poor science achievement.

Access to ICT for learning

One factor that may be increasingly important in identifying poor science achievers is access to ICT devices with internet access. Studies on Filipino students; PISA achievement in reading (Bernardo et al., 2021 ) and mathematics (Bernardo et al., 2022 ) also found the same factor as a predictor of achievement, consistent with much of the research in other countries (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ; but see Bulut and Cutumisu, 2018 ). Presumably, access to the internet outside the school environment has become an important resource for learning science; perhaps not just for accessing relevant scientific knowledge available online but also as a means of communicating with classmates for information sharing, collaboration in learning activities, and supporting each other’s motivations and engagement in science learning. Filipino students without such access are disadvantaged in the domain of science.

Practical implications: Focusing on the lowest achievers

The current study provides some analysis that could inform reform efforts in the domain of science learning, particularly as it concerns the lowest-achieving Filipino students in science. The results and discussion focus on factors that seem to characterize these lowest-achieving science students, and as such provide entry points to identifying these students and designing interventions for this particular group of students. Our approach focuses on the sizable proportion (over 35%) of Filipino students who have been assessed as demonstrating extremely low competencies (levels 1b and below) in science. The Philippine educational system does not lack programs for the more gifted students in science such as special science schools (Faustino and Hiwatig, 2012 ), competitions, scholarships, and other forms of support for students pursuing advanced studies and careers in science (De La Cruz, 2022 ). But there is not much that is documented about what is being done for the students like the 35% who are demonstrating extremely low levels of scientific literacy, even if they have progressed to the high school levels of the country’s formal education system. The first important implication of our findings is that these students need to be identified and understood before their science learnings can be addressed.

We should clarify that the characterization of poor-performing Filipino students in science should not be interpreted as the opposite characterization of better- or high-performing students. It is likely that there are qualitative differences between the experiences of poor and better science learners that are not captured by simply assuming a linear relationship between the factors that predict science learning. Indeed, if our machine learning approach was applied to identify the high-achieving students (i.e., Levels 4–6), it is likely that a different set of variables will be in the best machine learning model (and that can be explored in a different study). But by implication, the characterization of the poor-performing students in the results does not point to simple instructional or curricular interventions, and we do think there are some important policy implications that can be considered by stakeholders who are concerned with improving science education achievement among Filipino learners.

Instructional programs for poor achievers

Science educators have long noted that there are profound diversities in students of different ability levels, that simply assuming that one form of good teaching fits all types of learners is no longer tenable (Ault, 2010 ; Lynch, 2001 ; Yang et al., 2019 ). In this regard, the science education reform community of stakeholders should consider moving away from a one-size-teaching-fits-all approach that tends to be designed for students in the middle range of abilities using whole class instruction, and instead, move towards approaches that consider diverse adaptive learning approaches (Yang et al., 2019 ) and differentiated instruction (Pablico et al., 2017 ) that might be more responsive to (or at least that might not simply ignore) the needs of the low achievers.

Ensuring reading skills

There is a lot of evidence that good reading strategies and reading comprehension are strongly associated with science achievement (Cano et al., 2014 ; Caponera et al., 2016 ; Kolić-Vrhovec et al., 2011 ), but Filipino learners on average have extremely poor reading skills in English (Bernardo et al., 2021 ), which is the medium-of-instruction in science. Presumably, there are science learning activities that are more experiential and discovery-oriented and less dependent on students’ reading textbooks; but a previous study of students’ perception of science classes revealed a trend of decreasing science inquiry activities accompanied by an increase in self-learning, presumably involving reading textbooks and learning modules from Grade 5 to 10 (Bernardo et al., 2008 ). If Filipino science learners will be expected to do much of their learning through textbooks and learning modules in English, there should be strong efforts to strengthen the reading strategies and skills of Filipino learners.

Science in future professions and Philippine society

We interpreted part of the results as being associated with the view that science learning and achievement are irrelevant to higher future occupational aspirations. While these interpretations are speculative, there is probably a strong basis for the view that one does not need science to attain respectable occupations in the Philippines. There are many models of successful Filipino professionals and individuals who do not seem overtly display knowledge and use of science. In this regard, efforts to improve the science achievement of Filipino students might need to reckon with the perceived irrelevance of science in Philippine society. Scholars have problematized the lack of a science culture in the Philippines (Pertierra, 2004 ), perhaps vividly displayed in the recent COVID-19 pandemic, when there was widespread uncritical sharing of misinformation on vaccines, false cures, and other scientific matters through social media and social networks (Amit et al., 2022 ) and when scientific advice on pertinent issues was diluted and filtered before decisions were made by national leaders (Vallejo and Ong, 2020 ). Beyond schools, there should be efforts to change public perceptions of the importance of science in Filipinos’ social mobility and Filipino society’s development.

Improving school climate

The poor-performing students in science were identified by reports of a low sense of belonging in school and low perceived cooperation among students. These social experiences may be associated with lower achievement as they indicate a lack of meaningful sense of connectedness with students and teachers in the school, which is associated with lower engagement in the science classes, even if the social experiences are not specifically confined or referring to the science classes. The factors that contribute to these negative social experiences might vary across schools and communities and should be understood in proper contexts. Once the nature and causes of these social experiences are better understood, appropriate contextualized interventions can be developed.

Access to ICT devices and connectivity

Previous studies have documented how ICT availability and use positively predicted student achievement (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ), and similar results were also found in Filipino students’ achievement in reading (Bernardo et al., 2021 ), mathematics (Bernardo et al., 2022 ), and now in science. Together with improving access to the internet, there should be an effort to train teachers and students how to more effectively use the internet to deepen their learning of science concepts and processes, and in ways that adapt to students’ diverse abilities, interests, motivations, and circumstances (Yang et al., 2019 ).

Conclusions

Based on the assumption that science-for-all requires all Filipino citizens to acquire the scientific literacy required to effectively engage with and contribute to Philippine society in the 21st Century, we focused on the Filipino students with the lowest levels of science achievement in PISA 2018. We used machine learning to explore the variables that best identify the poor-performing Filipino students, as these variables could be used to better track and understand their learning needs. Our study points to a cluster of variables related to the student’s reading strategies, occupational aspirations, social experiences in school, and access to ITC and the internet. The variables depart from the typical focus of reform efforts on teachers’ competencies, curriculum, and instruction. But if we truly want to improve Filipino students’ science literacy, we need to understand the experiences of students who are failing to do so, as these point to problems that need to be addressed in their learning experiences in Philippine schools.

Data availability

The data analyzed in this study are available on the PISA 2018 Database page on the website of the Organisation for Economic Co-operation and Development at https://www.oecd.org/pisa/data/2018database/ , accessed on 17 Feb 2020.

For completeness, we also conducted a SHAP analysis for the best algorithm for each of the other machine learning approaches. A comparative summary of the top 15 variables that feature in the prediction models is shown in Supplementary File.

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Acknowledgements

This research was funded by a grant to the third author from the De La Salle University-Angelo King Institute for Economic and Business Studies (AKI Research Grants 2020–2021 Project No. 500-139), and a Research Fellowship to the first author from the National Academy of Science and Technology, Philippines.

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Conceptualization: ABIB, MOC II, MOC; Machine learning methodology, MOC II; Machine learning modeling and evaluation: JMMT; Data preprocessing and feature engineering: SAY; Data visualization: UCC; Writing—original draft preparation, review, and editing: ABIB, MOC II, MOC; Project administration: MOC II, MOC; Funding acquisition: MOC, MOC II, ABIB.

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Bernardo, A.B.I., Cordel, M.O., Calleja, M.O. et al. Profiling low-proficiency science students in the Philippines using machine learning. Humanit Soc Sci Commun 10 , 192 (2023). https://doi.org/10.1057/s41599-023-01705-y

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This report gives an overview of the current situation of science education in the Philippines. In addition, this features the requirements and initiatives done by the government in terms of keeping up its science education program with the changes brought by the twenty-first century. The science curriculum in the Philippines was implemented to produce scientifically literate individuals who are responsible decision makers and can apply scientific knowledge to look for solutions to problems of the community. However, in the latest results of Programme for International Student Assessment (PISA) 2018, the Philippines ranked last among participating countries in which Science was one was one of the subjects tested. The results of the first participation of the country in PISA has paved the way for the Department of Education to propose more programs to address the deficient academic performance and advance the quality of education in the Philippines. Furthermore, the rapid change brought by Industry 4.0 also brings challenge to science education implementers for the country needs to ensure that it can adapt with the emerging technologies like A.I. and Robotics. Several initiatives were done by the Department of Science and Technology and Science Education Institute through their responsive and tailor-fit programs and projects.

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Acknowledgements

I want to thank our God Almighty for giving me wisdom and perseverance to write this report. I also want to extend my gratitude to Prof. Lee Shok Mee of Penang, Malaysia for this opportunity. To my wife Cara, thank you for the love and support that you have given me in the writing process. To my friend, Mr. Romnick P. Nicolas, thank you for proofreading this paper.

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De La Cruz, R.J.D. (2022). Science Education in the Philippines. In: Huang, R., et al. Science Education in Countries Along the Belt & Road. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-6955-2_20

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Here Are the Best Projects of the 2019 National Science & Technology Fair

Science fair winners vie for the Gokongwei Brothers Foundation Young Scientist Award

After reviewing 51 entries by 93 Grade 7 to 12 student-researchers from public and private schools nationwide, the Department of Education has announced the Best Six Projects of the recently concluded 2019 National Science and Technology Fair (NSTF), sponsored by the Gokongwei Brothers Foundation (GBF).

The said projects, representing the work of 12 high school student-researches, were selected from the winners of the individual and team competitions under the Physical Science, Life Science, and Robotics and Intelligent Machines categories. They’re now one step closer to having the chance of representing the Philippines at this year’s Intel International Science and Engineering Fair (ISEF) and receiving the Gokongwei Brothers Foundation Young Scientist Award. GBF will support the Philippine delegation with an all-expenses-paid trip to the competition, billed as the Olympics of high school science fairs, to be held from May 12 to 17 in Phoenix, Arizona.

“The goal of the Gokongwei Brothers Young Scientist Award is to respond to your call for opportunities and to recognize our globally competitive young scientists who, amid adversity and the challenges in our country, did not use that as a stumbling block to achieve great work and excellence in STEM [Science, Technology, Engineering, and Mathematics]. I think that represents not only great intelligence but strength of character as well,” shared GBF Executive Director Grace Colet during the NSTF 2019 closing ceremony on February 21.

In an earlier statement, GBF said that it is honored to support future leaders in the STEM field among today’s young Filipino science researchers and innovators in K to 12. It should be noted that in most developed countries, the growth of STEM occupations outpaces those of non-STEM careers. And as the Philippine economy grows, increased value will be placed on employees or workers with solid STEM backgrounds. In theory, STEM education trains people to be adept in problem solving, increases their science literacy, and enables the generation of new ideas, products and processes—all crucial for the continued growth of our economy. With GBF’s support, the importance of STEM is inculcated in the hearts and minds of the bright young student-researchers at the NSTF.  

Among those vying to represent the country at Intel ISEF 2019 is Grade 10 student Maryjoise Karla Buan from Pangasinan National High School who hopes to save the environment one tree at a time. When visiting her father who works at a powerplant in Sual, Pangasinan, Karla could have had a view of green mountainscapes, but she is only ever greeted by barren lands resulting from illegal logging and other destructive practices in the forest area.

With three thousand pesos, Karla developed an alerting device that can detect possible signs of illegal logging and kaingin (slash-and-burn farming). Equipped with sound, flame, and smoke sensors, the device is programmed to notify authorities and first responders such as forest rangers when the sound of a chainsaw or signs of a fire are detected. Karla’s project was recognized as NSTF 2019’s best individual research in Robotics and Intelligent Machines category.

In the team competition, the winning entry from Cagayan National High School hopes to help farmers minimize risks and increase their rate of production with an automated temperature and monitoring system for drying and storing Philippine rice varieties. Alpha Acain, Lester Sabadao, and Lia Denise Tan designed a prototype of a post-harvest equipment for local rice farmers who are still heavily dependent on traditional sun-drying and “kamalig” storage methods. The team offers an innovative and affordable solution for farmers to prevent capital losses due to crop spoilage and increase the quality of harvest.

Like the Intel ISEF hopefuls from Cagayan Valley, Nathaniel Reyes of Quezon National High School hopes to benefit his community through a proposed solution to harmful algal blooms infesting aquaculture ponds in Quezon Province. NSTF 2019’s best individual research in Physical Science focused on how charcoal made out of cornstalk, an abundant waste material in Nathaniel’s community, can be used to treat the algae infestation that leads to shellfish contamination and loss of income among owners of lakes and fish ponds.

Meanwhile, the winning Physical Science research in the team competition is a potential gamechanger in the areas of muffling systems, automotive interior technology, and wall linings with their proposed cost-effective acoustic panels made from Philippine natural fibers. Neil David Cayanan, Shaira Gozun, and E’van Relle Tongol of Angeles City National Science High School used biomass materials from abaca, bamboo, and water hyacinth to produce eco-friendly sound absorption materials.

The best individual research in the Life Science category also looked into an abundant species in the Philippines, the “Jamaican cherry,” locally known as “aratiles.” Maria Isabel Layson of Iloilo National High School studied the antioxidant activity and antidiabetic properties of aratiles and its possible benefits to people with diabetes or obesity. With results suggesting practical application toward prevention of type 2 diabetes, the research suggests the Philippines could carve a niche in the organized development of products from aratiles, such as dietary supplements.

On the other hand, the Life Science research in the team competition studied a possible organic solution to fungus-caused brown eye spot disease attacking Kapeng Barako plants, one of the main threats to Cavite farmers. A perennial problem among Philippine coffee farmers, the disease is a hurdle in meeting the soaring local and international demand for the elusive coffee variety. Taguig Science High School’s John Eric Aggarao, Kathleen Chloie Antonio, and Anna Beatriz Suavengco isolated bacteria from Kapeng Barako leaves and used these as biocontrol agents to manage the disease. The results show this approach has a higher success rate versus commercial antifungal drugs in treating the disease.

The proponents of the Best 6 Projects will undergo a rigorous review and a boot camp by DepEd to ensure alignment with international standards. After which, the final delegates to the 2019 Intel International Science and Engineering Fair (ISEF) will be selected and recognized as the pioneer recipients of the Gokongwei Brothers Foundation Young Scientist Award.

At the NSTF’s closing ceremonies, GBF awarded medals to the proponents of the best six projects and announced its commitment to support the final representatives to this year’s Intel ISEF.

“I really feel hopeful about our future when I hear all of you, [especially] when you defend your projects. In our own little way, we hope to be part of the challenge of raising our future scientists, especially after seeing how much we need to do to give you more opportunities and more mentoring. We, at Gokongwei Brothers Foundation, are committed to doing that with DepEd and all of you,” Colet said.

In her remarks, DepEd’s Bureau of Curriculum Development Director and NSTF Director Jocelyn Andaya acknowledged GBF’s sponsorship of NSTF 2019. “I wish to profusely thank Gokongwei Brothers Foundation, who did not hesitate to support this endeavor and committed to continue to support this in the future,” Andaya said.

For more information on the Gokongwei Brothers Foundation, please visit www.jgsummit.com.ph/gokongwei-brothers-foundation . You may also call (02) 451-8888 loc. 1118 or email [email protected]

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PH students win at International Students Science Fair

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This is AI generated summarization, which may have errors. For context, always refer to the full article.

MANILA, Philippines – A team from the Philippine Science High School (PSHS)  won at the 10th  International Students Science Fair (ISSF) recently held in Moscow, Russia, the Department of Foreign Affairs said on Friday, September 5.

PHSH students Adrielle Theresa DL. Cusi, John Christian G. Nacpil, and Lizbeth Joy G. Tan were cited for their research entitled, “Development of a Leap Motion Controller-based Program for Finger Range-of-Motion Measurement.”

Their research was one of the 3 projects awarded as “Most Creative and Innovative”  in the Biotechnology, Health, and Medicine cluster.  The DFA said a total of 23 projects from different countries competed in the category.

Delegates from 25 countries presented a total of 120 projects in both poster exhibitions and roundtable discussions. The projects were divided into 5 different clusters: Biotechnology, Health, and Medicine; Energy, Engineering and Robotics; Information Technology and Telecommunications; and Sustainability and Environmental Science. 

The  annual ISSF aims to facilitate networking and information-sharing among students and teachers from various countries in the fields of science, technology, engineering, and mathematics (STEM).

This year’s ISSF was  was hosted by the Moscow Chemical Lyceum #3103 (MCL) in collaboration with the Lift to the Future organization and the People’s Friendship University of Russia.  – Rappler.com

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