Development and validation of Science Instructional Materials (SIMs) for the least learned competencies

https://doi.org/10.35912/jshe.v5i3.2735
Published: May 20, 2025

Abstract:

Purpose: This study focused on developing and evaluating Science Instructional Materials (SIMs) to address the least-learned competencies among Grade 8 students, providing structured resources for learners struggling with key scientific concepts.

Research methodology: A research and development approach was employed, identifying the least learned competencies through summative assessments. The SIMs were designed based on these competencies and evaluated by science teachers, master teachers, and Learning Resource Management and Development System (LRMDS) members. The evaluation criteria included content quality, format, presentation, organization, and accuracy, with statistical analysis to examine differences in ratings.

Results: The SIMs received high ratings across all evaluation categories, with no significant differences in the assessments from science teachers, Master Teachers, and LRMDS members, indicating broad acceptance and reliability.

Conclusions: The findings showed that SIMs were highly rated, aligning well with instructional standards and classroom needs. While there were significant differences in ratings among evaluator groups, the overall acceptability was high, suggesting that the SIMs effectively addressed learning challenges, particularly in physics. Their structure, clarity, and adherence to curriculum standards were affirmed. Further validation is required in diverse educational contexts.

Limitations: This study was limited to a single public secondary school, which may affect the generalizability of the findings. Additional validation in various educational settings is needed.

Contribution: This study provides an evidence-based approach for developing instructional materials in science education, focusing on addressing learning gaps and supporting competency development.

Novelty: This study introduces SIMs specifically designed to enhance competencies identified as least learned, validated through expert evaluation aligned with curriculum standards.

Keywords:
1. Grade 8 Science
2. Learning Resource Validation
3. Least Learned Competencies
4. Research and Development
5. Science Instructional Materials
Authors:
1 . Rakma Macalikod
Mindanao State University – General Santos City, Philippines
2 . Ebrahim Alpe Simpal
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How to Cite
Macalikod, R., & Simpal, E. A. . (2025). Development and validation of Science Instructional Materials (SIMs) for the least learned competencies. Journal of Social, Humanity, and Education, 5(3), 195–209. https://doi.org/10.35912/jshe.v5i3.2735
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Vol. 5 No. 3 (2025): May
Articles
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References

    Al-Adwan, A. S., Nofal, M., Akram, H., Albelbisi, N. A., & Al-Okaily, M. (2022). Towards a Sustainable Adoption of E-Learning Systems: The Role of Self-Directed Learning. Journal of Information Technology Education: Research, 21. doi:https://doi.org/10.28945/4980

    Alabi, M. (2024). Visual Learning: The Power of Visual Aids and Multimedia: October.

    Alinsunurin, J. (2021). Unpacking underperformance: Learning mindsets and the challenge of academic achievement among Filipino students. AIM RSN PCC Discussion Paper, 4. doi:http://dx.doi.org/10.2139/ssrn.3867956

    Baron, J. (2022). HIV/AIDS Awareness and the Level of Sexual Risk Behaviors Among Senior High School Students: An Evaluation. Journal of Social, Humanity, and Education, 3(1), 43-55. doi:https://doi.org/10.35912/jshe.v3i1.1145

    Baron, J. V. (2023). Blackboard system and students’ academic performance: an experimental study in the Philippines. Journal of Social, Humanity, and Education, 3(3), 173-184. doi:https://doi.org/10.35912/jshe.v3i3.1186

    Baron, J. V., & Cruz, J. A. D. (2023). The spiral progression approach in teaching science: Its Volatilities, Uncertainties, Complexities, and Ambiguities (VUCA). Journal of Social, Humanity, and Education, 3(2), 89-103. doi:https://doi.org/10.35912/jshe.v3i2.1194

    Baron, J. V., & Robles, A. C. M. O. (2023). Structural equation model: Organizational performance among state universities and colleges in Philippines. Journal of Social, Humanity, and Education, 3(4), 307-320. doi:https://doi.org/10.35912/jshe.v3i4.1505

    Bonitez, A. G. (2021). Effectiveness of Science Strategic Intervention Material in Elevating the Performance Level of Grade Seven Students. International Journal of Advanced Research in Education and Society, 3(2), 18-31.

    Calo, J. R., & De Vera, M. (2025). The quality of science education: Viewpoints of secondary school science teachers. Journal of Research in Education and Pedagogy, 2(1), 95-109. doi:https://doi.org/10.70232/jrep.v2i1.26

    Clark, R. C., & Mayer, R. E. (2023). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning: john Wiley & sons.

    Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of science education and technology, 32(3), 444-452. doi:https://doi.org/10.1007/s10956-023-10039-y

    Fitrianto, I., & Saif, A. (2024). The role of virtual reality in enhancing Experiential Learning: a comparative study of traditional and immersive learning environments. International Journal of Post Axial: Futuristic Teaching and Learning, 97-110. doi:https://doi.org/10.59944/postaxial.v2i2.300

    Ghimire, S. (2024). Hands-on pedagogies in science classrooms: exploring Nepali teachers’ perspectives on learning kits and inquiry-based science learning. University of British Columbia.

    Groenewald, E., Kilag, O., Unabia, R., Manubag, M., Zamora, M., & Repuela, D. (2023). The dynamics of problem-based learning: A study on its impact on social science learning outcomes and student interest. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(6), 303-313.

    Haas, A., Januszyk, R., Grapin, S. E., Goggins, M., Llosa, L., & Lee, O. (2021). Developing instructional materials aligned to the next generation science standards for all students, including English learners. Journal of Science Teacher Education, 32(7), 735-756. doi:https://doi.org/10.1080/1046560X.2020.1827190

    Irasuti, I., & Bachtiar, B. (2024). Empowering indonesian efl teachers: the transformative impact of visual literacy training on teaching materials. International Journal of Learning, Teaching and Educational Research, 23(8), 116-136. doi:https://doi.org/10.26803/ijlter.23.8.7

    Janoušková, S., Pyskatá Rathouská, L., Žák, V., & Urválková, E. S. (2023). The scientific thinking and reasoning framework and its applicability to manufacturing and services firms in natural sciences. Research in Science & Technological Education, 41(2), 653-674. doi:https://doi.org/10.1080/02635143.2021.1928048

    Karunarathna, I., Gunasena, P., De Alvis, K., & Jayawardana, A. (2024). Structured reviews: Organizing, synthesizing, and analyzing scientific literature. Retrieved from ResearchGate.

    Lapinid, M. R. C., Cordel II, M. O., Teves, J. M. M., Yap, S. A., Chua, U. C., & Bernardo, A. B. (2022). Which Filipino students are being left behind in mathematics? Testing machine learning models to differentiate lowest-performing filipino students in public and private schools in the 2018 PISA mathematics test.

    Ligado, F., Guray, N. D., & Bautista, R. G. (2022). Pedagogical beliefs, techniques, and practices towards hands-on science. American Journal of Educational Research, 10(10), 584-591. doi:https://doi.org/10.12691/education-10-10-1

    Maier, U., & Klotz, C. (2022). Personalized feedback in digital learning environments: Classification framework and literature review. Computers and Education: Artificial Intelligence, 3, 100080. doi:https://doi.org/10.1016/j.caeai.2022.100080

    Marougkas, A., Troussas, C., Krouska, A., & Sgouropoulou, C. (2023). Virtual reality in education: a review of learning theories, approaches and methodologies for the last decade. Electronics, 12(13), 2832. doi:https://doi.org/10.3390/electronics12132832

    Molin, F., De Bruin, A., & Haelermans, C. (2022). A conceptual framework to understand learning through formative assessments with student response systems: The role of prompts and diagnostic cues. Social Sciences & Humanities Open, 6(1), 100323. doi:https://doi.org/10.1016/j.ssaho.2022.100323

    Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self?regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328-1353. doi:https://doi.org/10.1111/bjet.13454

    Nicholson, E. C. (2021). Factors associated with safe medication administration in specified residential facilities for older persons within the Metro-North, Western Cape Province. Stellenbosch: Stellenbosch University.

    Porat, E., Shamir?Inbal, T., & Blau, I. (2023). Teaching prototypes and pedagogical strategies in integrating Open Sim?based virtual worlds in K?12: Insights from perspectives and practices of teachers and students. Journal of Computer Assisted Learning, 39(4), 1141-1153. doi:https://doi.org/10.1111/jcal.12786

    Rachma, N., & Muhlas, I. (2022). Comparison of waterfall and prototyping models in research and development (r&d) methods for android-based learning application design. Jurnal Inovatif: Inovasi Teknologi Informasi Dan Informatika, 5(1), 36-39. doi:https://doi.org/10.32832/inova-tif.v5i1.7927

    Ramdani, A., Jufri, A., Gunawan, G., Fahrurrozi, M., & Yustiqvar, M. (2021). Analysis of students' critical thinking skills in terms of gender using science teaching materials based on the 5E learning cycle integrated with local wisdom. Jurnal Pendidikan IPA Indonesia, 10(2), 187-199. doi:https://doi.org/10.15294/jpii.v10i2.29956

    Rianti, R., Gunawan, G., Verawati, N. N. S. P., & Taufik, M. (2024). The Effect of Problem Based Learning Model Assisted by PhET Simulation on Understanding Physics Concepts. Lensa: Jurnal Kependidikan Fisika, 12(1), 28-43. doi:https://doi.org/10.33394/j-lkf.v12i1.8783

    Robinson, R. S. (2024). Purposive sampling Encyclopedia of quality of life and well-being research (pp. 5645-5647): Springer.

    Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), 11524. doi:https://doi.org/10.3390/su151511524

    Santoso, H., & Putra, P. H. (2021). Development & evaluation of e-learning module based on visual and global preferences using a user-centered design approach. International Journal of Emerging Technologies in Learning (iJET), 16(15), 139-151.

    Simpal, E. A., & Robles, A. C. (2024). Education 4.0: Awareness, Readiness, and Digital Competence of Higher Education Institutions (HEIs) Faculty in Region XII. Readiness, and Digital Competence of Higher Education Institutions (HEIs) Faculty in Region XII (November 10, 2024).

    Smith, K., Maynard, N., Berry, A., Stephenson, T., Spiteri, T., Corrigan, D., . . . Smith, T. (2022). Principles of problem-based learning (PBL) in STEM education: Using expert wisdom and research to frame educational practice. Education Sciences, 12(10), 728. doi:https://doi.org/10.3390/educsci12100728

    Tomlinson, C. A. (2023). The parallel curriculum model: A design to develop potential & challenge high-ability learners Systems and models for developing programs for the gifted and talented (pp. 571-598): Routledge.

    Wang, E., Tuma, A. P., Doan, S., Henry, D., Lawrence, R., Woo, A., & Kaufman, J. H. (2021). Teachers’ perceptions of what makes instructional materials engaging, appropriatel y challenging, and usable. RAND Corporation.

    Wijaya, H., Maryanti, R., Wulandary, V., & Irawan, A. R. (2022). Numerical minimum competence assessment for increasing students’ interest in mathematics. ASEAN Journal of Science and Engineering Education, 2(3), 183-192. doi:https://doi.org/10.17509/ajsee.v2i3.38660

    Zhai, X. (2023). ChatGPT for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46. doi:https://doi.org/10.1145/3589649

  1. Al-Adwan, A. S., Nofal, M., Akram, H., Albelbisi, N. A., & Al-Okaily, M. (2022). Towards a Sustainable Adoption of E-Learning Systems: The Role of Self-Directed Learning. Journal of Information Technology Education: Research, 21. doi:https://doi.org/10.28945/4980
  2. Alabi, M. (2024). Visual Learning: The Power of Visual Aids and Multimedia: October.
  3. Alinsunurin, J. (2021). Unpacking underperformance: Learning mindsets and the challenge of academic achievement among Filipino students. AIM RSN PCC Discussion Paper, 4. doi:http://dx.doi.org/10.2139/ssrn.3867956
  4. Baron, J. (2022). HIV/AIDS Awareness and the Level of Sexual Risk Behaviors Among Senior High School Students: An Evaluation. Journal of Social, Humanity, and Education, 3(1), 43-55. doi:https://doi.org/10.35912/jshe.v3i1.1145
  5. Baron, J. V. (2023). Blackboard system and students’ academic performance: an experimental study in the Philippines. Journal of Social, Humanity, and Education, 3(3), 173-184. doi:https://doi.org/10.35912/jshe.v3i3.1186
  6. Baron, J. V., & Cruz, J. A. D. (2023). The spiral progression approach in teaching science: Its Volatilities, Uncertainties, Complexities, and Ambiguities (VUCA). Journal of Social, Humanity, and Education, 3(2), 89-103. doi:https://doi.org/10.35912/jshe.v3i2.1194
  7. Baron, J. V., & Robles, A. C. M. O. (2023). Structural equation model: Organizational performance among state universities and colleges in Philippines. Journal of Social, Humanity, and Education, 3(4), 307-320. doi:https://doi.org/10.35912/jshe.v3i4.1505
  8. Bonitez, A. G. (2021). Effectiveness of Science Strategic Intervention Material in Elevating the Performance Level of Grade Seven Students. International Journal of Advanced Research in Education and Society, 3(2), 18-31.
  9. Calo, J. R., & De Vera, M. (2025). The quality of science education: Viewpoints of secondary school science teachers. Journal of Research in Education and Pedagogy, 2(1), 95-109. doi:https://doi.org/10.70232/jrep.v2i1.26
  10. Clark, R. C., & Mayer, R. E. (2023). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning: john Wiley & sons.
  11. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of science education and technology, 32(3), 444-452. doi:https://doi.org/10.1007/s10956-023-10039-y
  12. Fitrianto, I., & Saif, A. (2024). The role of virtual reality in enhancing Experiential Learning: a comparative study of traditional and immersive learning environments. International Journal of Post Axial: Futuristic Teaching and Learning, 97-110. doi:https://doi.org/10.59944/postaxial.v2i2.300
  13. Ghimire, S. (2024). Hands-on pedagogies in science classrooms: exploring Nepali teachers’ perspectives on learning kits and inquiry-based science learning. University of British Columbia.
  14. Groenewald, E., Kilag, O., Unabia, R., Manubag, M., Zamora, M., & Repuela, D. (2023). The dynamics of problem-based learning: A study on its impact on social science learning outcomes and student interest. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(6), 303-313.
  15. Haas, A., Januszyk, R., Grapin, S. E., Goggins, M., Llosa, L., & Lee, O. (2021). Developing instructional materials aligned to the next generation science standards for all students, including English learners. Journal of Science Teacher Education, 32(7), 735-756. doi:https://doi.org/10.1080/1046560X.2020.1827190
  16. Irasuti, I., & Bachtiar, B. (2024). Empowering indonesian efl teachers: the transformative impact of visual literacy training on teaching materials. International Journal of Learning, Teaching and Educational Research, 23(8), 116-136. doi:https://doi.org/10.26803/ijlter.23.8.7
  17. Janoušková, S., Pyskatá Rathouská, L., Žák, V., & Urválková, E. S. (2023). The scientific thinking and reasoning framework and its applicability to manufacturing and services firms in natural sciences. Research in Science & Technological Education, 41(2), 653-674. doi:https://doi.org/10.1080/02635143.2021.1928048
  18. Karunarathna, I., Gunasena, P., De Alvis, K., & Jayawardana, A. (2024). Structured reviews: Organizing, synthesizing, and analyzing scientific literature. Retrieved from ResearchGate.
  19. Lapinid, M. R. C., Cordel II, M. O., Teves, J. M. M., Yap, S. A., Chua, U. C., & Bernardo, A. B. (2022). Which Filipino students are being left behind in mathematics? Testing machine learning models to differentiate lowest-performing filipino students in public and private schools in the 2018 PISA mathematics test.
  20. Ligado, F., Guray, N. D., & Bautista, R. G. (2022). Pedagogical beliefs, techniques, and practices towards hands-on science. American Journal of Educational Research, 10(10), 584-591. doi:https://doi.org/10.12691/education-10-10-1
  21. Maier, U., & Klotz, C. (2022). Personalized feedback in digital learning environments: Classification framework and literature review. Computers and Education: Artificial Intelligence, 3, 100080. doi:https://doi.org/10.1016/j.caeai.2022.100080
  22. Marougkas, A., Troussas, C., Krouska, A., & Sgouropoulou, C. (2023). Virtual reality in education: a review of learning theories, approaches and methodologies for the last decade. Electronics, 12(13), 2832. doi:https://doi.org/10.3390/electronics12132832
  23. Molin, F., De Bruin, A., & Haelermans, C. (2022). A conceptual framework to understand learning through formative assessments with student response systems: The role of prompts and diagnostic cues. Social Sciences & Humanities Open, 6(1), 100323. doi:https://doi.org/10.1016/j.ssaho.2022.100323
  24. Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self?regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328-1353. doi:https://doi.org/10.1111/bjet.13454
  25. Nicholson, E. C. (2021). Factors associated with safe medication administration in specified residential facilities for older persons within the Metro-North, Western Cape Province. Stellenbosch: Stellenbosch University.
  26. Porat, E., Shamir?Inbal, T., & Blau, I. (2023). Teaching prototypes and pedagogical strategies in integrating Open Sim?based virtual worlds in K?12: Insights from perspectives and practices of teachers and students. Journal of Computer Assisted Learning, 39(4), 1141-1153. doi:https://doi.org/10.1111/jcal.12786
  27. Rachma, N., & Muhlas, I. (2022). Comparison of waterfall and prototyping models in research and development (r&d) methods for android-based learning application design. Jurnal Inovatif: Inovasi Teknologi Informasi Dan Informatika, 5(1), 36-39. doi:https://doi.org/10.32832/inova-tif.v5i1.7927
  28. Ramdani, A., Jufri, A., Gunawan, G., Fahrurrozi, M., & Yustiqvar, M. (2021). Analysis of students' critical thinking skills in terms of gender using science teaching materials based on the 5E learning cycle integrated with local wisdom. Jurnal Pendidikan IPA Indonesia, 10(2), 187-199. doi:https://doi.org/10.15294/jpii.v10i2.29956
  29. Rianti, R., Gunawan, G., Verawati, N. N. S. P., & Taufik, M. (2024). The Effect of Problem Based Learning Model Assisted by PhET Simulation on Understanding Physics Concepts. Lensa: Jurnal Kependidikan Fisika, 12(1), 28-43. doi:https://doi.org/10.33394/j-lkf.v12i1.8783
  30. Robinson, R. S. (2024). Purposive sampling Encyclopedia of quality of life and well-being research (pp. 5645-5647): Springer.
  31. Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), 11524. doi:https://doi.org/10.3390/su151511524
  32. Santoso, H., & Putra, P. H. (2021). Development & evaluation of e-learning module based on visual and global preferences using a user-centered design approach. International Journal of Emerging Technologies in Learning (iJET), 16(15), 139-151.
  33. Simpal, E. A., & Robles, A. C. (2024). Education 4.0: Awareness, Readiness, and Digital Competence of Higher Education Institutions (HEIs) Faculty in Region XII. Readiness, and Digital Competence of Higher Education Institutions (HEIs) Faculty in Region XII (November 10, 2024).
  34. Smith, K., Maynard, N., Berry, A., Stephenson, T., Spiteri, T., Corrigan, D., . . . Smith, T. (2022). Principles of problem-based learning (PBL) in STEM education: Using expert wisdom and research to frame educational practice. Education Sciences, 12(10), 728. doi:https://doi.org/10.3390/educsci12100728
  35. Tomlinson, C. A. (2023). The parallel curriculum model: A design to develop potential & challenge high-ability learners Systems and models for developing programs for the gifted and talented (pp. 571-598): Routledge.
  36. Wang, E., Tuma, A. P., Doan, S., Henry, D., Lawrence, R., Woo, A., & Kaufman, J. H. (2021). Teachers’ perceptions of what makes instructional materials engaging, appropriatel y challenging, and usable. RAND Corporation.
  37. Wijaya, H., Maryanti, R., Wulandary, V., & Irawan, A. R. (2022). Numerical minimum competence assessment for increasing students’ interest in mathematics. ASEAN Journal of Science and Engineering Education, 2(3), 183-192. doi:https://doi.org/10.17509/ajsee.v2i3.38660
  38. Zhai, X. (2023). ChatGPT for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46. doi:https://doi.org/10.1145/3589649