Rima International Journal of Education (RIJE)

ISSN: 2756 – 6749(print); 3141-2033(online)

Rima International Journal of Education (RIJE)

Delving Deeper into Chemistry Education: Understanding How Students Learn and How to Teach Effectively

*1Hassan Aliyu, 2Amina M. Chado, 3Umar Idris Sarkin Bauchi, 4L. A. Fadipe and 5Corrienna Abdul Talib

*1Department of Science Education, Faculty of Education, Sokoto State University (SSU), Sokoto. Email: nagoronyo@gmail.com & aliyu.hassan@ssu.edu.ng ORCID: https://orcid.org/0000-0003-4929-3126

2&3Department of Science Education, School of Science and Technology Education (SSTE), Federal University of Technology Minna (FUTMinna), Niger State.

4Department of chemistry, School of Physical Science, Federal University of Technology Minna (FUTMinna), Niger State.

5Department of science, technology, mathematics and creative multimedia, Faculty of social science and humanities, Universiti Teknologi Malaysia (UTM), Johor, Malaysia

Cite this as: Aliyu, H., Chado,A. M., Idris, U. S. B., Fadipe, L. A. & Talib, C. A. (2026). Delving Deeper into Chemistry Education: Understanding How Students Learn and How to Teach Effectively. Rima International Journal of Education, 5(2), 221—245. DOI: https://doi.org/10.65760/rijessu.v5.2.16

Abstract

Chemistry education directly influences workforce preparation for energy, medicine, and materials science. However, persistent difficulties in mastering abstract concepts such as thermodynamics and molecular interactions limit student success, with attrition rates exceeding 30% at many institutions. The cognitive mechanisms underlying these failures remain poorly specified, and instructional strategies that work in one context often fail in resource‑constrained settings. Here we show that a 10‑week intervention combining scenario‑based problem solving with molecular visualization software significantly improves conceptual mastery. In a controlled trial with 324 preservice teachers, the experimental group achieved adjusted post‑test scores 2.53 points higher than the control group (ANCOVA, F(1,318)=37.4, p<0.001, η²=0.105). Gains concentrated on the most difficult concepts: pre‑test correct rates below 8% for thermodynamics and intermolecular forces rose substantially in the experimental condition. Despite this improvement, over 57% of students never participated in collaborative problem‑solving, and only 19.9% rated collaboration as highly effective compared to 71.5% for simulations. We conclude that representational bottlenecking, not general ability, drives chemistry learning failures, and that cognitive‑conflict pedagogy targeting specific conceptual barriers produces measurable gains even in technology‑limited environments.

Keywords

Chemistry education, Representational fluency, Cognitive conflict, Active learning, Sub‑Saharan Africa

Reference

Adegoke, B. A., & Oladele, I. T. (2023). Collaborative learning practices in Nigerian university chemistry classrooms: A survey of six institutions. African Journal of Chemical Education, 13(2), 45–62.

Aikenhead, G. S., & Ogawa, M. (2023). Indigenous knowledge and science education: Restructuring the scientific context. Cultural Studies of Science Education, 18(1), 112–130.

Aliyu, H. (2025). A Review of Instructional Strategies for Maximizing the Effectiveness of PhET Interactive Simulations in Chemistry Education. Rima International Journal of education4(1), 479-487.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Broman, K., Bernhard, J., & Pettersson, A. J. (2022). Immediate usefulness versus long-term retention: Student preferences for simulations and collaboration in chemistry. Nordic Studies in Science Education, 18(3), 234–250.

Burton, B. N., Bonner, T., Faloye, A. O., Bradley, S. A., Warner, D. O., Pittet, J. F., … & Milam, A. J. (2024). Exploring the potential of evidence-based practice on mitigating health care disparities. Anesthesia & Analgesia139(5), 1106-1111.

Connor, M. C., & Raker, J. R. (2024). Factors associated with chemistry faculty members’ cooperative adoption of evidence-based instructional practices: results from a national survey. Chemistry Education Research and Practice25(3), 625-642.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.

diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2–3), 105–225.

Fink, A., Frey, R. F., & Solomon, E. D. (2020). Belonging in general chemistry predicts first-year undergraduates’ performance and attrition. Chemistry Education Research and Practice21(4), 1042-1062.

Freeman, S., Alston, S., & Wenderoth, M. P. (2023). Simulation-enhanced inquiry versus lecture: Effects on particulate reasoning in introductory chemistry. CBE—Life Sciences Education, 22(2), ar18.

Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Information Age Publishing.

Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M. C. (2010). Connecting high school physics experiences to outcome expectations in college. Journal of Research in Science Teaching, 47(8), 978–1003.

Idsardi, R. (2020). Evidence-based practices for the active learning classroom. In Active learning in college science: The case for evidence-based practice (pp. 13-25). Cham: Springer International Publishing.

Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Chemical Education, 68(9), 701–703.

Kukulu, K., & Sarac, S. (2018). Development of the Chemistry Self-Efficacy Scale for university students. Journal of Psychoeducational Assessment, 36(5), 512–525.

Leppink, J., Paas, F., van der Vleuten, C. P. M., van Gog, T., & van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.

Mulford, D. R., & Robinson, W. R. (2002). An inventory for alternate conceptions among first-semester general chemistry students. Journal of Chemical Education, 79(6), 739–744.

Nneji, C. C., & Okonkwo, F. A. (2024). Low-tech collaborative problem-solving in Nigerian secondary chemistry: Scripted protocols without digital tools. International Journal of Science Education, 46(3), 298–317.

Okonkwo, E. N., & Adeniyi, T. O. (2025). Network reliability and simulation-dependent pedagogy in Nigerian universities: A multi-site study. Education and Information Technologies, 30(1), 87–104.

Pande, P. (2021). Learning and expertise with scientific external representations: an embodied and extended cognition model. Phenomenology and the Cognitive Sciences20(3), 463-482.

Reinholz, D. L., & Apkarian, N. (2024). Collaborative learning frequency in introductory STEM courses: National norms and trends. International Journal of STEM Education, 11(1), 25–42.

Santos, F. M., Ribeiro, T. C., & Oliveira, L. A. (2024). Structured metacognitive prompts in chemistry formative assessment: A Brazilian classroom study. Revista de Educação em Ciências, 19(2), 145–163.

Sevian, H., & Talanquer, V. (2022). Spaced opportunities for mapping macroscopic observations to submicroscopic mechanisms in chemistry learning. Journal of Research in Science Teaching, 59(6), 1023–1048.