Computational Chemistry Software for Developing Deep Conceptual Understanding among Pre-Service Chemistry Teachers

Teguh Wibowo (Department of Chemistry Education, Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang, Semarang, 50185)
Ariyatun Ariyatun (Department of Chemistry Education, Faculty of Mathematics and Natural Science, Universitas Negeri Jakarta, Jakarta Timur, 13220)
Annisa Adiwena Putri (Department of Chemistry, Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang, Semarang, 50185)
Ervin Tri Suryandari (Department of Chemistry, Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang, Semarang, 50185)
Azlan Bin Kamari (Department of Chemistry Education, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, 35900)
Jajang Muhariyansah (Graduate Institute of Science Education, College of Science, National Taiwan Normal University, Taipei, 106308)

Jurnal Tadris Kimiya
Vol 11 No 1 (2026)

Developing students’ deep conceptual understanding remains a persistent challenge in chemistry education because many chemical phenomena involve abstract molecular-level representations that are difficult to visualize through conventional instruction. Although computational chemistry software (CCS) has been widely used in scientific research, empirical evidence regarding its effectiveness in promoting meaningful conceptual understanding in undergraduate chemistry education remains limited. This study investigated the effectiveness of CCS in enhancing chemistry education students’ deep conceptual understanding. A quasi-experimental nonequivalent control group design was employed involving undergraduate chemistry education students assigned to experimental and control groups. The experimental group learned with computational chemistry software (Gaussian, Avogadro, ChemDraw, and Spartan), whereas the control group received conventional instruction. Students’ deep conceptual understanding was assessed using a validated instrument measuring conceptual connectivity, scientific reasoning and concept application, and higher-order thinking skills. Data were analyzed using independent-samples t-tests, effect size analysis, and structural model evaluation with SmartPLS. The findings revealed that students who learned with CCS significantly outperformed those receiving conventional instruction across all dimensions of deep conceptual understanding (p < .001). Large effect sizes were observed for conceptual connectivity (0.89) and scientific reasoning and concept application (0.85), while a moderate-to-large effect was found for higher-order thinking skills (0.79). The measurement model also demonstrated satisfactory validity, reliability, and model fit. These findings indicate that CCS serves not only as a molecular visualization tool but also as an effective pedagogical resource for supporting scientific reasoning, conceptual integration, and higher-order thinking in chemistry learning. The study contributes empirical evidence supporting the integration of computational chemistry into undergraduate curricula to foster deeper conceptual understanding and digitally enriched chemistry instruction.