Virtual Practical Work for Quality Learning in Life and Earth Sciences: Student Performance and Perceptions
DOI:
https://doi.org/10.15294/jpii.v15i3.48504Keywords:
practical work, virtual laboratory, conceptual and procedural knowledge, motivation, secondary educationAbstract
Although virtual laboratories are increasingly used to address constraints on practical science learning, evidence remains limited on locally developed virtual laboratories in Moroccan lower-secondary science and on evaluations that combine objective conceptual and procedural learning outcomes with multidimensional learner perceptions. This study compared students' conceptual and procedural knowledge after using LabInnov or receiving conventional non-laboratory instruction and examined users' perceptions of the platform. A quantitative exploratory quasi-experimental study used a post-test-only non-equivalent-group design with 34 third-year lower-secondary students from two intact classes, with one class assigned to each instructional condition. The experimental group (n = 17) used LabInnov during two 120-minute sessions, while the control group (n = 17) studied the same scientific content conventionally. Both groups completed a ten-item post-test scored from 0 to 20, and the experimental group completed a 15-item questionnaire assessing satisfaction, usability, perceived understanding, motivation, and collaboration. The experimental group achieved higher post-test scores (M = 13.06, SD = 2.34) than the control group (M = 9.06, SD = 2.04), with a mean difference of 4.00 points, 95% CI [2.47, 5.53], t(32) = 5.318, p < .001, with a large effect (d = 1.82). Questionnaire means ranged from 3.65 to 4.67, and motivation was positively associated with perceived understanding (Spearman's ρ = .631, p = .006). These findings suggest that LabInnov may support conceptual and procedural knowledge when regular practical work is constrained. However, the small sample, post-test-only design, and use of only one intact class per condition prevent causal attribution and leave open the possibility that the observed difference partly reflects class-level characteristics. Conceptually, the study contributes an integrated evaluative perspective on virtual laboratory learning by considering objective conceptual and procedural outcomes alongside multidimensional learner perceptions and the relationship between motivation and perceived understanding within a locally developed Moroccan virtual laboratory context.
References
Achuthan, K., Raghavan, D., Shankar, B., Francis, S. P., & Kolil, V. K. (2021). Impact of remote experimentation, interactivity and platform effectiveness on laboratory learning outcomes. International Journal of Educational Technology in Higher Education, 18, Article 38.
Agustina, R. D., & Putra, R. P. (2022). Sophisticated thinking blended laboratory (STB-LAB) learning model: Implications on virtual and real laboratory for increasing undergraduate students' argumentation skills. Jurnal Pendidikan IPA Indonesia, 11(4), 657–671.
Ahmad, N. J., Yakob, N., Bunyamin, M. A. H., Winarno, N., & Akmal, W. H. (2021). The effect of interactive computer animation and simulation on students' achievement and motivation in learning electrochemistry. Jurnal Pendidikan IPA Indonesia, 10(3), 311–324.
Akuma, F. V., & Callaghan, R. (2019). Characterising extrinsic challenges linked to the design and implementation of inquiry-based practical work. Research in Science Education, 49(6), 1677–1706.
Al-Duhani, F., Saat, R. M., & Abdullah, M. N. S. (2023). Effectiveness of virtual laboratory on grade eight students' achievement in learning electricity. Malaysian Online Journal of Educational Sciences, 11(3), 30–43.
Chan, P., Van Gerven, T., Dubois, J.-L., & Bernaerts, K. (2021). Virtual chemical laboratories: A systematic literature review of research, technologies and instructional design. Computers & Education Open, 2, Article 100053.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Distrik, I. W., Ertikanto, C., Purwati, Y. S., Saregar, A., & Ab Rahman, N. F. (2024). Digital problem-based worksheet with 3D PageFlip: An effort to address concept understanding problems and enhance digital literacy skills. Jurnal Pendidikan IPA Indonesia, 13(1), 116–127.
Diwakar, S., Kolil, V. K., Francis, S. P., & Achuthan, K. (2023). Intrinsic and extrinsic motivation among students for laboratory courses—Assessing the impact of virtual laboratories. Computers & Education, 198, Article 104758.
Dyrberg, N. R., Treusch, A. H., & Wiegand, C. (2017). Virtual laboratories in science education: Students' motivation and experiences in two tertiary biology courses. Journal of Biological Education, 51(4), 358–374.
El Kharki, K., Berrada, K., & Burgos, D. (2021). Design and implementation of a virtual laboratory for physics subjects in Moroccan universities. Sustainability, 13(7), Article 3711.
Elmoazen, R., Saqr, M., Khalil, M., & Wasson, B. (2023). Learning analytics in virtual laboratories: A systematic literature review of empirical research. Smart Learning Environments, 10, Article 23.
Fadda, D., Salis, C., & Vivanet, G. (2022). About the efficacy of virtual and remote laboratories in STEM education in secondary school: A second-order systematic review. Journal of Educational, Cultural and Psychological Studies, 26, 51–72.
Fredricks, J. A., Hofkens, T. L., & Wang, M.-T. (2019). Addressing the challenge of measuring student engagement. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 689–712). Cambridge University Press.
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168.
Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128.
Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153, Article 103897.
Oliveira, H., & Bonito, J. (2023). Practical work in science education: A systematic literature review. Frontiers in Education, 8, 1151641.
Jebb, A. T., Ng, V., & Tay, L. (2021). A review of key Likert scale development advances: 1995–2019. Frontiers in Psychology, 12, Article 637547.
Jebara, S., Lghazi, Y., & Bassiri, M. (2025a). Constraints, challenges, and innovation issues for integrating virtual lab sessions in Life and Earth Sciences education in Morocco. Sociology of Science and Technology, 16(4), 50–64.
Jebara, S., Lghazi, Y., & Bassiri, M. (2025b). Design, development, and scripting of a virtual platform for practical work in Earth and Life Sciences: LabInnov. In EDULEARN25 proceedings (pp. 8231–8238). IATED.
Kapici, H. O., Akcay, H., & de Jong, T. (2019). Using hands-on and virtual laboratories alone or together—Which works better for acquiring knowledge and skills? Journal of Science Education and Technology, 28(3), 231–250.
Kapici, H. O., Akcay, H., & de Jong, T. (2020). How do different laboratory environments influence students' attitudes toward science courses and laboratories? Journal of Research on Technology in Education, 52(4), 534–549.
Kapici, H. O., Akcay, H., & Cakir, H. (2022). Investigating the effects of different levels of guidance in inquiry-based hands-on and virtual science laboratories. International Journal of Science Education, 44(2), 324–345.
Kurtz, M., Benabbou, A., Pons, C., & Broisin, J. (2025). Collaboration in virtual and remote laboratories for education: A systematic literature review. International Journal of Computer-Supported Collaborative Learning, 20, 549–603.
Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), Article 33267.
Lestari, D. P., Supahar, Paidi, Suwarjo, & Herianto. (2023). Effect of science virtual laboratory combination with demonstration methods on lower-secondary school students' scientific literacy ability in a science course. Education and Information Technologies, 28, 16153–16175.
Levene, H. (1960). Robust tests for equality of variances. In I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, & H. B. Mann (Eds.),
Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 278–292). Stanford University Press.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55.
Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236.
Moore, M. G. (1989). Three types of interaction. American Journal of Distance Education, 3(2), 1–7.
Muilwijk, S. E., & Lazonder, A. W. (2023). Learning from physical and virtual investigation: A meta-analysis of conceptual knowledge acquisition. Frontiers in Education, 8, Article 1163024.
Nafidi, Y., Alami, A., Zaki, M., El Batri, B., Hassani, M. E., & Afkar, H. (2018). L’intégration des TIC dans l’enseignement des sciences de la vie et de la terre au Maroc : État des lieux et défis à relever. European Scientific Journal, 14(1), 97–121.
Najoui, K., & Alami, A. (2017). Importance des travaux pratiques dans l’enseignement des sciences de la Terre au secondaire qualifiant marocain. American Journal of Innovative Research and Applied Sciences, 4(6), 230–239.
OECD. (2023). PISA 2022 results (Volume I and II)—Country notes: Morocco. https://www.oecd.org/en/publications/pisa-2022-results-volume-i-and-ii-country-notes_ed6fbcc5-en/morocco_10dfcb74-en.html
Posit Team. (2025). RStudio (Version 2024.12.1+563) [Computer software]. Posit Software, PBC.
Radhamani, R., Kumar, D., Nizar, N., Achuthan, K., Nair, B. G., & Diwakar, S. (2021). What virtual laboratory usage tells us about laboratory skill education pre- and post-COVID-19: Focus on usage, behavior, intention and adoption. Education and Information Technologies, 26(6), 7477–7495.
Reichardt, C. S. (2019). Quasi-experimentation: A guide to design and analysis. Guilford Press.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860.
Sarapak, C., Jumpatam, J., Lunnoo, T., Kongngarm, N., Raso, S., & Kearns, K. (2025). Comparing 3D virtual labs and traditional labs: Impact on teacher training and student learning in physics education. Jurnal Pendidikan IPA Indonesia, 14(4).
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Shambare, B., & Simuja, C. (2022). A critical review of teaching with virtual lab: A panacea to challenges of conducting practical experiments in science subjects beyond the COVID-19 pandemic in rural schools in South Africa. Journal of Educational Technology Systems, 50(3), 393–408.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611.
Shchevliagin, M., & Koroleva, D. (2024). Four scenarios of personalized learning integration mediated by a digital platform. Turkish Online Journal of Distance Education, 25(2), 76–95.
Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101.
Student. (1908). The probable error of a mean. Biometrika, 6(1), 1–25.
Taber, K. S. (2018). The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.
UNESCO. (2023). Global education monitoring report 2023: Technology in education—A tool on whose terms?
Viitaharju, P., Nieminen, M., Linnera, J., Yliniemi, K., & Karttunen, A. J. (2023). Student experiences from virtual reality-based chemistry laboratory exercises. Education for Chemical Engineers, 44, 191–199.
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens—With new examples of knowledge, skills and attitudes (EUR 31006 EN). Publications Office of the European Union.
Wörner, S., Kuhn, J., & Scheiter, K. (2022). The best of two worlds: A systematic review on combining real and virtual experiments in science education. Review of Educational Research, 92(6), 911–952.
Widiyatmoko, A., Nugrahani, R., Yanitama, A., & Darmawan, M. S. (2023). The effect of virtual reality game-based learning to enhance STEM literacy in energy concepts. Jurnal Pendidikan IPA Indonesia, 12(4), 648–657.
Yang, C., Zhang, J., Hu, Y., Yang, X., Chen, M., Shan, M., & Li, L. (2024). The impact of virtual reality on practical skills for students in science and engineering education: A meta-analysis. International Journal of STEM Education, 11, Article 28.
Zhang, N., & Liu, Y. (2024). Design and implementation of virtual laboratories for higher education sustainability: A case study of Nankai University. Frontiers in Education, 8, Article 1322263.

