Alkhaldi, T., Pranata, I., & Athauda, R. I. (2016). A review of contemporary virtual and remote laboratory implementations: observations and findings. Journal of Computers in Education, 3(3), 329–351. https://doi.org/10.1007/s40692-016-0068-z
Anderson, L. W., & Bloom, B. S. (2014). A taxonomy for learning, teaching, and assessing : a revision of Bloom’s. In TA - TT - (Pearson ne). Pearson. https://doi.org/LK - https://worldcat.org/title/864384105
Assaraf, O., & Orion, N. (2005). Development of system thinking skills in the context of Earth System education. Journal of Research in Science Teaching, 42, 518–560. https://doi.org/10.1002/tea.20061
Aulia, E. V., Poedjiastoeti, S., & Agustini, R. (2018). The Effectiveness of Guided Inquiry-based Learning Material on Students’ Science Literacy Skills. Journal of Physics: Conference Series, 947(1), 12049. https://doi.org/10.1088/1742-6596/947/1/012049
Blatti, J., Garcia, J., Cave, D., Monge, F., Cuccinello, A., Portillo, J., Juarez, B., Chan, E., & Schwebel, F. (2019). Systems Thinking in Science Education and Outreach toward a Sustainable Future. Journal of Chemical Education, 96. https://doi.org/10.1021/acs.jchemed.9b00318
Bozzo, G., Lopez, V., Couso, D., & Monti, F. (2022). Combining real and virtual activities about electrostatic interactions in primary school. International Journal of Science Education, 44(18), 2704–2723. https://doi.org/10.1080/09500693.2022.2149284
Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218–237. https://doi.org/https://doi.org/10.1016/j.compedu.2015.07.003
Byrne, J., Heavey, C., & Byrne, P. J. (2010). A review of Web-based simulation and supporting tools. Simulation Modelling Practice and Theory, 18(3), 253–276. https://doi.org/https://doi.org/10.1016/j.simpat.2009.09.013
De Andrade, V., Shwartz, Y., Freire, S., & Baptista, M. (2022). Students’ mechanistic reasoning in practice: Enabling functions of drawing, gestures and talk. Science Education, 106(1), 199–225. https://doi.org/10.1002/sce.21685
De Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and Virtual Laboratories in Science and Engineering Education. Science, 340(6130), 305–308. https://doi.org/10.1126/science.1230579
Dickes, A. C., Sengupta, P., Farris, A. M. Y. V., & Basu, S. (2016). Development of Mechanistic Reasoning and Multilevel Explanations of Ecology in Third Grade Using Agent-Based Models. Science Education, 100(4), 734–776. https://doi.org/https://doi.org/10.1002/sce.21217
Durlak, J. (2009). How to Select, Calculate, and Interpret Effect Sizes. Journal of Pediatric Psychology, 34, 917–928. https://doi.org/10.1093/jpepsy/jsp004
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(1), 23. https://doi.org/10.1186/s40561-023-00244-y
Engelhardt, P. V., & Beichner, R. J. (2003). Students’ understanding of direct current resistive electrical circuits. American Journal of Physics, 72(1), 98–115. https://doi.org/10.1119/1.1614813
Eshach, H., Lin, T.-C., & Tsai, C.-C. (2018). Misconception of sound and conceptual change: A cross-sectional study on students’ materialistic thinking of sound. Journal of Research in Science Teaching, 55(5), 664–684. https://doi.org/https://doi.org/10.1002/tea.21435
Flegr, S., Kuhn, J., & Scheiter, K. (2023). When the whole is greater than the sum of its parts: Combining real and virtual experiments in science education. Computers & Education, 197, 104745. https://doi.org/https://doi.org/10.1016/j.compedu.2023.104745
Gilissen, M. G. R., Knippels, M.-C. P. J., & van Joolingen, W. R. (2020). Bringing systems thinking into the classroom. International Journal of Science Education, 42(8), 1253–1280. https://doi.org/10.1080/09500693.2020.1755741
Harrison, V., Kemp, R., Brace, N., Kemp, R., & Snelgar, R. (2021). SPSS for Psychologists. In SPSS for Psychologists. Red Globe Press. https://doi.org/10.1007/978-1-137-57923-2
Haskel-Ittah, M. (2023). Explanatory black boxes and mechanistic reasoning. Journal of Research in Science Teaching, 60(4), 915–933. https://doi.org/https://doi.org/10.1002/tea.21817
Hedges, L. V. (1981). Distribution Theory for Glass’s Estimator of Effect Size and Related Estimators. Journal of Educational Statistics, 6(2), 107–128. https://doi.org/10.2307/1164588
Hmelo-Silver, C. E., Liu, L., Gray, S., & Jordan, R. (2015). Using representational tools to learn about complex systems: A tale of two classrooms. Journal of Research in Science Teaching, 52(1), 6–35. https://doi.org/https://doi.org/10.1002/tea.21187
Inkinen, J., Klager, C., Juuti, K., Schneider, B., Salmela-Aro, K., Krajcik, J., & Lavonen, J. (2020). High school students’ situational engagement associated with scientific practices in designed science learning situations. Science Education, 104(4), 667–692. https://doi.org/https://doi.org/10.1002/sce.21570
Iseki, H. (2020). Cohen’s kappa statistics as a convenient means to identify accurate SARS-CoV-2 rapid antibody tests. MedRxiv, 2020.06.13.20130070. https://doi.org/10.1101/2020.06.13.20130070
Jahanifar, M., & Hormozi Nejad, M. (2023). Improving students’ causal reasoning skills with the computer modelling. Technology of Education Journal (TEJ), 17(3), 607–620. https://doi.org/10.22061/tej.2023.9401.2841
Jiménez-Aleixandre, M. P., Crujeiras, B., Taber, K. S., & Akpan, B. (2017). Science education. New directions in mathematics and science education: Vol. null (null (ed.)).
Kang, H., Thompson, J., & Windschitl, M. (2014). Creating Opportunities for Students to Show What They Know: The Role of Scaffolding in Assessment Tasks. Science Education, 98. https://doi.org/10.1002/sce.21123
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. https://doi.org/10.1007/s10956-018-9762-0
Kind, P., & Osborne, J. (2017). Styles of Scientific Reasoning: A Cultural Rationale for Science Education? Science Education, 101(1), 8–31. https://doi.org/10.1002/sce.21251
Lazenby, K., & Becker, N. M. (2021). Evaluation of the students’ understanding of models in science (SUMS) for use in undergraduate chemistry. Chem. Educ. Res. Pract., 22(1), 62–76. https://doi.org/10.1039/D0RP00084A
López, V., & Pintó, R. (2017). Identifying secondary-school students’ difficulties when reading visual representations displayed in physics simulations. International Journal of Science Education, 39(10), 1353–1380. https://doi.org/10.1080/09500693.2017.1332441
Margunayasa, I. G., Dantes, N., Marhaeni, A., & Suastra, I. W. (2019). The Effect of Guided Inquiry Learning and Cognitive Style on Science Learning Achievement. International Journal of Instruction.
Momsen, J., Speth, E. B., Wyse, S., & Long, T. (2022). Using Systems and Systems Thinking to Unify Biology Education. CBE—Life Sciences Education, 21(2), es3. https://doi.org/10.1187/cbe.21-05-0118
Nguyen, H., & Santagata, R. (2021). Impact of computer modeling on learning and teaching systems thinking. Journal of Research in Science Teaching, 58(5), 661–688. https://doi.org/10.1002/tea.21674
Osborne, J., & Lederman, N. G. (2014). Handbook of Research on Science Education: Vol. null (null (ed.)).
Perkins, K., Adams, W., Dubson, M., Finkelstein, N., Reid, S., Wieman, C., & LeMaster, R. (2006). PhET: Interactive Simulations for Teaching and Learning Physics. The Physics Teacher, 44, 18–23. https://doi.org/10.1119/1.2150754
Plass, J. L., Homer, B. D., & Hayward, E. O. (2009). Design factors for educationally effective animations and simulations. Journal of Computing in Higher Education, 21(1), 31–61. https://doi.org/10.1007/s12528-009-9011-x
Raven, S., & Wenner, J. A. (2023). Science at the center: Meaningful science learning in a preschool classroom. Journal of Research in Science Teaching, 60(3), 484–514. https://doi.org/https://doi.org/10.1002/tea.21807
S., S., Hmelo-Silver, C. E., Jordan, R., Eberbach, C., & Sinha, S. (2017). Systems learning with a conceptual representation: A quasi-experimental study. Instructional Science, 45(1), 53. https://doi.org/10.1007/s11251-016-9392-y
Sadideen, H., Hamaoui, K., Saadeddin, M., & Kneebone, R. (2012). Simulators and the simulation environment: Getting the balance right in simulation-based surgical education. International Journal of Surgery (London, England), 10. https://doi.org/10.1016/j.ijsu.2012.08.010
Sarabando, C., Cravino, J. P., & Soares, A. A. (2014). Contribution of a Computer Simulation to Students’ Learning of the Physics Concepts of Weight and Mass. Procedia Technology, 13, 112–121. https://doi.org/https://doi.org/10.1016/j.protcy.2014.02.015
Sjøberg, M., Furberg, A., & Knain, E. (2023). Undergraduate biology students’ model-based reasoning in the laboratory: Exploring the role of drawings, talk, and gestures. Science Education, 107(1), 124–148. https://doi.org/https://doi.org/10.1002/sce.21765
Stanny, C. J. (2016). Reevaluating Bloom’s Taxonomy: What Measurable Verbs Can and Cannot Say about Student Learning. Education Sciences, 6(4). https://doi.org/10.3390/educsci6040037
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics. Pearson. http://queens.ezp1.qub.ac.uk/login?url=http://ebookcentral.proquest.com/lib/qub/detail.action?docID=5581921
Tytler, R., Prain, V., Aranda, G., Ferguson, J., & Gorur, R. (2020). Drawing to reason and learn in science. Journal of Research in Science Teaching, 57(2), 209–231. https://doi.org/https://doi.org/10.1002/tea.21590
Usman, M., Suyanta, & Huda, K. (2021). Virtual lab as distance learning media to enhance student’s science process skill during the COVID-19 pandemic. Journal of Physics: Conference Series, 1882(1), 12126. https://doi.org/10.1088/1742-6596/1882/1/012126
Wang, T.-L., & Tseng, Y.-K. (2016). The Comparative Effectiveness of Physical, Virtual, and Virtual-Physical Manipulatives on Third-Grade Students’ Science Achievement and Conceptual Understanding of Evaporation and Condensation. International Journal of Science and Mathematics Education, 16. https://doi.org/10.1007/s10763-016-9774-2
Widiyatmoko, A. (2018). The Effectiveness of Simulation in Science Learning on Conceptual Understanding : A Literature Review.
Widodo, W., Rosdiana, L., Fauziah, A. M., & Suryanti. (2018). Revealing Student’s Multiple-Misconception on Electric Circuits. Journal of Physics: Conference Series, 1108(1), 12088. https://doi.org/10.1088/1742-6596/1108/1/012088
Wilcox, R. R. (2022). Chapter 12 - ANCOVA (R. R. B. T.-I. to R. E. and H. T. (Fifth E. Wilcox (ed.); pp. 773–826). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-820098-8.00018-X
Wörner, S., Becker, S., Küchemann, S., Scheiter, K., & Kuhn, J. (2022). Development and validation of the ray optics in converging lenses concept inventory. Phys. Rev. Phys. Educ. Res., 18(2), 20131. https://doi.org/10.1103/PhysRevPhysEducRes.18.020131
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. https://doi.org/10.3102/00346543221079417
Xu, X., Allen, W., Miao, Z., Yao, J., Sha, L., & Chen, Y. (2018). Exploration of an interactive “Virtual and Actual Combined” teaching mode in medical developmental biology. Biochemistry and Molecular Biology Education, 46. https://doi.org/10.1002/bmb.21174
Zacharia, Z., & Michael, M. (2016). Using Physical and Virtual Manipulatives to Improve Primary School Students’ Understanding of Concepts of Electric Circuits (pp. 125–140). https://doi.org/10.1007/978-3-319-22933-1_12
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