Attali, Y., & van der Kleij, F. (2017). Effects of feedback elaboration and feedback timing during computer-based practice in mathematics problem solving. Computers & Education, 110, 154-169. http://dx.doi.org/10.1016/j.compedu.2017.03.012.
Bernstein, E., McMenamin, S. A., & Johanek, M. C. (2016). Authentic Online Branching Simulations: Promoting Discourse around Problems of Practice. In P. Dickenson, U. National University, J. J. Jaurez, & U. National University (Eds.), Student Engagement and Participation (pp. 1197–1216). IGI Global.
Butler, A. C., Karpicke, J. D., & Roediger, H. L., III. (2008). Correcting a metacognitive error: feedback increases retention of low-confidence correct responses. Journal of Experimental Psychology, 34, 918–928. doi: 10.1037/0278-7393.34.4.918.
Chiu, M. H., Chou, C. C., & Liu, C. J. (2002). Dynamic processes of conceptual change: analysis of constructing mental models of chemical equilibrium. Journal of Research in Science Teaching, 39(8), 688-712. DOI: 10.1002/tea.10041
Devetak, I., Urbančič, M., Wissiak Grm, K. S., Krnel, D., & Glažar, S. A. (2004). Submicroscopic representations as a tool for evaluating students’ chemical conceptions. Acta Chimica Slovenica, 51(4), 799-814.
Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.
Finn, B., Thomas, R., Rawson, K, A. (2018). Learning more from feedback: elaborating feedback with examples enhances concept learning. Learning and Instruction, 54, 104-113.
Greca, I. M., & Moreira, M. A. (2000). Mental models, conceptual models, and modelling. International Journal of Science Education, 22(1), 2-11. https://doi.org/10.1080/095006900289976.
Hattie, J. (2009). Visible learning: A synthesis of 800+ meta-analyses on achievement. London: Routledge.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Huan, C., Meng, C.C., Lian, L. H. (2020). Incorporating feedback in online cognitive diagnostic assessment for enhancing grade five students' achievement in 'time'. Journal of Computer Education, DOI: 10.1007/s40692-020-00176-3
Jaber, L. Z., & BouJaoude, S. (2012). A macro–micro–symbolic teaching to promote relational understanding of chemical reactions. International Journal of Science Education, 34(7), 973–998.
Johnson, C. I., & Priest, H. A. (2014). The feedback principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 449–463). Cambridge: Cambridge University Press.
Johnson-Laird, P. N. (1983). Mental models. Towards a cognitive science of language, inference, and consciousness. Cambridge, UK: Cambridge University Press.
Kozma, R., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 43(9), 949–968.
Lindgren, R., & Schwartz, D. L. 2009. Spatial learning and computer simulations in science. International. Journal of Science Education, 31, 419–438.
Lipnevich, A., & Smith, J, K. 2009. Effects of differential feedback on students' examination performance. Journal of Experimental Psychology Applied, 15(4), 319-333. DOI: 10.1037/a0017841
Luyben, P. D., Warden, K. B. 2008. Comparative effects of video plus-text versus text-only instructional formats on acquisition and generalization of concept learning to real life situations. Journal of Education Technology Systems, 37(2), 159-174. https://doi.org/10.2190/ET.37.2.d
Máñez, I., Vidal-Abarca, E., & Martínez, T. (2019). Does computer-based elaborated feedback influence the students’ question-answering process? Electronic Journal of Research in Educational Psychology, 17(1), 81-106.
Makransky, G., Mayer, R., Noremolle, A., Cordoba, A. L., Wandali, J., & Bonde, M. (2019). Investigating the feasibility of using assessment and explanatory feedback in desktop virtual reality simulations. Education Technology Research Development, 68, 283-317. https://doi.org/10.1007/s11423-019-09690-3.
Mayer, R. E., & Moreno, R. (2002). Aids to computer-based multimedia learning. Learning and Instruction, 12, 107–119.
Meir, E., Wendel, D., Pope, D. S. Hsiao, L., Chen, D., & Kim, K, J. (2019). Are intermediate constraint question formats useful for evaluating student thinking and promoting learning in formative assessments?. Computers and education, 141, 103606. https://doi.org/10.1016/j.compedu.2019.103606
Norman, D. (1983). Some observations on mental models. In D. Gentner and A. Stevens (Eds. Mental models (pp. 6-14). Lawrence Erlbaum Associates, Hillsdale, NJ.
O'Keefe, P. A., Letourneau, S. M., Homer, B. D., Schwartz, R. N., & Plass, J. L. (2014). Learning from multiple representations: An examination of fixation patterns in a science simulation. Computers in Human Behavior, 35, 234–242. doi:10.1016/j.chb.2014.02.040.
Prokša, M., Drozdíková, A., & Haláová, Z. (2019). Learners’ understanding of chemical equilibrium at submicroscopic, macroscopic, and symbolic levels. Chemistry Didactics Ecology Metrology, 23(1).
Pulukuri, S., & Abrams, B. 2021. Improving learning outcomes and metacognitive monitoring: replacing traditional textbook readings with questions-embedded videos. Journal Chemistry Education, 98, 2156-2166. https://doi.org/10.1021/acs.jchemed.1c00237.
Serral, E., & Snoeck, M. (2019). Conceptual framework for feedback automation in sles. In Smart education and e-learning 2016 (pp. 97–107). Springer.
Shehab, S. S., & BouJaoude, S. (2017). Analysis of the chemical representations in secondary Lebanese chemistry textbooks. International Journal of Science and Mathematics Education, 15, 797-816. DOI 10.1007/s10763-016-9720-3.
Shute, V. J. 2008. Focus on formative feedback. Review of Educational Research, 78(1), 153-189. DOI: 10.3102/0034654307313795
Stieff, M., & McCombs, M. (2006). Increasing representational fluency with visualization tools. In S. Barab, K. E. Hay, & D. T. Hickey (Eds.), Proceedings of the Seventh International Conference of the Learning Sciences (ICLS) (Vol. 1, pp. 730–736). Mahwah, NJ: Erlbaum.
Talanquer, V. (2010). Macro, submicro, and symbolic: the many faces of the chemistry triplet. International Journal of Science Education, 32, 1–17.
Van der Kleij, F. M., Feskens, R. C., & Eggen, T. J. H. M. (2015). Effects of feedback in a computer-based learning environment on students' learning outcomes: A meta-analysis. Review of Educational Research, 85, 475–511. doi: 10.3102/0034654314564881
Waight, Noemi., & Gillmeister, Kristina. (2014). Teachers and Students’ Conceptions of Computer-Based Models in the Context of High School Chemistry: Elicitations at the Pre-intervention Stage. Research in Science Education, 44(2), 335-361. DOI 10.1007/s11165-013-9385-7
Zumdahl, S. (2017). Chemistry AP Edition (10th ed.). Cengage.