Connecting explanations to representations: benefits of highlighting techniques in tutorial videos on students’ learning in organic chemistry

References

• Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198. https://doi.org/https://doi.org/10.1016/j.learninstruc.2006.03.001
   Web of Science ®Google Scholar
• Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (2013). Learning through case comparisons: A meta-analytic review. Educational Psychologist, 48(2), 87–113. https://doi.org/https://doi.org/10.1080/00461520.2013.775712
   Web of Science ®Google Scholar
• Anzovino, M. E., & Bretz, S. L. (2015). Organic chemistry students’ ideas about nucleophiles and electrophiles: The role of charges and mechanisms. Chemistry Education Research and Practice, 16(4), 797–810. https://doi.org/https://doi.org/10.1039/C5RP00113G
   Web of Science ®Google Scholar
• Ayres, P., & Sweller, J. (2014). The split attention principle in mutlimedia learning. In R. E. Mayer (Ed.), Cambridge handbooks in psychology. The Cambridge handbook of multimedia learning (pp. 206–226). Cambridge University Press.
   Google Scholar
• Bhattacharyya, G., & Bodner, G. M. (2005). “It gets me to the product”: How students propose organic mechanisms. Journal of Chemical Education, 82(9), 1402–1407. https://doi.org/https://doi.org/10.1021/ed082p1402
   Web of Science ®Google Scholar
• Bodé, N. E., Deng, J. M., & Flynn, A. B. (2019). Getting past the rules and to the WHY: Causal mechanistic arguments when judging the plausibility of organic reaction mechanisms. Journal of Chemical Education, 96(6), 1068–1082. https://doi.org/https://doi.org/10.1021/acs.jchemed.8b00719
   Web of Science ®Google Scholar
• Boucheix, J.-M., & Lowe, R. K. (2010). An eye tracking comparison of external pointing cues and internal continuous cues in learning with complex animations. Learning and Instruction, 20(2), 123–135. https://doi.org/https://doi.org/10.1016/j.learninstruc.2009.02.015
   Web of Science ®Google Scholar
• Bruice, P. Y. (2016). Organic chemistry + mastering chemistry with Etext (8th ed.). Prentice Hall.
   Google Scholar
• Caspari, I., Kranz, D., & Graulich, N. (2018). Resolving the complexity of organic chemistry students’ reasoning through the lens of a mechanistic framework. Chemistry Education Research and Practice, 19(4), 1117–1141. https://doi.org/https://doi.org/10.1039/C8RP00131F
   Web of Science ®Google Scholar
• Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/https://doi.org/10.1207/s15516709cog1302_1
   Web of Science ®Google Scholar
• Clayden, J., Greeves, N., & Warren, S. G. (2012). Organic chemistry (Second ed.). Oxford University Press.
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). L. Erlbaum Associates.
   Google Scholar
• Cooper, M. M., Corley, L. M., & Underwood, S. M. (2013). An investigation of college chemistry students’ understanding of structure-property relationships. Journal of Research in Science Teaching, 50(6), 699–721. https://doi.org/https://doi.org/10.1002/tea.21093
   Web of Science ®Google Scholar
• Daniel, K. L., Bucklin, C. J., Leone, E. A., & Idema, J. (2018). Towards a definition of representational competence. In K. L. Daniel (Ed.), Towards a framework for representational competence in science education (pp. 3–11). Springer International Publishing.
   Google Scholar
• de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21(2), 113–140. https://doi.org/https://doi.org/10.1007/s10648-009-9098-7
   Web of Science ®Google Scholar
• Ferguson, R., & Bodner, G. M. (2008). Making sense of the arrow-pushing formalism among chemistry majors enrolled in organic chemistry. Chemistry Education Research and Practice, 9(2), 102–113. https://doi.org/https://doi.org/10.1039/B806225K
   Web of Science ®Google Scholar
• Field, A. P., & Wilcox, R. R. (2017). Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers. Behaviour Research and Therapy, 98, 19–38. https://doi.org/https://doi.org/10.1016/j.brat.2017.05.013. http://www.sciencedirect.com/science/article/pii/S0005796717301067
   PubMed Web of Science ®Google Scholar
• Gentner, D. (1989). The mechanisms of analogical learning. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 197–241). Cambridge University Press.
   Google Scholar
• Gilbert, J. K., & Treagust, D. (2009). Models and modeling in science education. Vol. 4: Multiple representations in chemical education (1st ed.). Springer.
   Google Scholar
• Graulich, N. (2015a). The tip of the iceberg in organic chemistry classes: How do students deal with the invisible? Chemistry Education Research and Practice, 16(1), 9–21. https://doi.org/https://doi.org/10.1039/C4RP00165F
   Web of Science ®Google Scholar
• Graulich, N. (2015b). Intuitive judgments govern students’ answering patterns in multiple-choice exercises in organic chemistry. Journal of Chemical Education, 92(2), 205–211. https://doi.org/https://doi.org/10.1021/ed500641n
   Web of Science ®Google Scholar
• Graulich, N., & Schween, M. (2018). Concept-Oriented task design: Making purposeful case comparisons in organic chemistry. Journal of Chemical Education, 95(3), 376–383. https://doi.org/https://doi.org/10.1021/acs.jchemed.7b00672
   Web of Science ®Google Scholar
• Grove, N. P., Cooper, M. M., & Rush, K. M. (2012). Decorating with arrows: Toward the development of representational competence in organic chemistry. Journal of Chemical Education, 89(7), 844–849. https://doi.org/https://doi.org/10.1021/ed2003934
   Web of Science ®Google Scholar
• Heller, K. A., & Perleth, C. (2000). KFT 4 - 12 + R: Kognitiver Fähigkeitstest für 4. bis 12. Klassen, Revision [KFT 4 - 12 + R: Cognitive ability test for grades 4 to 12, revision]. Beltz.
   Google Scholar
• Hilton, A., & Nichols, K. (2011). Representational classroom practices that contribute to students’ conceptual and representational understanding of chemical bonding. International Journal of Science Education, 33(16), 2215–2246. https://doi.org/https://doi.org/10.1080/09500693.2010.543438
   Web of Science ®Google Scholar
• Jamet, E., Gavota, M., & Quaireau, C. (2008). Attention guiding in multimedia learning. Learning and Instruction, 18(2), 135–145. https://doi.org/https://doi.org/10.1016/j.learninstruc.2007.01.011
   Web of Science ®Google Scholar
• Jeung, H-J, Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode instruction. Educational Psychology, 17(3), 329–345. https://doi.org/https://doi.org/10.1080/0144341970170307
   Google Scholar
• Johnstone, A. H. (1982). Macro- and microchemistry. School Science Review, 64(227), 377–379.
   Google Scholar
• Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and expertise. In B. H. Ross (Ed.), The psychology of learning and motivation (pp. 117–165). Elsevier.
   Google Scholar
• Kozma, R. B. (2020). Use of multiple representations by experts and novices. In P. van Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Educational psychology handbook series. Handbook of learning from multiple representations and perspectives (pp. 33–47). Routledge, Taylor & Francis Group.
   Google Scholar
• Kozma, R., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34(9), 949–968. https://doi.org/https://doi.org/10.1002/(SICI)1098-2736(199711)34:9<949::AID-TEA7>3.0.CO;2-U
   Web of Science ®Google Scholar
• Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Models and modeling in science education: Vol. 1. Visualization in science education (pp. 121–145). Springer.
   Google Scholar
• Low, R., & Sweller, J. (2014). The modality principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbooks in psychology. The Cambridge handbook of multimedia learning (pp. 227–246). Cambridge University Press.
   Google Scholar
• Lüdecke, D. (2018). Ggeffects: Tidy data frames of marginal effects from regression models. Journal of Open Source Software, 3(26), 772–776. https://doi.org/https://doi.org/10.21105/joss.00772
   Google Scholar
• Maechler, M., Rousseeuw, P., Croux, C., Todorov, V., Ruckstuhl, A., Salibian-Barrera, M., … Di Anna Palma, M. (2020). robustbase: Basic Robust Statistics (Version R package version 0.93-6). Comprehensive R Archive Network (CRAN). http://CRAN.R-project.org/package=robustbase
   Google Scholar
• Maeyer, J., & Talanquer, V. (2013). Making predictions about chemical reactivity: Assumptions and heuristics. Journal of Research in Science Teaching, 50(6), 748–767. https://doi.org/https://doi.org/10.1002/tea.21092
   Web of Science ®Google Scholar
• Mair, P., & Wilcox, R. (2020). Robust statistical methods in R using the WRS2 package. Behavior Research Methods, 52(2), 464–488. https://doi.org/https://doi.org/10.3758/s13428-019-01246-w
   PubMed Web of Science ®Google Scholar
• Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), Cambridge handbooks in psychology. The Cambridge handbook of multimedia learning (pp. 43–71). Cambridge University Press.
   Google Scholar
• Popova, M., & Jones, T. (2021). Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills. Chemistry Education Research and Practice, 22(3), 733–748. Advance online publication. https://doi.org/https://doi.org/10.1039/D0RP00329H
   Web of Science ®Google Scholar
• Rappoport, L. T., & Ashkenazi, G. (2008). Connecting levels of representation: Emergent versus submergent perspective. International Journal of Science Education, 30(12), 1585–1603. https://doi.org/https://doi.org/10.1080/09500690701447405
   Web of Science ®Google Scholar
• Rau, M. A. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4), 717–761. https://doi.org/https://doi.org/10.1007/s10648-016-9365-3
   Web of Science ®Google Scholar
• Rau, M. A. (2020). Cognitive and socio-cultural theories on competencies and practices involved in learning with multiple external representations. In P. van Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Educational psychology handbook series. Handbook of learning from multiple representations and perspectives (pp. 17–32). Routledge, Taylor & Francis Group.
   Google Scholar
• Rodemer, M., Eckhard, J., Graulich, N., & Bernholt, S. (2020). Decoding case comparisons in organic chemistry: Eye-tracking students’ visual behavior. Journal of Chemical Education, 97(10), 3530–3539. https://doi.org/https://doi.org/10.1021/acs.jchemed.0c00418
   Web of Science ®Google Scholar
• Scheid, J., Müller, A., Hettmannsperger, R., & Schnotz, W. (2018). Representational competence in science education: From theory to assessment. In K. L. Daniel (Ed.), Towards a framework for representational competence in science education (pp. 263–277). Springer International Publishing.
   Google Scholar
• Schnotz, W. (2014). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), Cambridge handbooks in psychology. The Cambridge handbook of multimedia learning (pp. 72–103). Cambridge University Press.
   Google Scholar
• Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145. https://doi.org/https://doi.org/10.1080/07370008.2010.507318
   Web of Science ®Google Scholar
• Strickland, A. M., Kraft, A., & Bhattacharyya, G. (2010). What happens when representations fail to represent?: Graduate students’ mental models of organic chemistry diagrams. Chemistry Education Research and Practice, 11(4), 293–301. https://doi.org/https://doi.org/10.1039/C0RP90009E
   Web of Science ®Google Scholar
• Tabbers, H. K., Martens, R. L., & van Merriënboer, J. J. G. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74(1), 71–81. https://doi.org/https://doi.org/10.1348/000709904322848824
   PubMed Web of Science ®Google Scholar
• Taber, K. S. (2009). Learning at the symbolic level. In J. K. Gilbert & D. Treagust (Eds.), Models and modeling in science education: Vol. 4: Multiple representations in chemical education (1st ed., pp. 75–105). Springer.
   Google Scholar
• Taskin, V., & Bernholt, S. (2014). Students’ understanding of chemical formulae: A review of empirical research. International Journal of Science Education, 36(1), 157–185. https://doi.org/https://doi.org/10.1080/09500693.2012.744492
   Web of Science ®Google Scholar
• Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3(4), 257–287. https://doi.org/https://doi.org/10.1037/1076-898X.3.4.257
   Web of Science ®Google Scholar
• Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38. https://doi.org/https://doi.org/10.18637/jss.v059.i05
   PubMed Web of Science ®Google Scholar
• Tippett, C. D. (2016). What recent research on diagrams suggests about learning with rather than learning from visual representations in science. International Journal of Science Education, 38(5), 725–746. https://doi.org/https://doi.org/10.1080/09500693.2016.1158435
   Web of Science ®Google Scholar
• Treagust, D., Chittleborough, G., & Mamiala, T. (2003). The role of submicroscopic and symbolic representations in chemical explanations. International Journal of Science Education, 25(11), 1353–1368. https://doi.org/https://doi.org/10.1080/0950069032000070306
   Web of Science ®Google Scholar
• Treagust, D. F., Duit, R., Joslin, P., & Lindauer, I. (1992). Science teachers’ use of analogies: Observations from classroom practice. International Journal of Science Education, 14(4), 413–422. https://doi.org/https://doi.org/10.1080/0950069920140404
   Web of Science ®Google Scholar
• van Gog, T. (2014). The signaling (or cueing) principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbooks in psychology. The Cambridge handbook of multimedia learning (pp. 263–278). Cambridge University Press.
   Google Scholar
• Weinrich, M. L., & Talanquer, V. (2015). Mapping students’ conceptual modes when thinking about chemical reactions used to make a desired product. Chemistry Education Research and Practice, 16(3), 561–577. https://doi.org/https://doi.org/10.1039/C5RP00024F
   Web of Science ®Google Scholar
• Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686–1691. https://doi.org/https://doi.org/10.21105/joss.01686
   Google Scholar