Self-Explaining Photosynthesis to Achieve Conceptual Change: An Analysis of Explanation Content

Merrin Oliver, Virginia Troemel

Abstract


Students enter biology coursework with various misconceptions needing revision. However, achieving conceptual change of these misconceptions in the classroom is notoriously difficult and requires specific instruction. Self-explanations can promote conceptual change, but their effects can depend on the content produced. This study investigates how the content of learners’ explanations of photosynthesis processes affects learning. We examined data from an online assignment in introductory biology where 118 college undergraduates answered multiple-choice questions related to commonly misconceived processes in photosynthesis and respiration and were then prompted to self-explain the correct answer. One week later, students took a test that measured learning in the activity. Using mixed methods analyses, we qualitatively explored the types of explanations learners made, categorized the different types of explanations, and performed quantitative analyses to examine relations between explanation content and test scores. We identified five categories of self-explanations that varied in engagement, accuracy, and focus. Accuracy of the explanation mattered; accurate explanations predicted higher test scores, and inaccurate explanations predicted lower test scores. We also identified three different groups of learners: highly performing learners who were actively engaged and accurate; moderately performing learners who were engaged but often paraphrased or explained inaccurately; and low performing learners who were disengaged and avoided explaining. We provide implications for use of self-explaining misconceived material.

https://doi.org/10.26803/ijlter.21.7.22


Keywords


self-explanation; misconception; conceptual change; mixed-method; biology

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References


American Association for the Advancement of Science (AAAS; 2022, July 25). AAAS science assessment. https://www.aaas.org/programs/project-2061/assessment-resources

Aleven, V. A., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26(2), 147-179. https://doi.org/10.1016/S0364-0213(02)00061-7

Amir, R., & Tamir, P. (1994). In-depth analysis of misconceptions as a basis for developing research-based remedial instruction: The case of photosynthesis. The American Biology Teacher, 56(2), 94-100. https://doi.org/10.2307/4449760

Anderson, C. W., Sheldon, T. H., & Dubay, J. (1990). The effects of instruction on college nonmajors' conceptions of respiration and photosynthesis. Journal of Research in Science teaching, 27(8), 761-776. https://doi.org/10.1002/tea.3660270806

Audesirk, G., Audesirk, T., & Byers, B. (2013). Biology: Life on Earth (10th ed.). Essex, UK: Pearson Publishing.

Ayres, P., & Paas, F. (2012). Cognitive load theory: New directions and challenges. Applied Cognitive Psychology, 26(6), 827-832. https://doi.org/10.1002/acp.2882

Benassi, M., Garofalo, S., Ambrosini, F., Sant’Angelo, R. P., Raggini, R., De Paoli, G., & Piraccini, G. (2020). Using two-step cluster analysis and latent class cluster analysis to classify the cognitive heterogeneity of cross-diagnostic psychiatric inpatients. Frontiers in Psychology, 11, 1085. https://doi.org/10.3389/fpsyg.2020.01085

Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing self-explanation: A meta-analysis. Educational Psychology Review, 30(3), 703-725. https://doi.org/10.1007/s10648-018-9434-x

Boomer, S. M., & Latham, K. L. (2011). Manipulatives-based laboratory for majors biology–a hands-on approach to understanding respiration and photosynthesis. Journal of Microbiology & Biology Education: JMBE, 12(2), 127. https://doi.org/10.1128/jmbe.v12i2.245

Chi, M. T. H. (1996). Constructing self-explanations and scaffolded explanations in tutoring. Applied Cognitive Psychology, 10, 33-49. https://doi.org/10.1002/(SICI)1099-0720(199611)10:7<33::AID-ACP436>3.0.CO;2-E

Chi, M. T. H. (1997). Quantifying analyses of verbal data: A practical guide. The Journal of Learning Sciences, 6(3), 271-315. https://www.public.asu.edu/~mtchi/papers/Verbaldata.pdf

Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161-199. https://doi.org/10.1207/s15327809jls1402_1

Chi, M. T. H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), Handbook of research on conceptual change (pp. 61-82). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Chi, M. T. H. (2013). Two kinds and four sub-types of misconceived knowledge, ways to change it, and the learning outcomes. International Handbook of Research on Conceptual Change. Stella Vosniadou (ed.). Abingdon: Routledge

Chi, M. T. H. (2018). Learning from examples via self-explanations. In L. B. Resnit (Eds.), Knowing, learning, and instruction (pp. 251-282). Routledge. https://apps.dtic.mil/sti/pdfs/ADA198809.pdf

Chin, C., & Brown, D. E. (2000). Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 37(2), 109-138. https://doi.org/10.1002/(SICI)1098-2736(200002)37:2<109::AID-TEA3>3.0.CO;2-7

Common Core State Standards. (2022, July 25). Math standards. http://www.corestandards.org/math/

Cordero, A. & Lineback, J. (2013). Misconceptions are “so yesterday!". CBE Life Sciences Education, 12. 352-6. http://doi.org/10.1187/cbe.13-01-0014.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. http://doi.org/10.1177/1529100612453266

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28, 717–741. https://doi.org/10.1007/s10648-015-9348-9

Fonseca, B., & Chi, M. T. H. (2011). The self-explanation effect: A constructive learning activity. The Handbook of Research on Learning and Instruction, 270-321. https://doi.org/10.1016/j.hpe.2015.11.005

Galvin, E., Simmie, G. M., & O'Grady, A. (2015). Identification of misconceptions in the teaching of biology: a pedagogical cycle of recognition, reduction and removal. Higher Education of Social Science, 8(2), 1-8. http://doi.org/10.3968/6519

Gregory, T. R. (2009). Understanding natural selection: Essential concepts and common misconceptions. Evolution: Education and Outreach, 2(2), 156–175. https://doi.org/10.1007/s12052-009-0128-1

Guzzetti, B. J., Snyder, T. E., Glass, G. V., & Gamas, W. S. (1993). Promoting conceptual change in science: A comparative meta-analysis of instructional interventions from reading education and science education. Reading Research Quarterly, 117-159. https://doi.org/10.2307/747886

Haslam, F., & Treagust, D. F. (1987). Diagnosing secondary students' misconceptions of photosynthesis and respiration in plants using a two-tier multiple choice instrument. Journal of Biological Education, 21(3), 203-211. https://doi.org/10.1080/00219266.1987.9654897

Hausmann, R. G., & Vanlehn, K. (2007). Explaining self-explaining: A contrast between content and generation. Frontiers in Artificial Intelligence and Applications, 158, 417. https://www.public.asu.edu/~kvanlehn/Stringent/PDF/07AIED_BH_KVL.pdf

Heddy, B. C., & Sinatra, G. M. (2013). Transforming misconceptions: Using transformative experience to promote positive affect and conceptual change in students learning about biological evolution. Science Education, 97(5), 723–744. https://doi.org/10.1002/sce.21072

Hiller, S., Rumann, S., Berthold, K., & Roelle, J. (2020). Example-based learning: Should learners receive closed-book or open-book self-explanation prompts? Instructional Science, 48(6), 623-649. https://doi.org/10.1007/s11251-020-09523-4

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509-539. https://doi.org/10.1007/s10648-007-9054-3

Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23-31. http://dx.doi.org/10.1207/S15326985EP3801_4

Karakaya, F., Yilmaz, M., & Aka, E. I. (2021). Examination of pre-service science teachers’ conceptual perceptions and misconceptions about photosynthesis. Pedagogical Research, 6(4). https://doi.org/10.29333/pr/11216

Kendeou, P., & Van Den Broek, P. (2007). The effects of prior knowledge and text structure on comprehension processes during reading of scientific texts. Memory & Cognition, 35(7), 1567-1577. https://doi.org/10.3758/BF03193491

Kohn, K. P., Underwood, S. M., & Cooper, M. M. (2018). Energy connections and misconnections across chemistry and biology. CBE—Life Sciences Education, 17(1), ar3. https://doi.org/10.1187/cbe.17-08-0169

Kwon, K., & Jonassen, D. H. (2011). The influence of reflective self-explanations on problem-solving performance. Journal of Educational Computing Research, 44(3), 247-263. https://doi.org/10.2190/EC.44.3.a

Lee C. H., & Kalyuga, S. (2014). Expertise reversal effect and its instructional implications. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.) Applying science of learning in education: Infusing psychological science into the curriculum, pp. 31-44. http://teachpsych.org/ebooks/asle2014/index.php

Leonard M. J., Kalinowski S. T., & Andrews T. C. (2014). Misconceptions yesterday, today, and tomorrow. CBE Life Sci Educ, 13(2). 179-86. http://doi.org/10.1187/cbe.13-12-0244

Leppink, J., Broers, N. J., Imbos, T., Van Der Vleuten, C. P., & Berger, M. P. (2012). Self-explanation in the domain of statistics: An expertise reversal effect. Higher Education, 63(6), 771-785. https://doi.org/10.1007/s10734-011-9476-1

Linnenbrink-Garcia, L., Pugh, K. J., Koskey, K. L., & Stewart, V. C. (2012). Developing conceptual understanding of natural selection: The role of interest, efficacy, and basic prior knowledge. The Journal of Experimental Education, 80(1), 45-68. https://doi.org/10.1080/00220973.2011.559491

Lombrozo, T. (2006). The structure and function of explanations. TRENDS in Cognitive Science, 10(10), 464–470. http://doi.org/10.1016/j.tics.2006.08.004

Meir, E., Perry, J., Herron, J. C., & Kingsolver, J. (2007). College students’ misconceptions about evolutionary trees. The American Biology Teacher, 69(7). https://doi.org/10.1662/0002-7685(2007)69[71:CSMAET]2.0.CO;2

Morrison, J. R., Bol, L., Ross, S. M., & Watson, G. S. (2015). Paraphrasing and prediction with self-explanation as generative strategies for learning science principles in a simulation. Educational Technology Research and Development, 63(6), 861-882. https://doi.org/10.1007/s11423-015-9397-2

Nadelson, L. S., Heddy, B. C., Jones, S., Taasoobshirazi, G., & Johnson, M. L. (2018). Conceptual change in science teaching and learning: Introducing the dynamic model of conceptual change. International Journal of Educational Psychology, 7(2), 151. https://doi.org/10.2196/resprot.2372

Oliver, M., Renken, M., & Williams, J. J. (2018). Revising biology misconceptions using retrieval practice and explanation prompts. Proceedings of the 13th International Conference of the Learning Sciences. https://repository.isls.org//handle/1/490

Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational research, 63(2), 167-199. https://doi.org/10.3102/00346543063002167

Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29. https://doi.org/10.1016/S0364-0213(99)80017-2

Renkl, A. (2014). Toward an instructionally oriented theory of example?based learning. Cognitive Science, 38(1), 1-37. https://doi.org/10.1111/cogs.12086

Rittle-Johnson, B., & Loehr, A. M. (2017). Eliciting explanations: Constraints on when self-explanation aids learning. Psychonomic Bulletin & Review, 1-10. https://doi.org/10.3758/s13423-016-1079-5

Roelle, J., & Renkl, A. (2020). Does an option to review instructional explanations enhance example-based learning? It depends on learners’ academic self-concept. Journal of Educational Psychology, 112(1), 131. https://doi.org/10.1007/s11251-020-09523-4

Roy, M., & Chi, M. T. (2005). The self-explanation principle in multimedia learning. The Cambridge handbook of multimedia learning, 271-286. https://education.asu.edu/sites/default/files/multim_chapter_final_1.pdf

Sinatra, G. M. (2005). The" warming trend" in conceptual change research: The legacy of Paul R. Pintrich. Educational psychologist, 40(2), 107-115. https://doi.org/10.1207/s15326985ep4002_5

Sinatra, G. M., & Broughton, S. H. (2011). Bridging reading comprehension and conceptual change in science education: The promise of refutation text. Reading Research Quarterly, 46, 374 –393. http://dx.doi.org/10.1002/RRQ.005

Sinatra, G. M., & Chinn, C. A. (2012). Thinking and reasoning in science: Promoting epistemic conceptual change. In K. R. Harris, S. Graham, T. Urdan, A. G. Bus, S. Major, & H. L. Swanson (Eds.), APA educational psychology handbook, Vol. 3. Application to learning and teaching (pp. 257–282). American Psychological Association. https://doi.org/10.1037/13275-011

Sinatra, G. M., & Pintrich, P. R. (2003). Intentional conceptual change (Eds.). Routledge. https://doi.org/10.4324/9781410606716-7

Sinatra, G. M., & Taasoobshirazi, G. (2011). Intentional conceptual change. Handbook of Self-Regulation of Learning and Performance, 203-216. https://doi.org/10.4324/9780203839010

Södervik, I., Virtanen, V., & Mikkilä-Erdmann, M. (2015). Challenges in understanding photosynthesis in a university introductory biosciences class. International Journal of Science and Mathematics Education, 13(4), 733-750. https://doi.org/10.1007/s10763-014-9571-8

Svandova, K. (2014). Secondary school students’ misconceptions about photosynthesis and plant respiration: Preliminary results. Eurasia Journal of Mathematics, Science & Technology Education, 10(1), 59-67. https://doi.org/10.12973/eurasia.2014.1018a

Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123-138. https://doi.org/10.1007/s10648-010-9128-5

Sweller, J., Ayres, P. L., Kalyuga, S. & Chandler, P. A. (2003). The expertise reversal effect. Educational Psychologist, 38 (1), 23-31. http://dx.doi.org/10.1207/S15326985EP3801_4

Tas, E., Cepni, S., & Kaya, E. (2012). The effects of web-supported and classical concept maps on students' cognitive development and misconception change: A case study on photosynthesis. Energy Education Science and technology Part B: Social and Educational Studies, 4(1), 241-252. https://odu-tr.academia.edu/ErdemKaya

Tippett, C. D. (2010). Refutation text in science education: A review of two decades of research. International Journal of Science and Mathematics Education, 8(6), 951-970. http://doi.org/10.1007/s10763-010-9203-x

Van Den Broek, P., & Kendeou, P. (2008). Cognitive processes in comprehension of science texts: The role of co?activation in confronting misconceptions. Applied Cognitive Psychology, 22(3), 335-351. http://doi.org/10.1002/acp.1418

VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2(1), 1-59. https://www.jstor.org/stable/1466684

Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177. https://doi.org/10.1007/s10648-005-3951-0

Vaughn, A. R., Brown, R. D., & Johnson, M. L. (2020). Understanding conceptual change and science learning through educational neuroscience. Mind, Brain, and Education, 14(2), 82-93. https://doi.org/10.1111/mbe.12237

Vosniadou, S. (2007). Conceptual change and education. Human Development, 50(1), 47-54. https://doi.org/10.1159/000097684

Warfa, A. R. M. (2016). Mixed-methods design in biology education research: Approach and uses. CBE—Life Sciences Education, 15(4), rm5. https://doi.org/10.1187/cbe.16-01-0022

Williams, J. J., & Lombrozo, T. (2010). The role of explanation in discovery and generalization: Evidence from category learning. Cognitive Science, 34(5), 776-806. https://doi.org/10.1111/j.1551-6709.2010.01113.x

Williams, J. J., Lombrozo, T., & Rehder, B. (2013). The hazards of explanation: Overgeneralization in the face of exceptions. Journal of Experimental Psychology: General, 142(4), 1006. http://doi.org/10.1037/a0030996

Yu, C. H. (2010). Exploratory data analysis in the context of data mining and resampling. International Journal of Psychological Research, 3(1), 9-22. http://doi.org/10.21500/2011208


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