Abstract
The study investigated the effects of the use of dynamic computer visualizations (DCVs) integrated with the POE (Predict-Observe-Explain) sequence on learners’ understanding and retention of solubility and on their motivation to learn chemistry. In the research, nonequivalent groups pretest–posttest control and comparison group design was used. The study was conducted on 56 freshmen enrolled in the science teacher education program at a mid-sized university in northwestern Turkey. One of the two classes was randomly assigned as an experimental group (n = 31), and the other was assigned as a control group (n = 25). The data were collected using the Solubility Concept Test (SCT), Chemistry Motivation Questionnaire-II (CMQ-II), Molecular Level Visualization Test (MLVT), and Questionnaire of Students’ Perceptions of DCVs (QSP-DCVs). The quantitative data were analyzed using repeated-measures MANOVA, and the qualitative data were analyzed using content analysis. The results indicated that the use of DCVs integrated with the POE sequence was more effective on learners’ understanding of solubility than the traditional lecturer-centered narrative-based teaching method. It was found that some prior misconceptions were retained or reinforced even though the use of DCVs integrated with the POE sequence was used. This study also found that the use of DCVs integrated with the POE sequence had a considerable significant effect on learners’ retention of what they learned. Although there was no statistically significant difference in the change taking place in motivation towards chemistry between the experimental and control group, experimental group learners’ perceptions of the use of DCVs were quite positive. Based on the study, it is concluded that DCVs integrated with the POE sequence can be used as an effective way to improve learners’ understanding and retention of fundamental chemistry concepts.
Résumé
L’étude a examiné les effets de l’utilisation de visualisations informatiques dynamiques (VID) intégrées à la séquence POE (Prédire — Observer — Expliquer) sur la compréhension et la rétention de la solubilité par les apprenants et sur leur motivation à apprendre la chimie. Pour la recherche, nous avons utilisé un modèle de contrôle et de comparaison de groupes non équivalents (prétest — test de contrôle). L’étude a été menée auprès de 56 étudiants de première année inscrits au programme de formation des enseignants en sciences dans une université de taille moyenne du nord-ouest de la Turquie. L’une des deux classes a été désignée au hasard comme groupe expérimental (n = 31), et l’autre comme groupe de contrôle (n = 25). Nous avons recueilli les données à l'aide de l’épreuve du concept de solubilité (ECS), du questionnaire de motivation pour la chimie-II (QMC-II), du test de visualisation du niveau moléculaire (TVNM) et du questionnaire sur la perception des VID par les étudiants (QPE-VIDs). Nous avons analysé les données quantitatives à l’aide d’une répétition d’analyses de la variance à plusieurs variables et les données qualitatives au moyen d’une analyse de contenu. Les résultats indiquent que l’utilisation de VID intégrées à la séquence POE a été plus efficace en ce qui a trait à la compréhension de la solubilité par les apprenants que la méthode d’enseignement courante centrée sur l’exposé et fondée sur la narration. Il a été constaté que certaines idées fausses préexistantes ont été conservées ou renforcées malgré l’utilisation de VID intégrées à la séquence POE. Cette étude a également montré que l’utilisation de VID intégrées à la séquence POE avait un effet considérable sur la rétention des connaissances acquises par les apprenants. Bien qu’il n’y ait pas de différence statistiquement significative entre le groupe expérimental et le groupe de contrôle en ce qui concerne l’évolution de la motivation à l’égard de la chimie, les apprenants du groupe expérimental ont montré qu’ils avaient un sentiment favorable envers l’utilisation des VID. Sur la base de cette étude, nous concluons que les VID intégrées à la séquence POE peuvent être utilisées comme moyens efficaces d’améliorer la compréhension et la rétention des concepts fondamentaux de la chimie par les apprenants.
Similar content being viewed by others
Data Availability
The data supporting this study's findings are available from the corresponding author upon reasonable request.
References
Adadan, E., & Savasci, F. (2012). An analysis of 16–17-year-old students’ understanding of solution chemistry concepts using a two-tier diagnostic instrument. International Journal of Science Education, 34(4), 513–544. https://doi.org/10.1080/09500693.2011.636084
Akaygun, S. (2016). Is the oxygen atom static or dynamic? The effect of generating animations on students’ mental models of atomic structure. Chemistry Education Research and Practice, 17(4), 788–807. https://doi.org/10.1039/C6RP00067C
Akaygun, S. (2015). Visualization of unseen: the use of dynamic computer visualizations in chemistry education [Görünmeyeni görselleştirme: Kimya eğitiminde hareketli bilgisayar görsellerinin kullanımı]. In Ayas A. & Sözbilir, M. (Eds.), Teaching of chemistry: Good practice eaxmples for faculties, teachers and pre-service teachers [Kimya öğretimi: Öğretmen eğitimcileri, öğretmenler ve öğretmen adayları için iyi uygulama örnekleri] (pp. 679–711). Ankara: Pegem Akademi.
Akpinar, E. (2014). The use of interactive computer animations based on POE as a presentation tool in primary science teaching. Journal of Science Education and Technology, 23(4), 527–537. https://doi.org/10.1007/s10956-013-9482-4
Al-Balushi, S. M., & Al-Hajri, S. H. (2014). Associating animations with concrete models to enhance students’ comprehension of different visual representations in organic chemistry. Chemistry Education Research and Practice, 15(1), 47–58. https://doi.org/10.1039/C3RP00074E
Ardac, D., & Akaygun, S. (2004). Effectiveness of multimedia‐based instruction that emphasizes molecular representations on students’ understanding of chemical change. Journal of Research in Science Teaching, 41(4), 317–337. https://doi.org/10.1002/tea.20005
Azizoglu, N., Alkan, M., & Geban, Ö. (2006). Undergraduate pre-service teachers’ understandings and misconceptions of phase equilibrium. Journal of Chemical Education, 83(6), 947–953. https://doi.org/10.1021/ed083p947
Bain, G. A., Yi, J., Beikmohamadi, M., Herman, T. M., & Patrick, M. A. (2006). Using physical models of biomolecular structures to teach concepts of biochemical structure and structure depiction in the introductory chemistry laboratory. Journal of Chemical Education, 83(9), 1322. https://doi.org/10.1021/ed083p1322
Barak, M., Ashkar, T., & Dori, Y. J. (2011). Learning science via animated movies: Its effect on students’ thinking and motivation. Computers & Education, 56(3), 839–846. https://doi.org/10.1016/j.compedu.2010.10.025
Barnea, N., & Dori, Y. J. (1999). High-school chemistry students’ performance and gender differences in a computerized molecular modeling learning environment. Journal of Science Education and Technology, 8(4), 257–271. https://doi.org/10.1023/A:1009436509753
Bell, R.L., & Smetana, L.K. (2008). Using computer simulations to enhance science teaching and learning. In R.L., Bell, J. Gess-Newsome, & J. Luft, (Eds.), Technology in the secondary science classroom. Washington, D.C.: National Science Teachers Association.
Bentler, P. M. (1990). Comparative fit indexes in structural modeling. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
Calik, M., & Ayas, A. (2005). A Cross-Age study on the understanding of chemical solutions and their components. International Education Journal, 6(1), 30–41.
Calik, M., Ayas, A., & Coll, R.K. (2010). Investigating the effectiveness of teaching methods based on a four-step constructivist strategy. Journal of Science Education and Technology,19(1), 32–48. https://doi.org/10.1007/s10956-009-9176-0
Cetinkaya, U., & Kirilmazkaya, G. (2022). The effect of POE method with PhET simulation on primary school student’s science attitudes and success: Greenhouse Effect. Education Quarterly Reviews, 5(4), 350–360. https://doi.org/10.31219/osf.io/3hbf7
ChemDemos (2012).UO Libraries Interactive Media Group. https://chemdemos.uoregon.edu/
Choi, H. J., & Johnson, S. D. (2005). The effect of context-based video instruction on learning and motivation in online courses. The American Journal of Distance Education, 19(4), 215–227. https://doi.org/10.1207/s15389286ajde1904_3
Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459. https://doi.org/10.3102/00346543053004445
Cook, M. P. (2006). Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Science Education, 90(6), 1073–1091. https://doi.org/10.1002/sce.20164
Correia, A. P., Koehler, N., Thompson, A., & Phye, G. (2019). The application of PhET simulation to teach gas behavior on the submicroscopic level: Secondary school students’ perceptions. Research in Science & Technological Education, 37(2), 193–217. https://doi.org/10.1080/02635143.2018.1487834
Costu, B. (2008). Learning science through the PDEODE teaching strategy: Helping students make sense of everyday situations. Eurasia Journal of Mathematics, Science and Technology Education, 4(1), 3–9. https://doi.org/10.12973/ejmste/75300
Costu, B., Ayas, A., & Niaz, M. (2012). Investigating the effectiveness of a POE-based teaching activity on students’ understanding of condensation. Instructional Science, 40, 47–67. https://doi.org/10.1007/s11251-011-9169-2
Di Serio, Á., Ibáñez, M. B., & Kloos, C. D. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Computers & Education, 68, 586–596. https://doi.org/10.1016/j.compedu.2012.03.002
Dori, Y. J., & Barak, M. (2001). Virtual and physical molecular modeling: Fostering model perception and spatial understanding. Educational Technology & Society, 4(1), 61–74. https://www.jstor.org/stable/jeductechsoci.4.1.61
Duit, R. & Treagust, D. (2003). Conceptual change: A powerful framework for improving science teaching and learning. International Journal of Science Education, 25(6), 671–688. https://doi.org/10.1080/09500690305016
Ebbinghaus, H. (1885). Memory: A contribution to experimental psychology. Retrieved 10 July 2020 from http://psychclassics.yorku.ca/Ebbinghaus/memory6.htm
Erdem Ozcan, G., & Uyanik, G. (2022). The effects of the “Predict-Observe-Explain (POE)” strategy on academic achievement, attitude and retention in science learning. Journal of Pedagogical Research, 6(3), 103–111. https://doi.org/10.33902/JPR.202215535
Eryilmaz, A. (2010). Development and application of three-tier heat and temperature test: Sample of bachelor and graduate students. Eurasian Journal of Educational Research, 40, 53–76.
Falvo, D. (2008). Animations and simulations for teaching and learning molecular chemistry. International Journal of Technology in Teaching and Learning, 4(1), 68–77
Fensham, P. J., & Kass, H. (1988). Inconsistent or discrepant events in science instruction. Studies in Science Education,15(1), 1–16. https://doi.org/10.1080/03057268808559946
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). SAGE Publications
Gall, M. D., Borg, W. R. & Gall, J. P. (1996). Educational research: An introduction (6th ed.). White Plains, NY: Longman
Georghiades, P. (2004). Making pupils’ conceptions of electricity more durable by means of situated metacognition. International Journal of Science Education, 26(1), 85–99. https://doi.org/10.1080/0950069032000070333
Glynn, S.M., Brickman, P., Armstrong, N. & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48 (10), 1159–1176. https://doi.org/10.1002/tea.20442
Haidar, A. H., & Abraham, M. R. (1991). A comparison of applied and theoretical knowledge of concepts based on the particulate nature of matter. Journal of Research in Science Teaching, 28(10), 919–938. https://doi.org/10.1002/tea.3660281004
Haysom, J. & Bowen, M. (2010). Predict, observe, explain: Activities enhancing scientific understanding. NSTA Press
Hong, J. C., Hsiao, H. S., Chen, P. H., Lu, C. C., Tai, K. H., & Tsai, C. R. (2021). Critical attitude and ability associated with students’ self-confidence and attitude toward “predict-observe-explain” online science inquiry learning. Computers & Education, 166, 104172. https://doi.org/10.1016/j.compedu.2021.104172
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53–60.
Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75–83. https://doi.org/10.1111/j.1365-2729.1991.tb00230.x
Karamustafaoglu, S., & Mamlok-Naaman, R. (2015). Understanding electrochemistry concepts using the predict-observe-explain strategy. Eurasia Journal of Mathematics, Science and Technology Education, 11(5), 923–936. https://doi.org/10.12973/eurasia.2015.1364a
Kang, S., Scharmann, L. C., & Noh, T. (2004). Reexamining the role of cognitive conflict in science concept learning. Research in Science Education, 34, 71–96. https://doi.org/10.1023/B:RISE.0000021001.77568.b3
Kearney, M. (2004). Classroom use of multimedia-supported predict–observe–explain tasks in a social constructivist learning environment. Research in science education, 34, 427–453. https://doi.org/10.1007/s11165-004-8795-y
Kearney, M., & Treagust, D. F. (2001). Constructivism as a referent in the design and development of a computer program using interactive digital video to enhance learning in physics. Australasian Journal of Educational Technology, 17(1), 64–79. https://doi.org/10.14742/ajet.1773
Kearney, M., Treagust, D. F., Yeo, S., & Zadnik, M. G. (2001). Student and teacher perceptions of the use of multimedia supported predict–observe–explain tasks to probe understanding. Research in Science Education 31, 589–615. https://doi.org/10.1023/A:1013106209449
Kelly, R. M., & Jones, L. L. (2007). Exploring how different features of animations of sodium chloride dissolution affect students’ explanations. Journal of Science Education and Technology, 16(5), 413–429. https://doi.org/10.1007/s10956-007-9065-3
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York London: The Guilford Press
Kozma, R. B. (1994). Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42(2), 7–19. https://doi.org/10.1007/BF02299087
Krause, S., & Isaacs-Sodeye, W. (2013). The Effect of a Visually-Based Intervention on Students’ Misconceptions Related to Solutions, Solubility and Saturation in a Core Materials Course. 2013 ASEE Annual Conference Proceedings.
Krause, S., & Tasooji, A. (2007). Diagnosing students’ misconceptions on solubility and saturation for understanding of phase diagrams. ASEE Annual Conference and Exposition, Conference Proceedings.
Leech, N. L., Barrett, K. C. & Morgan, G. A. (2005). SPSS for Intermediate Statistics, Use and Interpretation. (2nd ed.). Lawrence Erlbaum Associates Inc., Mahwah.
Liew, C.-W. (2004). The effectiveness of predict-observe-explain technique in diagnosing students’ understanding of science and identifying their level of achievement. [Doctoral Thesis, Curtin University of Technology].
Liew, C. W., & Treagust, D. F. (1998). The effectiveness of predict-observe-explain tasks in diagnosing students' understanding of science and in identifying their levels of achievement. Annual Meeting of The AERA, San Diego, CA.
m3lls34 (2011, May 24). Super saturated solutions [Video file]. Retrieved from https://www.youtube.com/watch?v=1y3bKIOkcmk&t=47s
Marbach‐Ad, G., Rotbain, Y., & Stavy, R. (2008). Using computer animation and illustration activities to improve high school students’ achievement in molecular genetics. Journal of Research in Science Teaching, 45(3), 273–292. https://doi.org/10.1002/tea.20222
Mayer, R. E., & Moreno, R. (2002). Aids to computer-based multimedia learning. Learning and Instruction, 12, 107–119. https://doi.org/10.1016/S0959-4752(01)00018-4
McMillan, J. H., & Schumacher, S. (2006). Research in education: Evidence based inquiry (6th ed). New York, Pearson Education.
Nakhleh, M. B. (1992). Why some students don’t learn chemistry: Chemical misconceptions. Journal of Chemical Education, 69(3), 191–196. https://doi.org/10.1021/ed069p191
Napier, J. D., & Riley, J. P. (1985). Relationship between affective determinants and achievement in science for seventeen-year-olds. Journal of Research in Science Teaching, 22(4), 365–383. https://doi.org/10.1002/tea.3660220407
National Research Council (1997). Science Teaching Reconsidered: A Handbook. Washington, DC: The National Academies Press. https://doi.org/10.17226/5287
O’day, D. H. (2007). The value of animations in biology teaching: a study of long-term memory retention. CBE—Life Sciences Education, 6(3), 217–223. https://doi.org/10.1187/cbe.07-01-0002
O’Neil, H. F., Chuang, S., & Huang, T. (2010). Instructional system provided feedback. Retrieved 15 July 2020 from https://www.sciencedirect.com/topics/neuroscience/dual-coding-theory
Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart &Winston.
Paivio, A. (1986). Mental representations: a dual coding approach. Oxford, UK: Oxford University Press.
Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45(3), 255–285. https://doi.org/10.1037/h0084295
Pinarbasi, T. & Canpolat, N. (2003). Students’ understanding of solution chemistry concepts. Journal of Chemical Education, 80(11), 1328–1332. https://doi.org/10.1021/ed080p1328
Pinarbasi, T. (2002). Investigation of effectiveness of conceptual change approach on understanding of solubility concepts (Publication No. 121471). [Doctoral Thesis, Atatürk University], Council of Higher Education Thesis Center.
Reynolds, C. R., Livingston, R. B., & Willson, V. (2006). Measurement and assessment in education. Boston, MA: Allyn & Bacon.
Rieber, L. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82, 135–140. https://doi.org/10.1037/0022-0663.82.1.135
Rieber, L., Tzeng, S.-C., Tribble, K., & Chu, G. (1996, April). Feedback and elaboration within a computer based simulation: A dual coding perspective. Paper presented at the Annual Meeting of the American Educational Research Association, New York, NY.
Robinson, C. & Nathan, M. (2001). Considerations of Learning and Learning Research: Revisiting the “Media Effects” Debate. Journal of Interactive Learning Research, 12(1), 69–88. Norfolk, VA: Association for the Advancement of Computing in Education (AACE). Retrieved March 6, 2023 from https://www.learntechlib.org/primary/p/8458/
Rotbain, Y., Marbach-Ad, G., & Stavy, R. (2008). Using a computer animation to teach high school molecular biology. Journal of Science Education and Technology, 17(1), 49–58. https://doi.org/10.1007/s10956-007-9080-4
Rosen, Y. (2009). The effects of an animation-based on-line learning environment on transfer of knowledge and on motivation for science and technology learning. Journal of Educational Computing Research, 40(4), 451–467. https://doi.org/10.2190/EC.40.4.d
Sesen, B. A. & Mutlu, A. (2016). Predict-Observe-Explain tasks in chemistry laboratory: Pre-service elementary teachers’ understanding and attitudes. Sakarya University Journal of Education, 6(2), 184 208. https://doi.org/10.19126/suje.46187
Sirhan, G., (2007). Learning difficulties in chemistry: An overview. Journal of Turkish Science Education, 4(2), 2–20.
Tasker, R., & Dalton, R. (2006). Research into practice: Visualisation of the molecular world using animations. Chemistry Education Research and Practice, 7(2), 141–159. https://doi.org/10.1039/B5RP90020D
The PhET Interactive Simulations Project (2002). http://phet.colorado.edu
Treagust, D. F., Mthembu, Z., & Chandrasegaran, A. L. (2014). Evaluation of the predict-observe-explain instructional strategy to enhance students’ understanding of redox reactions. In I. Devetak, & S. A. Glažar (Eds.). Learning with understanding in the chemistry classroom (pp. 265–286). Dordrecht: Springer Netherlands.
Tosun, C. (2013). Adaptation of Chemistry Motivation Questionnaire-II to Turkish: A validity and reliability study. Erzincan University Journal of Education Faculty, 15(1), 173–202.
Tro, N. J. (2011). Chemistry: A molecular approach (2th ed.). Pearson
Tuan, H. L., Chin, C. C., & Shieh, S. H. (2005). The development of a questionnaire to measure students’ motivation towards science learning. International Journal of Science Education, 27(6), 639–654. https://doi.org/10.1080/0950069042000323737
Velázquez-Marcano, A., Williamson, V. M., Ashkenazi, G., Tasker, R., & Williamson, K. C. (2004). The use of video demonstrations and particulate animation in general chemistry. Journal of Science Education and Technology, 13(3), 315–323. https://www.jstor.org/stable/40186650
Wang, S. K., & Reeves, T. C. (2007). The effects of a web-based learning environment on student motivation in a high school earth science course. Educational Technology Research and Development, 55(2), 169–192. https://doi.org/10.1007/s11423-006-0638-2
White, R., & Gunstone, R. (1992). Probing understanding. London and New York: The Falmer Press.
Williamson, V. M., & Abraham, M. R. (1995). The effects of computer animation on the particulate mental models of college chemistry students. Journal of Research in Science Teaching, 32(5), 521–534. https://doi.org/10.1002/tea.3660320508
Wu, H. K., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38(7), 821–842. https://doi.org/10.1002/tea.1033
Yalcin, F. A. (2012). Pre-service primary science teachers’ understandings of the effect of temperature and pressure on solid–liquid phase transition of water. Chemistry Education Research and Practice, 13(3), 369–377. https://doi.org/10.1039/C2RP20021J
Yildirim, A. & Simsek, H. (2006). Qualitative research methods in social sciences. [Sosyal bilimlerde nitel araştırma yöntemleri] (5th ed.), Ankara: Seckin Publishing.
Zacharia, Z. C. (2005). The impact of interactive computer simulations on the nature and quality of postgraduate science teachers’ explanations in physics. International Journal of Science Education, 27(14), 1741–1767. https://doi.org/10.1080/09500690500239664
Zhu, L., & Grabowski, B.L. (2006). Web-based animation or static graphics: is the extra cost of animation worth it? Jl. of Educational Multimedia and Hypermedia, 15(3), 329–347. https://www.learntechlib.org/primary/p/6292/
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The author declares no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kahraman, S. The Use of Dynamic Computer Visualizations Integrated with the POE Sequence: Its Effect on Learners’ Understanding, Retention, and Motivation. Can. J. Sci. Math. Techn. Educ. 23, 179–209 (2023). https://doi.org/10.1007/s42330-023-00284-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42330-023-00284-z