İŞBİRLİKLİ ÖĞRENME İLE BİRLİKTE KULLANILAN MODELLERİN, ANİMASYONLARIN VE YEDİ İLKE’NİN KİMYA BAŞARISINA ETKİSİ

Year 2020, Issue: 41, 1 - 25, 29.12.2020

Mustafa Alyar Kemal Domuş

https://doi.org/10.33418/ataunikkefd.781598

Abstract

Kimya, içerisinde birçok soyut kavramın yer aldığı bir disiplindir. Kimyanın öğrenilmesi için soyut yapıların anlaşılmasının önemli olduğu düşünülmektedir. Bu yüzden bu araştırma, soyut kavramların anlaşılması da göz önünde bulundurularak, işbirlikli öğrenme ile animasyonların, modellerin (oyun hamuru ve çubuk-top) ve yedi ilkenin (lisans eğitiminde niteliği arttırmak amacıyla ileri sürülen iyi bir eğitim için yedi ilke) birlikte uygulanmasının kimya başarısına etkisini incelemektedir. Araştırma ön test-son test karşılaştırmalı grup yarı deneysel desene göre yürütülmüştür. Araştırmaya 91 fen bilgisi öğretmenliği birinci sınıf öğrencisi katılmıştır. Öğrenciler dört farklı gruba ayrılmış ve birinci grupta işbirlikli öğrenme, ikinci grupta işbirlikli öğrenme ve yedi ilke, üçüncü grupta işbirlikli öğrenme ve animasyon, dördüncü grupta ise işbirlikli öğrenme ve modellerle uygulamalar gerçekleştirilmiştir. Katılımcılardan veriler iki ölçekle toplanmıştır. Deney gruplarının homojen olma durumlarını belirlemek için Ön Bilgi Testi, uygulanan yöntem ve tekniklerin kimya başarısına etkisini belirlemek için Akademik Başarı Testi kullanılmıştır. Araştırmadan elde edilen bulgular incelendiğinde işbirlikli öğrenmenin yedi ilke ile birlikte uygulanmasının kimya başarısı üzerinde ciddi bir etkisi (p<.05; η2=0,13) olduğu sonucuna erişilmiştir.

Keywords

İşbirlikli Öğrenme, Animasyon, Model, Yedi İlke, Kimya Başarısı

Supporting Institution

Atatürk Üniversitesi, Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

PRJ2015/413

Thanks

Atatürk Üniversitesi, Bilimsel Araştırma Projeleri Koordinasyon Birimi'ne bu araştırmanın gerçekleştirilmesi için vermiş olduğu finansal destekten dolayı teşekkür ederiz.

References

• Abramczyk, A. and Jurkowski, S. (2020). Cooperative learning as an evidence-based teaching strategy: What teachers know, believe, and how they use it. Journal of Education for Teaching, 46(3), 296-308. doi: 10.1080/02607476.2020.1733402
• Adadan, E., Irving, K. E. and Trundle, K. C. (2009). Impacts of multi‐representational instruction on high school students’ conceptual understandings of the particulate nature of matter. International Journal of Science Education, 31(13), 1743-1775, DOI: 10.1080/09500690802178628
• 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. DOI: 10.1039/c6rp00067c.
• Aktan, M. B. (2016). Pre-service science teachers’ perceptions and attitudes about the use of models. Journal of Baltic Science Education, 15(1), 7-17.
• Allred, Z. D. R. and Bretz, S. L. (2019). University chemistry students’ interpretations of multiple representations of the helium atom. Chemistry Education Research and Practice, 20(2), 358-368.
• Alsancak, D. ve Altun, A. (2011). Bilgisayar destekli işbirlikli öğrenme ortamlarında geçişken bellek ile grup uyumu, grup atmosferi ve performans arasındaki ilişki. Eğitim Teknolojisi Kuram ve Uygulama, 1 (2), 1-16.
• Ardac, D. and 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.
• Aydoğdu, S. (2012). Üniversite öğretim elemanlarının Chickering ve Gamson öğrenme ilkelerini kullanma düzeyleri (Doktora tezi). Yüksek Öğretim Kurulu Ulusal Tez Merkezi’nden edinilmiştir. (Tez No. 319647)
• Bamberger, Y. M. and Davis, E. A. (2013). Middle-school science students’ scientific modelling performances across content areas and within a learning progression, International Journal of Science Education, 35(2), 213-238. DOI: 10.1080/09500693.2011.624133
• Barak, M., Ashkar, T. and Dori, Y. J. (2011). Learning science via animated movies: Its effect on students’ thinking and motivation. Computers & Education, 56(3), 839-846.
• Barnea, N. and Dori, Y. J. (2000). Computerized molecular modeling the new technology for enhancing model perception among chemistry educators and learners. Chemistry Education: Research and Practice in Europe, 1(1), 109–120.
• Batts, D. L. (2005). Perceived agreement between student and instructor on the use of the seven principles for good practice in undergraduate education in online courses. (Doctoral dissertation). ProQuest Dissertations and Theses database. (UMI No. 3166877)
• Bergqvist, A. and Rundgren, S. C. (2017). The influence of textbooks on teachers’ knowledge of chemical bonding representations relative to students’ difficulties understanding. Research in Science & Technological Education, 35(2), 215-237. https://doi.org/10.1080/02635143.2017.1295934
• Bolliger, D. U. and Martin, F. (2018). Instructor and student perceptions of online student engagement strategies. Distance Education, 39(4), 568-583. doi: 10.1080/01587919.2018.1520041
• Burke, K. A., Greenbowe, T. J. and Windschitl, M. A. (1998). Developing and using conceptual computer animations for chemistry instruction. Journal of Chemical Education, 75 (12), 1658–1661.
• Caboni, T. C., Mundy, M. E. and Duesterhaus, M. B. (2002). The implications of the norms of undergraduate college students for faculty enactment of principles of good practice in undergraduate education. Peabody Journal of Education, 77(3), 125-137.
• Campbell, T., Longhurst, M. L., Wang, S. K., Hsu, H. Y. and Coster, D. C. (2015). Technologies and reformed-based science instruction: the examination of a professional development model focused on supporting science teaching and learning with technologies. Journal of Science Education and Technology, 24(5), 562–579. https://doi.org/10.1007/s10956-015-9548-6.
• Chan, C. K. Y. (2015). Use of animation in engaging teachers and students in assessment in Hong Kong higher education. Innovations in Education and Teaching International, 52(5), 474–484. https://doi.org/10.1080/14703297.2013.847795
• Chan, M. (2020). A multilevel SEM study of classroom talk on cooperative learning and academic achievement: Does cooperative scaffolding matter?. International Journal of Educational Research, 101, 101564.
• Chang, H. Y. and Quintana, C. (2006). Student-generated animations: Supporting middle school students’ visualization, interpretation and reasoning of chemical phenomena. In Proceedings of the 7th international conference of the learning sciences. Bloomington, IN: Lawrence Erlbaum Associates.
• Chickering, A. W. and Gamson, Z. (1999). Development and adaptations of the seven principles for good practice in undergraduate education. New Directions for Teaching and Learning, 80, 75-81.
• Chickering, A.W. and Gamson, Z. (1987). Seven principles of good practice in undergraduate education. AAHE Bulletin, 39 (7), 3-7.
• Chiou, C. C., Tien, L. C. and Lee, L. T. (2015). Effects on learning of multimedia animation combined with multidimensional concept maps. Computers and Education, 80, 211-223. https://doi.org/10.1016/j.compedu.2014.09.002
• Chiu, M. H., Chou, C. C. and 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. Cisterna, D., Forbes, C. T. and Roy, R. (2019). Model-based teaching and learning about inheritance in third-grade science. International Journal of Science Education, 41(15), 2177-2199.
• Costouros, T. (2020). Jigsaw cooperative learning versus traditional lectures: Impact on student grades and learning experience. Teaching & Learning Inquiry, 8(1), 154-172.
• Crews, T. B., Wilkinson, K. and Neill, J. K. (2015). Principles for good practice in undergraduate education: Effective online course design to assist students’ success. Journal of Online Learning and Teaching, 11(1), 87-103.
• Çavdar, O. and Doymuş, K. (2018). Karışımlar konusunun öğretilmesinde işbirlikli öğrenme yönteminin iyi bir eğitim ortamı için yedi ilke ve modellerle kullanılması. Eğitimde Kuram ve Uygulama, 14(3), 325–346.
• Dalacosta, K., Kamariotaki-Paparrigopoulou, M., Palyvos, J. A. and Spyrellis, N. (2009). Multimedia application with animated cartoons for teaching science in elementary education. Computers & Education, 52(4), 741-748.
• Dinçer, S. ve Balaman, F. (2019). Sosyal medyanın öğretim faaliyetlerinde kullanılmasının öğrenci, öğretmen ve veliler açısından değerlendirilmesi: Edmodo örneği. Trakya Üniversitesi Sosyal Bilimler Dergisi, 21(2), 887-907, DOI: 10.26468/trakyasobed.580410.
• Doymuş, K. (2008). Teaching chemical bonding through jigsaw cooperative learning. Research in Science & Technological Education, 26 (1), 47-57.
• Ebenezer, J. V. (2001). A hypermedia environment to explore and negotiate students conceptions animation of the solution process of table salt. Journal of Science Education and Technology, 10(1), 73–92.
• Eilks, I. (2005). Experiences and reflections about teaching atomic structure in a jigsaw classroom in lower secondary school chemistry lessons. Journal of Chemical Education, 82 (2), 313-319.
• Engida, T. (2014). Chemistry teacher professional development using the technological pedagogical content knowledge (TPACK) framework. African Journal of Chemical Education, 4(3), 2-21.
• Estébanez, R. P. (2016). An approachment to cooperative learning in higher education: comparative study of teaching methods in engineering. EURASIA Journal of Mathematics, Science and Technology Education, 13(5), 1331-1340.
• Falcão, D., Colinvaux, D., Krapas, S., Querioz, G., Alves, F., Cazelli, S., ... and Gouvea, G. (2004). A model‐based approach to science exhibition evaluation: A case study in a Brazilian astronomy museum. International Journal of Science Education, 26(8), 951-978.
• Fredrickson, J. (2015). Online learning and student engagement: Assessing the impact of a collaborative writing requirement. Academy of Educational Leadership Journal, 19(3), 127-140. Fulmer, G. W. and Liang, L. L. (2013). Measuring model-based high school science instruction: Development and application of a student survey. Journal of Science Education and Technology, 22(1), 37–46. https://doi.org/10.1007/s10956-012-9374-z.
• García-Almeida, D. J. and Cabrera-Nuez, M. T. (2020). The influence of knowledge recipients’ proactivity on knowledge construction in cooperative learning experiences. Active Learning in Higher Education, 21(1), 79-92.
• Gilbert, J. K., Boulter, C. and Rutherford, M. (1998). Models in explanations, part 1: horses for courses? International Journal of Science Education, 20(1), 83–97.
• Gilbert, J. K., Justi, R., van Driel, J. H., de Jong, O. and Treagust, D. F. (2004). Securing a future for chemical education. Chemistry Education: Research and Practice, 5(1), 5-14.
• Gillies, R. M. (2017). Promoting academically productive student dialogue during collaborative learning. International Journal of Educational Research, 97, 200–209. https://doi.org/10.1016/j.ijer.2017.07.014.
• Gouvea, J. and Passmore, C. (2017). ‘Models of’ versus ‘models for’ toward an agent-based conception of modeling in the science classroom. Science & Education, 26, 49–63. https://doi.org/10.1007/s11191-017-9884-4
• Halloun, I. (1996). Schematica modelling for meaningful learning of physics. Journal of Research in Science Teaching, 33(9), 1019–1041.
• Harrison, A. G. and Treagust, D. F. (2000). Learning about atoms, molecules, and chemical bonds: A case study of multiple‐model use in grade 11 chemistry. Science Education, 84(3), 352-381.
• Hattie, J. (2015). The applicability of visible learning to higher education. Scholarship of Teaching and Learning in Psychology, 1(1), 79–91. https://doi.org/10.1037/ stl0000021.
• Helm, C. (2017). Effects of social learning networks on student academic achievement and pro-social behavior in accounting. Journal for Educational Research Online, 9(1), 52-76.
• Hitt, A., White, O. and Hanson, D. (2005). Popping the kernel modeling the states of matter. Science Scope, 28(4), 39-41.
• Holzinger, A., Kickmeier-Rust, M. and Albert, D. (2008). Dynamic media in computer science education; content complexity and learning performance: is less more?. Journal of Educational Technology & Society, 11(1), 279-290.
• Hung, V. and Fung, D. (2017). The effectiveness of hybrid dynamic visualisation in learning genetics in a Hong Kong secondary school. Research in Science & Technological Education, 35(3), 308-329.
• Johnson, D., Johnson, R. and Smith, K. A. (1990). Cooperative learning: An active learning strategy. FOCUS on Teaching and Learning, 5(2), 1-8.
• Johnson, S. (2014). Applying the seven principles of good practice: Technology as a lever-in an online research course, Journal of Interactive Online Learning, 13(2), 41-50.
• Johnstone, A. H. (1982). Macro and microchemistry. School Science Review, 64, 295-305.
• Junco, R., Heibergert, G. and Lokent, E. (2011). The effect of twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27, 119-132.
• Karaçöp, A. and Doymuş, K. (2012). Effects of jigsaw cooperative learning and animation techniques on students’ understanding of chemical bonding and their conceptions of the particulate nature of matter. Journal of Science Education Technology, 22, 186-203.
• Kelly, R. M. and 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.
• Kelly, R. M. and S. Akaygun (2016). Insights into how students learn the difference between a weak acid and a strong acid from cartoon tutorials employing visualizations. Journal of Chemical Education 93(6), 1010-1019.
• Kelly, R. M., Phelps, A. J. and Sanger, M. J. (2004). The effects of a computer animation on students’ conceptual understanding of a can-crushing demonstration at the macroscopic, microscopic, and symbolic levels. Chemical Educator, 9(3), 184-189.
• Kim, S. I., Yoon, M., Whang, S. M., Tversky, B. and Morrison, J. B. (2007). The effect of animation on comprehension and interest. Journal of Computer Assisted Learning, 23(3), 260-270.
• Kocaman Karoğlu, A., Kiraz, E. and Özden, M. Y. (2014). Good practice principles in an undergraduate blended course design. Education and Science, 39 (173), 249-263.
• Kyndt, E., Raes, E., Lismont, B., Timmers, F., Cascallar, E. and Dochy, F. (2013). A meta-analysis of the effects of face-to-face cooperative learning. Do recent studies falsify or verify earlier findings?. Educational Research Review, 10, 133-149.
• Lalit, M. and Piplani, S. (2019). Active learning methodology–jigsaw technique: An innovative method in learning anatomy. Journal of the Anatomical Society of India, 68(2), 147-152.
• Lin, E. (2006). Cooperative learning in the science classroom. The Science Teacher, 73 (1), 33-39.
• Louca, L. T. and Zacharia, Z. C. (2015). Examining learning through modeling in K-6 science education. Journal of Science Education and Technology, 24(2-3), 192-215.
• Maia, P. F. and Justi, R. (2009). Learning of chemical equilibrium through modelling-based teaching. International Journal of Science Education, 31(5), 603–630.
• Marcos, R. I. S., Fernández, V. L., González, M. T. D. and Phillips-Silver, J. (2020). Promoting children’s creative thinking through reading and writing in a cooperative learning classroom. Thinking Skills and Creativity, 36, 100663.
• McMillan, J.H. and Schumacher, S. (2010). Research in education: Evidence-based inquiry. London: Pearson.
• McRae, K., Nedjadrasul, D., Pau, R., Lo, B. P. H. and King, L. (2018). Abstract concepts and pictures of real‐world situations activate one another. Topics in Cognitive Science, 10(3), 518-532.
• Medero, G. S. and Albaladejo, G. P. (2020). The use of a wiki to boost open and collaborative learning in a Spanish university. Knowledge Management & E-Learning: An International Journal, 12(1), 1-17.
• Mendonça, P. C. C. and Justi, R. (2011). Contributions of the model of modelling diagram to the learning of ionic bonding: analysis of a case study. Research in Science Education, 41(4), 479–503.
• Mukawa, T. E. (2006). Meta-analysis of the effectiveness of online instruction in higher education using Chickering and Gamson’s seven principles for good practice. Yayımlanmamış Doktora Tezi. The University of San Francisco, San Francisco.
• Musaitif, L. M. (2013). The utilization of the seven principles for good practices of full-time and adjunct faculty in teaching health & science in community colleges (Doctoral dissertation). ProQuest Dissertations and Theses database. (UMI No. 3570995)
• Nakhleh, M. B. (1992). Why some students don't learn chemistry: Chemical misconceptions. Journal of Chemical Education, 69(3), 191-196.
• O'Dwyer, A. and Childs, P. E. (2017). Who says organic chemistry is difficult? Exploring perspectives and perceptions. EURASIA Journal of Mathematics, Science and Technology Education, 13(7), 3599-3620.
• Okumuş, S. ve Doymuş, K. (2018). İyi bir eğitim ortamı için yedi ilkenin işbirlikli öğrenme ve modellerle birlikte uygulanmasının 6. sınıf öğrencilerinin fen başarısına etkisi. Bayburt Eğitim Fakültesi Dergisi, 13(25), 203–238.
• Okumuş, S., Öztürk, B., Koç, Y., Çavdar, O. ve Aydoğdu, S. (2013). İşbirlikli öğrenme modeli ve iyi bir eğitim için yedi ilkenin sınıfta birlikte uygulanması. Ekev Akademi Dergisi, 57, 493-502.
• Oliva, J. M., Aragón, M. D. M. and Cuesta, J. (2015) The competence of modelling in learning chemical change: a study with secondary school students. International Journal of Science and Mathematics Education, 13, 751-791.
• Özmen, H. and Naseriazar, A. (2018). Effect of simulations enhanced with conceptual change texts on university students’ understanding of chemical equilibrium. Journal of the Serbian Chemical Society, 83(1), 121-137.
• Öztürk, B. ve Doymuş, K. (2018). İyi bir eğitim ortamı için yedi ilke ve modellerle desteklenen işbirlikli öğrenme yöntemlerinin akademik başarıya etkisi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22(2), 1957–1976.
• Phelps, R. P. (2019). Test frequency, stakes, and feedback in student achievement: A meta-analysis. Evaluation Review, 43(3-4), 111-151.
• Pollock, E., Chandler, P. and Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12(1), 61-86.
• Raviola, A. (2001). Assessing students’ conceptual understanding of solubility equilibrium. Journal of Chemical Education, 78(5), 629–631.
• Rieber, L. P. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82(1), 135-140.
• Ritter, M. E. and Lemke, K. A. (2000). Addressing the 'seven principles for good practice in undergraduate education' with internet-enhanced education. Journal of Geography in Higher Education, 24(1), 100-108.
• Russell, J. W., Kozma, R. B., Jones, T., Wykoff, J., Marx, N. and Davis, J. (1997). Use of simultaneous-synchronized macroscopic, microscopic, and symbolic representations to enhance the teaching and learning of chemical concepts. Journal of Chemical Education, 74(3), 330-334.
• Salame, I. I., Patel, S. and Suleman, S. (2019). Examining some of the students’ challenges in learning organic chemistry. International Journal of Chemistry Education Research, 3(1), 6-14.
• Samon, S. and Levy, S. T. (2017). Micro–macro compatibility: When does a complex systems approach strongly benefit science learning?. Science Education, 101(6), 985-1014.
• Sanger, M. J., Phelps, A. J. and Fienhold, J. (2000). Using a computer animation to improve students' conceptual understanding of a can-crushing demonstration. Journal of Chemical Education, 77(11), 1517-1520.
• Schauble, L., Glaser, R., Raghavan, K. and Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. The Journal of the Learning Sciences, 1(2), 201–238.
• Schwarz, C. V. and White, B. Y. (2005). Metamodelling knowledge: Developing students’ understanding of scientific modelling. Cognition and Instruction, 23(2), 165–205.
• Siegel, C. (2005). Implementing a research-based model of cooperative learning. The Journal of Educational Research, 98(6), 339–351.
• Sormunen, K., Juuti, K. and Lavonen, J. (2020). Maker-centered project-based learning in inclusive classes: Supporting students’ active participation with teacher-directed reflective discussions. International Journal of Science and Mathematics Education, 18(4), 691-712. Sotiriou, S. and Bogner, F. X. (2008). Visualizing the invisible: augmented reality as an innovative science education scheme. Advanced Science Letters, 1(1), 114–122. https://doi.org/10.1166/asl.2008.012.
• Sowan, A. K. and Jenkins, L. S. (2013). Designing, delivering and evaluating a distance learning nursing course responsive to students needs. International Journal of Medical Informatics, 82(6), 553-564.
• Stull, A. T., Gainer, M., Padalkar, S. and Hegarty, M. (2016). Promoting representational competence with molecular models in organic chemistry. Journal of Chemical Education, 93, 994–1001. https://doi.org/10.1021/acs.jchemed.6b00194
• Symington, D. and Kirkwood, V. (1996). Lecturer perceptions of student difficulties in a first-year chemistry course. Journal of Chemical Education, 73(4), 339-343.
• Şimşek, Ü., Doymuş, K. ve Karaçöp, A. (2008). Çözeltiler ünitesinde uygulanan grup araştırması tekniğinin öğrencilerin maddenin tanecikli yapısını anlamalarına ve akademik başarılarına etkisi. Bayburt Eğitim Fakültesi Dergisi, 3(1), 87-99.
• Taber, K. S. (2002). Alternative conceptions in chemistry-prevention, diagnosis and cure: Theoretical background. London: The Royal Society of Chemistry.
• Tanis, C. J. (2020). The seven principles of online learning: Feedback from faculty and alumni on its importance for teaching and learning. Research in Learning Technology, 28, 2319. http://dx.doi.org/10.25304/rlt.v28.2319
• The Ohio Learning Network. (2002). Quality learning in Ohio and at a distance: A report of the Ohio Learning network Task Force on quality in distance learning. Erişim Mayıs, 2015. http://www.oln.org/ILT/7_principles/learn_more.php
• Tirell, T. (2012). Chickering’s seven principles of good practice: Student attrition in community college online courses. Community College Journal of Research and Practice, 36(8), 580-590.
• Tou, N. X., Kee, Y. H., Koh, K. T., Camiré, M. and Chow, J. Y. (2020). Singapore teachers’ attitudes towards the use of information and communication technologies in physical education. European Physical Education Review, 26(2), 481-494.
• URL-1, Edmodo homepage. https://www.edmodo.com/?language=tr
• URL-2, http://www.mhhe.com/physsci/chemistry/animations/chang_7e_esp/ (Erişim Tarihi: Şubat, 2016).
• URL-3, http://www.satriwit3.ac.th/files/1210252020285154/files/decomposition.swf (Erişim Tarihi: Şubat, 2016).
• URL-4, http://www.mhhe.com/physsci/chemistry/essentialchemistry/flash/flash.mhtml (Erişim Tarihi: Şubat, 2016).
• URL-5, http://www.mhhe.com/physsci/chemistry/essentialchemistry/flash/flash.mhtml (Erişim Tarihi: Şubat, 2016). URL-6, https://pupils.highschoolofdundee.org.uk/dept/chemistry/default.aspx (Erişim Tarihi: Şubat, 2016).
• URL-7, http://www.johnwiley.net.au/highered/chemistry/molvis/25-entropy.swf (Erişim Tarihi: Şubat, 2016).
• URL-8, http://www.mhhe.com/physsci/chemistry/essentialchemistry/flash/flash.mhtml (Erişim Tarihi: Şubat, 2016).
• Waits, T. and Lewis, L. (2003). Distance education at degree-granting postsecondary institutions: 2000-2001 (NCES 2003–017). Erişim Şubat, 2017. http://nces.ed.gov/pubs2003/2003017.pdf
• Wang, H. C., Chang, C. Y. and Li, T. Y. (2007). The comparative efficacy of 2D-versus 3D-based media design for influencing spatial visualization skills. Computers in Human Behavior, 23(4), 1943-1957.
• Wei, S., Liu, X. and Jia, Y. (2013). Using Rasch measurement to validate the instrument of students’ understanding of models in science (SUMS). International Journal of Science and Mathematics Education, 12(5), 1067–1082.
• Whittle, R. J., Telford, A. and Benson, A. C. (2019). Insights from senior-secondary physical education students on teacher-related factors they perceive to influence academic achievement. Australian Journal of Teacher Education (Online), 44(6), 69-90.
• Winegar, M.L. An exploration of seven principles for good practice in Web-based courses. Ph.D. thesis, University of South Dakota. Erişim Ağustos, 2017. https://www.learntechlib.org/p/127305/.
• Wu, H. K. and Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88(3), 465-492.
• Yang, E. M., Andre, T., Greenbowe, T. J. and Tibell, L. (2003). Spatial ability and the impact of visualization/animation on learning electrochemistry. International Journal of Science Education, 25(3), 329-349.
• Yaseen, Z. (2018). Using student-generated animations: The challenge of dynamic chemical models in states of matter and the invisibility of the particles. Chemistry Education Research and Practice, 19(4), 1166–1185. https://doi.org/10.1039/c8rp00136g
• Ye, J., Lu, S. and Bi, H. (2019). The effects of microcomputer-based laboratories on students macro, micro, and symbolic representations when learning about net ionic reactions. Chemistry Education Research and Practice, 20(1), 288-301.