The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter

Ines Nuić; Saša Aleksej Glažar

doi:10.29333/ejmste/114483

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Ines Nuić ¹ ^* , Saša Aleksej Glažar ²

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¹ University of Sarajevo, BOSNIA AND HERZEGOVINA² University of Ljubljana, SLOVENIA^* Corresponding Author

Abstract

E-learning strategies for the teaching unit Structure and States of Matter were developed. Students’ achievements and the percentages of misconceptions were compared between experimental groups taught using web-based learning material (WBLM) used as homework after conventional teaching at school (EG1), and at school settings (EG2) with the control group (CG) taught with the teacher-centred approach. The results indicated that WBLM has potential in teaching since EG1 and EG2 students had higher achievements than CG students did on tests of knowledge. Appropriate statistical procedures were used to control the effects of students’ verbal and non-verbal intelligence, as well as their prior knowledge regarding the Particulate Nature of Matter. Certain misconceptions were also revealed in all groups of students, mostly related to transferring macroscopic properties to submicroscopic particles.

Keywords

structure and states of matter
web-based learning material
students’ achievements
misconception
macroscopic level
submicroscopic level

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2020, Volume 16, Issue 2, Article No: em1823

https://doi.org/10.29333/ejmste/114483

Publication date: 17 Dec 2019

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References

Adadan, E. (2013). Using multiple representations to promote grade 11 students’ scientific understanding of the particle theory of matter. Research in Science Education, 43(3), 1079-1105. https://doi.org/10.1007/s11165-012-9299-9.
Adbo, K., & Taber, K. S. (2009). Learners’ mental models of the particle nature of matter: A study of 16‐year‐old Swedish science students. International Journal of Science Education, 31(6), 757-786. https://doi.org/10.1080/09500690701799383.
Akaygun, S., & Jones, L. L. (2013). Dynamic visualizations: Tools for understanding the particulate nature of matter. In G. Tsaparlis & H. Sevian (Eds.), Concepts of matter in science education (pp. 281-300). Dordrecht, Netherlands: Springer Science + Business Media B.V. https://doi.org/10.1007/978-94-007-5914-5_13.
Al-Balushi, S. M. (2011). Students’ evaluation of the credibility of scientific models that represent natural entities and phenomena. International Journal of Science and Mathematics Education, 9(3), 571-601. https://doi.org/10.1007/s10763-010-9209-4.
Al-Balushi, S. M. (2013). The relationship between learners’ distrust of scientific models, their spatial ability, and the vividness of their mental images. International Journal of Science and Mathematics Education, 11(3), 707-732. https://doi.org/10.1007/s10763-012-9360-1.
Al-Balushi, S. M., & Al-Harthy, I. S. (2015). Students’ mind wandering in macroscopic and submicroscopic textual narrations and its relationship with their reading comprehension. Chemistry Education Research and Practice, 16(3), 680-688. https://doi.org/10.1039/C5RP00052A.
Al-Balushi, S. M., & Coll, R. K. (2013). Exploring verbal, visual and schematic learners’ static and dynamic mental images of scientific species and processes in relation to their spatial ability. International Journal of Science Education, 35(3), 460-489. https://doi.org/10.1080/09500693.2012.760210.
Anđelković, T., Anđelković, D., & Nikolić, Z. (2015, September). The impact of e-learning in chemistry education. Paper presented at The Sixth International Conference on E-Learning (eLearning-2015), Belgrade, Serbia. Retrieved from http://econference.metropolitan.ac.rs/files/pdf/2015/17-Tatjana-Andjelkovic-Darko-Andjelkovic-Zoran-Nikolic-The-Impact-of-eLearning-in-Chemistry-Education.pdf.
Andersson, B. (1990). Pupils’ conceptions of matter and its transformations (age 12–16). Studies in Science Education, 18(1), 53-85. https://doi.org/10.1080/03057269008559981.
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, 40(4), 317-337. https://doi.org/10.1002/tea.20005.
Ardac, D., & Akaygun, S. (2005). Using static and dynamic visuals to represent chemical change at molecular level. International Journal of Science Education, 27(11), 1269-1298. https://doi.org/10.1080/09500690500102284.
Awad, B. (2014). Empowerment of teaching and learning chemistry through information and communication technologies. African Journal of Chemical Education, 4(3), 34-47. Retrieved from https://www.ajol.info/index.php/ajce/article/view/104094.
Aydeniz, M., Bilican, K., & Kirbulut, Z. D. (2017). Exploring pre-service elementary science teachers’ conceptual understanding of particulate nature of matter through three-tier diagnostic test. International Journal of Education in Mathematics, Science and Technology (IJEMST), 5(3), 221-234. https://doi.org/10.18404/ijemst.296036.
Barke, H. D., Hazari, A., & Yitbarek, S. (2009). Misconceptions in Chemistry: Addressing Perceptions in Chemical Education. Berlin, Germany: Springer-Verlag.
Bebbel, D., Russell, M., & O’Dwyer, L. (2004). Measuring teachers’ technology uses: Why multiple-measures are more revealing. Journal of Research on Technology in Education, 37(1), 45–63. https://doi.org/10.1080/15391523.2004.10782425.
Berger, R., & Hänze, M. (2015). Impact of expert teaching quality on novice academic performance in the jigsaw cooperative learning method. International Journal of Science Education, 37(2), 294-320. https://doi.org/10.1080/09500693.2014.985757.
Bodner, G. M. (1986). Constructivism: a theory of knowledge. Journal of Chemical Education, 63(10), 873-878. https://doi.org/10.1021/ed063p873.
Boz, Y. (2006). Turkish pupils’ conceptions of the particulate nature of matter. Journal of Science Education and Technology, 15(2), 203-213. https://doi.org/10.1007/s10956-006-9003-9.
Bretz, S. L., & McClary, L. (2014). Students’ understandings of acid strength: how meaningful is reliability when measuring alternative conceptions? Journal of Chemical Education, 92(2), 212-219. https://doi.org/10.1021/ed5005195.
Chang, H. Y., Quintana, C., & Krajcik, J. S. (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94(1), 73-94. https://doi.org/10.1002/sce.20352.
Christian, B. N., & Yezierski, E. J. (2012). Development and validation of an instrument to measure student knowledge gains for chemical and physical change for grades 6–8. Chemistry Education Research and Practice, 13(3), 384-393. https://doi.org/10.1039/c2rp20041d.
Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (3rd ed). San Francisco, CA: Pfeiffer. https://doi.org/10.1002/9781118255971.
Dall’Oglio, A. M., Rossiello, B., Coletti, M. F., Caselli, M. C., Ravà, L., Di Ciommo, V., ... Pasqualetti, P. (2010). Developmental evaluation at age 4: validity of an Italian parental questionnaire. Journal of Paediatrics and Child Health, 46(7‐8), 419-426. https://doi.org/10.1111/j.1440-1754.2010.01748.x.
Devetak, I., & Glažar, S. A. (2014). Educational models and differences between groups of 16-year-old students in gender, motivation, and achievements in chemistry. In I. Devetak, & S. A. Glažar (Eds.), Learning with understanding in the chemistry classroom (pp. 209–231). Dordrecht, Netherlands: Springer Science + Business Media B.V. https://doi.org/10.1007/978-94-007-4366-3_6.
Eilks, I., Witteck, T., & Pietzner, V. (2010). Using multimedia learning aids from the internet for teaching chemistry: Not as easy as it seems? In S. Rodrigues (Ed.), Multiple Literacy and Science Education: ICTs in Formal and Informal Learning Environments (pp. 49-69). Hershey, PA: Information Science Reference. Limited preview. https://doi.org/10.4018/978-1-61520-690-2.ch004.
Ercan, O., Ural, E., & Özateş, D. (2016). The effect of web-assisted teaching on students’ achievement in the subject of mixtures and attitudes towards chemistry. Hacettepe University Journal of Education, 31(1), 163-179. https://doi.org/10.16986/HUJE.2015014089.
Faulconer, E. K., Griffith, J. C., Wood, B. L., Acharyya, S., & Roberts, D. L. (2018). A comparison of online and traditional chemistry lecture and lab. Chemistry Education Research and Practice, 19(1), 392-397. https://doi.org/10.1039/C7RP00173H.
Frailich, M., Kesner, M., & Hofstein, A. (2007). The influence of web‐based chemistry learning on students’ perceptions, attitudes, and achievements. Research in Science & Technological Education, 25(2), 179–197. https://doi.org/10.1080/02635140701250659.
Franco, G. A., & Taber, K. S. (2009). Secondary students’ thinking about familiar phenomena: Learners’ explanations from a curriculum context where ‘particles’ is a key idea for organising teaching and learning. International Journal of Science Education, 31(14), 1917-1952. https://doi.org/10.1080/09500690802307730.
Fuchs, T., & Woessmann, L. (2004). Computers and student learning: Bivariate and multivariate evidence on the availability and use of computers at home and at school. CEIS Working Paper No. 1321, 1-20. Retrieved from www.ifo.de/portal/pls/portal/docs/1/1188938.pdf.
Gabel, D. (2005). Enhancing students’ conceptual understanding of chemistry through integrating the macroscopic, particle, and symbolic representations of matter. In N. J. Pienta, M. M. Cooper, & T. Greenbowe (Eds.), Chemists’ Guide to Effective Teaching p. 77). Upper Saddle River, NJ: Pearson.
Gabel, D. L. (1993). Use of the particle nature of matter in developing conceptual understanding. Journal of Chemical Education, 70(3), 193-194. https://doi.org/10.1021/ed070p193.
Georghiades, P. (2000). Beyond conceptual change learning in science education: focusing on transfer, durability, and metacognition. Educational Research, 42(2), 119-139. https://doi.org/10.1080/001318800363773.
Gericke, N. M., & Hagberg, M. (2007). Definition of historical models of gene function and their relation to students’ understanding of genetics. Science & Education, 16, 849-881. https://doi.org/10.1007/s11191-006-9064-4.
Gilbert, J. K., & Treagust, D. (Eds.). (2009). Multiple Representations in Chemical Education. Dordrecht, Netherlands: Springer Science + Business Media B.V. https://doi.org/10.1007/978-1-4020-8872-8.
Gojak, S., Galijašević, S., Hadžibegović, Z., Zejnilagić-Hajrić, M., Nuić, I., & Korać, F. (2012). Integrated knowledge of physics and chemistry: Case of Physical Chemistry course. Bulletin of the Chemists and Technologists of Bosnia and Herzegovina, 38, 43-51. Retrieved from http://www.pmf.unsa.ba/hemija/glasnik/files/Issue%2038/38%20-%209-Gojak.pdf.
Harrison, A. G., & Treagust, D. F. (2002). The particulate nature of matter: Challenges in understanding the submicroscopic world. In Chemical education: Towards research-based practice (pp. 189-212). Springer, Dordrecht. https://doi.org/10.1007/0-306-47977-X_9.
Hinton, M. E., & Nakhleh, M. B. (1999). Students’ microscopic, macroscopic, and symbolic representations of chemical reactions. The Chemical Educator, 4, 158-167. https://doi.org/10.1007/s00897990325a.
Ibrahimović, N. (2015). Osnovnoškolsko i srednjoškolsko obrazovanje u BiH: Trenutno stanje i preporuke za reforme [Primary and secondary education in BiH: Current status and recommendations for reforms]. Sarajevo: Inicijativa za monitoring evropskih integracija BiH. Retrieved from http://eu-monitoring.ba/namir-ibrahimovic-osnovnoskolsko-i-srednjoskolsko-obrazovanje-u-bih-trenutno-stanje-i-preporuke-za-reforme/.
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. https://doi.org/10.1080/09500693.2011.569959.
Johnstone, A. H. (1982). Macro- and micro-chemistry. School Science Review, 64(227), 377-379. Cited in Tsaparlis, G., & Sevian, H. (2013). Introduction: Concepts of matter – Complex to teach and difficult to learn. In G. Tsaparlis and H. Sevian (Eds.), Concepts of Matter in Science Education (pp. 1-8). Dordrecht, Netherlands: Springer Science + Business Media B.V. https://doi.org/10.1007/978-94-007-5914-5_1.
Johnstone, A. H. (1993). The development of chemistry teaching: A changing response to changing demand. Journal of Chemical Education, 70(9), 701-705. https://doi.org/10.1021/ed070p701.
Juriševič, M. (2010). UM – Vprašalnik za učence osnovne šole [UM - Questionnaire for primary school students]. In Interim Report V5-0424: Analiza dejavnikov, ki vplivajo na trajnejše znanje z razumevanjem naravoslovno-tehniških vsebin (pp. 2–5). Ljubljana: Pedagoška fakulteta Univerze v Ljubljani.
Juriševič, M., & Devetak, I. (2010). Kako se učim kemijo [How I learn chemistry]. In Interim Report V5-0424: Analiza dejavnikov, ki vplivajo na trajnejše znanje z razumevanjem naravoslovno-tehniških vsebin (pp. 6-7). Ljubljana: Pedagoška fakulteta Univerze v Ljubljani.
Juriševič, M., Vogrinc, J., & Glažar, S. A. (2010). Odnos do kemije [Attitude toward chemistry]. In Interim Report V5-0424: Analiza dejavnikov, ki vplivajo na trajnejše znanje z razumevanjem naravoslovno-tehniških vsebin (p. 8). Ljubljana: Pedagoška fakulteta Univerze v Ljubljani.
Kaya, F., Juntune, J., & Stough, L. (2015). Intelligence and its relationship to achievement. Elementary Education Online 14(3), 1060-1078. https://doi.org/10.17051/io.2015.25436.
Kind, V. (2004). Beyond Appearances: Students’ misconceptions about basic chemical ideas (2nd Ed.) Royal Society of Chemistry. Retrieved from http://community.nsee.us/pd/pd2007_assessment/misconceptions/Beyond-appearances.pdf.
Koohang, A., Riley, L., Smith, T., & Schreurs, J. (2009). E-learning and constructivism: From theory to application. Interdisciplinary Journal of E-Learning and Learning Objects, 5(1), 91-109. https://doi.org/10.28945/3321.
Kozma, R. B. (2000). The use of multiple representations and the social construction of understanding in chemistry. In M. J. Jacopson & R. B. Kozma (Eds.), Innovations in science and mathematics education (pp. 11–45). Mahwah, NJ: Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410602671.
Kozma, R. B., & 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/10.1002/(SICI)1098-2736(199711)34:9<949::AID-TEA7>3.0.CO;2-U.
Laerd Statistics. (2013). One-way ANOVA in SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/one-way-anova-using-spss-statistics.php.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 1–12. https://doi.org/10.3389/fpsyg.2013.00863.
Lewalter, D. (2003). Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction 13, 177-189. https://doi.org/10.1016/S0959-4752(02)00019-1.
Lowe, R. K. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13(2), 157-176. https://doi.org/10.1039/C6RP00013D.
Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Retrieved from http://repository.alt.ac.uk/629/.
Meijer, M. R., Bulte, A. M. W., & Pilot, A. (2009). Structure-property relations between macro and micro representations: Relevant meso levels in authentic tasks. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (Models and modeling in chemical education, Vol. 4, pp. 195–213). Dordrecht, Netherlands: Springer Science + Business Media B.V. https://doi.org/10.1007/978-1-4020-8872-8_10.
Milenković, D. D., Hrin, T. N., Segedinac, M. D., & Horvat, S. (2016). Identification of misconceptions through multiple choice tasks at municipal chemistry competition test. Journal of Subject Didactics, 1(1), 3-12. https://doi.org/10.5281/zenodo.55468.
Milenković, D. D., Segedinac, M. D., & Hrin, T. N. (2014). Increasing high school students’ chemistry performance and reducing cognitive load through an instructional strategy based on the interaction of multiple levels of knowledge representation. Journal of Chemical Education, 91(9), 1409-1416. https://doi.org/10.1021/ed400805p.
Nakhleh, M. B., Samarapungavan, A., & Saglam, Y. (2005). Middle school students’ beliefs about matter. Journal of Research in Science Teaching, 42(5), 581-612. https://doi.org/10.1002/tea.20065.
National Research Council (1996). National science education standards. National Academies Press. https://doi.org/10.17226/4962.
Nehring, A., Nowak, K. H., zu Belzen, A. U., & Tiemann, R. (2015). Predicting students’ skills in the context of scientific inquiry with cognitive, motivational, and sociodemographic variables. International Journal of Science Education, 37(9), 1343-1363. https://doi.org/10.1080/09500693.2015.1035358.
Novak, J. (2005). Results and implications of a 12-year longitudinal study of science concept learning. Research in Science Education, 35(1), 23-40. https://doi.org/10.1007/s11165-004-3431-4.
Novick, S., & Nussbaum, J. (1978). Junior high school pupils’ understanding of the particulate nature of matter: An interview study. Science Education, 62(3), 273-281. https://doi.org/10.1002/sce.3730620303.
Nyachwaya, J. M., Mohamed, A. R., Roehrig, G. H., Wood, N. B., Kern, A. L., & Schneider, J. L. (2011). The development of an open-ended drawing tool: An alternative diagnostic tool for assessing students’ understanding of the particulate nature of matter. Chemistry Education Research and Practice, 12, 121-132. https://doi.org/10.1039/C1RP90017J.
Odhiambo, S. O. (2010). The impact of e-learning on academic performance: A case study of group learning sets (Master Thesis). Nairobi, Kenya: University of Nairobi.
Olakanmi, E. E. (2015). The effects of a web-based computer simulation on students’ conceptual understanding of rate of reaction and attitude towards chemistry. Journal of Baltic Science Education, 14(5), 627-640.
Olson, J., Codde, J., deMaagd, K., Tarkleson, E., Sinclair, J., Yook, S., & Egidio, R. (2011). An Analysis of e-Learning Impacts & Best Practices in Developing Countries. Michigan State University. Retrieved from http://cas.msu.edu/wp-content/uploads/2013/09/E-Learning-White-Paper_oct-2011.pdf.
Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education 25(9), 1049-1079. https://doi.org/10.1080/0950069032000032199.
Osborne, R. J., & Cosgrove, M. M. (1983). Children’s conceptions of the changes of state of water. Journal of Research in Science Teaching, 20(9), 825-838. https://doi.org/10.1002/tea.3660200905.
Pallant, J. (2010). SPSS Survival manual – A step by step guide to data analysis using SPSS (4th ed.) Berkshire, U. K.: McGraw-Hill.
Papageorgiou, G., Stamovlasis, D., & Johnson, P. M. (2010). Primary teachers’ particle ideas and explanations of physical phenomena: Effect of an in‐service training course. International Journal of Science Education, 32(5), 629-652. https://doi.org/10.1080/09500690902738016.
Pitjeng, P. (2014). Novice unqualified graduate science teachers’ topic specific pedagogical content knowledge, content knowledge and their beliefs about teaching. In H. Venkat, M. Rollnick, J. Loughran, & M. Askew (Eds.), Exploring mathematics and science teachers’ knowledge: Windows into teacher’s thinking (pp. 65-83). London, U. K.: Routledge. https://doi.org/10.4324/9781315883090.
Potter, N. M., & Overton, T. L. (2006). Chemistry in sport: Context-based e-learning in chemistry. Chemistry Education Research and Practice, 7(3), 195-202. https://doi.org/10.1039/B6RP90008A.
Pribush, R. A. (2015). Impact of technology on chemistry instruction. In M.V. Orna (Ed.), Sputnik to Smartphones: A Half-Century of Chemistry Education – ACS Symposium Series 1208 (pp. 155–194). Washington, D.C.: American Chemical Society. https://doi.org/10.1021/bk-2015-1208.ch010.
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/10.1080/09500690701447405.
Rizman Herga, N., Čagran, B., & Dinevski, D. (2016). Virtual laboratory in the role of dynamic visualisation for better understanding of chemistry in primary school. Eurasia Journal of Mathematics, Science & Technology Education, 12(3), 593-608. https://doi.org/10.12973/eurasia.2016.1224a.
Rizman Herga, N., Glažar, S. A., & Dinevski, D. (2015). Dynamic visualization in the virtual laboratory enhances the fundamental understanding of chemical concepts. Journal of Baltic Science Education 14(3), 351-365. Retrieved from http://www.scientiasocialis.lt/jbse/files/pdf/vol14/351-365.Herga_JBSE_Vol.14_No.3.pdf.
Russell, J. W., Kozma, R. B., Jones, T., Wykoff, J., Marx, N., & 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. https://doi.org/10.1021/ed074p330.
Sanger, M. J. (2000). Using particulate drawings to determine and improve students’ conceptions of pure substances and mixtures. Journal of Chemical Education, 77(6), 762-766. https://doi.org/10.1021/ed077p762.
Singer, J. E., Tal, R., & Wu, H. K. (2003). Students’ understanding of the particulate nature of matter. School Science and Mathematics, 103(1), 28-44. https://doi.org/10.1111/j.1949-8594.2003.tb18111.x.
Skelić, Dž., & Alić, A. (2009). Postignuća učenika u kontekstu porodičnih prilika [Student achievements in the context of family situation]. In N. Suzić, Ž. Saničanin, A. Alić, Dž. Skelić, D. Rukavina, E. Alibegović Goro, Ž. Džumhur, S. Šahinović Batista, I. Milinković Rosić, V. Mešić, & A. Ibraković (Eds.), Sekundarna analiza TIMSS 2007 u Bosni i Hercegovini (pp. 235–272). Sarajevo, Bosna i Hercegovina: Agencija za predškolsko, osnovno i srednje obrazovanje.
Slapničar, M., Devetak, I., Glažar, S. A., & Pavlin, J. (2017). Identification of the understanding of the states of matter of water and air among Slovenian students aged 12, 14 and 16 through solving authentic tasks. Journal of Baltic Science Education, 16(3), 308-323. Retrieved from http://oaji.net/articles/2017/987-1497963826.pdf.
Slapničar, M., Tompa, V., Glažar, S. A., & Devetak, I. (2018). Fourteen-year-old students’ misconceptions regarding the sub-micro and symbolic levels of specific chemical concepts. Journal of Baltic Science Education, 17(4), 620-632.
Stavy, R. (1988). Children’s conception of gas. International Journal of Science Education, 10(5), 553-560. https://doi.org/10.1080/0950069880100508.
Stavy, R., & Stachel, D. (1985). Children’s ideas about ‘solid’ and ‘liquid’. European Journal of Science Education, 7(4), 407-421. https://doi.org/10.1080/0140528850070409.
Stern, L., Barnea, N., & Shauli, S. (2008). The effect of a computerized simulation on middle school students’ understanding of the kinetic molecular theory. Journal of Science Education and Technology, 17, 305-315. https://doi.org/10.1007/s10956-008-9100-z.
Stieff, M., Ryu, M., & Yip, J. C. (2013). Speaking across levels – Generating and addressing levels confusion in discourse. Chemistry Education Research and Practice, 14(4), 376-389. https://doi.org/10.1039/C3RP20158A.
Taber, K. S. (2001). Building the structural concepts of chemistry: Some considerations from educational research. Chemistry Education Research and Practice, 2(2), 123-158. https://doi.org/10.1039/b1rp90014e.
Taber, K. S. (2013). Revisiting the chemistry triplet: drawing upon the nature of chemical knowledge and the psychology of learning to inform chemistry education. Chemistry Education Research and Practice, 14(2), 156-168. https://doi.org/10.1039/c3rp00012e.
Taber, K. S. (2017). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education (online first). https://doi.org/10.1007/s11165-016-9602-2.
Talanquer, V. (2011). Macro, submicro, and symbolic: The many faces of the chemistry “triplet”. International Journal of Science Education, 33(2), 179-195. https://doi.org/10.1080/09500690903386435.
Tang, H., & Abraham, M. R. (2016). Effect of computer simulations at the particulate and macroscopic levels on students’ understanding of the particulate nature of matter. Journal of Chemical Education, 93(1), 31-38. https://doi.org/10.1021/acs.jchemed.5b00599.
Tasker, R. (2016). ConfChem Conference on interactive visualizations for chemistry teaching and learning: Research into practice – Visualizing the molecular world for a deep understanding of chemistry. Journal of Chemical Education, 93(6), 1152-1153. https://doi.org/10.1021/acs.jchemed.5b00824.
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.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International journal of medical education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd.
Treagust, D. F. (2018). The importance of multiple representations for teaching and learning science. In: M. Shelley, & S. A. Kiray (Eds.), Education Research Highlights in Mathematics, Science and Technology 2018. Ames, Iowa: ISRES Publishing.
Treagust, D. F., Chandrasegaran, A. L., Crowley, J., Yung, B. H. W., Cheong, I. P. A., & Othman, J. (2010). Evaluating students’ understanding of kinetic particle theory concepts relating to the states of matter, changes of state and diffusion: A cross-national study. International Journal of Science and Mathematics Education, 8, 141-164. https://doi.org/10.1007/s10763-009-9166-y.
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/10.1080/0950069032000070306.
Tsaparlis, G. (1997). Atomic and molecular structure in chemical education: A critical analysis from various perspectives of science education. Journal of Chemical Education, 74(8), 922. https://doi.org/10.1021/ed074p922.
Tytler, R. (2000). A comparison of year 1 and year 6 students’ conceptions of evaporation and condensation: Dimensions of conceptual progression. International Journal of Science Education, 22(5), 447-467. https://doi.org/10.1080/095006900289723.
Ural, E., & Ercan, O. (2015). The effects of web-based educational software enriched by concept maps on learning of structure and properties of matter. Journal of Baltic Science Education, 14(1), 7-19. Retrieved from http://www.scientiasocialis.lt/jbse/files/pdf/vol14/7-19.Ural_JBSE_Vol.14_No.1.pdf.
West, K. (2009). States of matter: Gases, liquids, and solids. New York, NY: Infobase Publishing.
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.
Yaseen, Z., & Aubusson, P. (2018). Exploring student-generated animations, combined with a representational pedagogy, as a tool for learning in chemistry. Research in Science Education, 1-20. https://doi.org/10.1007/s11165-018-9700-4.
Yezierski, E. J., & Birk, J. P. (2006). Misconceptions about the particulate nature of matter: Using animations to close the gender gap. Journal of Chemical Education, 83(6), 954-960. https://doi.org/10.1021/ed083p954.

How to cite this article

APA

Nuić, I., & Glažar, S. A. (2020). The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter. Eurasia Journal of Mathematics, Science and Technology Education, 16(2), em1823. https://doi.org/10.29333/ejmste/114483

Vancouver

Nuić I, Glažar SA. The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter. EURASIA J Math Sci Tech Ed. 2020;16(2):em1823. https://doi.org/10.29333/ejmste/114483

AMA

Nuić I, Glažar SA. The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter. EURASIA J Math Sci Tech Ed. 2020;16(2), em1823. https://doi.org/10.29333/ejmste/114483

Chicago

Nuić, Ines, and Saša Aleksej Glažar. "The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter". Eurasia Journal of Mathematics, Science and Technology Education 2020 16 no. 2 (2020): em1823. https://doi.org/10.29333/ejmste/114483

Harvard

Nuić, I., and Glažar, S. A. (2020). The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter. Eurasia Journal of Mathematics, Science and Technology Education, 16(2), em1823. https://doi.org/10.29333/ejmste/114483

MLA

Nuić, Ines et al. "The Effect of e-Learning Strategy at Primary School Level on Understanding Structure and States of Matter". Eurasia Journal of Mathematics, Science and Technology Education, vol. 16, no. 2, 2020, em1823. https://doi.org/10.29333/ejmste/114483