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Cited by 33Online and Blended LearningOnline Learning Methods and InnovationsTechnology-Enhanced Education Studies

The impact of online learning on student motivation in science and biology classes

Open access
Bulić, Mila, Blažević, Ines·2020·Revija za elementarno izobraževanje
DOI 10.18690/rei.13.1.73-87.2020
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Abstract

The research aimed to check for difference in learning motivation between students learning online and students participating in modern classroom instruction. The study participants included students attending grades 5 to 8, divided into experimental (online classes) and control groups based on pre-testing. The results show that there is no statistically significant difference in motivation between the tested sub-samples. Student motivation for learning natural sciences declines with their years of schooling, but this is not statistically significant. It can be concluded that online teaching of Science and Biology as a learning tool can be as motivating as modern teaching using active classroom methods.

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  • The perception findings are consistent with the view that well-scaffolded simulation activities can support perceived competence and theory-practice integration.

    “These findings are consistent with the view that well-scaffolded simulation activities can support perceived competence and theory–practice integration, but they should not be interpreted as validated evidence of deep learning, cognitive engagement, or self-regulated learning [29].”

    From Comparing predictive, simulation-based and hybrid assessments in undergraduate process engineering (2026) · cites this paper as [29]

    The Conclusions and Practical Implications discuss online learning, student motivation, and e-learning as a tool for better understanding, but do not address scaffolded simulations or theory–practice integration. The Data Collection Procedure includes students’ assessments of their own performance, but does not test whether simulation activities support perceived competence. Searches for simulation activities and theory–practice integration found no supporting passage in our copy. (LLM, conf=0.74)

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Research works printed but not linked: 21

  1. Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of educational psychology, 84(3), 261.
  2. Bolliger, D. U., & Martindale, T. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-learning, 3(1), 61-68.
  3. Brumen, M., Krušič, H., & Zupančič, T. (2017). Uporaba informacijsko-komunikacijske tehnologije pri poučevanju in učenju angleščine v otroštvu. Revija za elementarno izobraževanje, 10(4), 427-443.
  4. REVIJA ZA ELEMENTARNO IZOBRAŽEVANJE. JOURNAL OF ELEMENTARY EDUCATION.
  5. Bulić, M., Jelaska, I., & Jelaska, P. M. (2017). The Effect of E-learning on the Acquisition of Learning Outcomes in Teaching Science and Biology. Croatian Journal of Education-Hrvatski časopis za odgoj i obrazovanje, 19(2), 447.
  6. Bulić, M., & Kostović Vranješ, V. (2019). The Impact of E-Learning on Student Selfresponsibility in Doing Their Homework.Školski vjesnik: časopis za pedagogijsku teoriju i praksu, 68(1), 127-140.
  7. Butler, R. (2014). Motivation in educational contexts: Does gender matter?. In Advances in child development and behavior (Vol. 47, pp. 1-41). JAI.
  8. Carlone, H. B. (2004). The Cultural production of Science in reform-based Physics: Girls' Access, Participation and Resistance. Journal of Research in Science Teaching, 41(4), 392-414.
  9. Deci, E. L., & Ryan, R. M. (1985). Cognitive evaluation theory. In Intrinsic motivation and self- determination in human behavior (pp. 43-85). Springer, Boston, MA.
  10. Güvercin, O., Tekkaya, C., Sungur, S. (2010). Across age study of elementary students' motivation towards science learning. Hacettepe University Journal of Education, 39, 233-243.
  11. Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2009). Science motivation questionnaire: Construct validation with nonscience majors. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 46(2), 127-146.
  12. Jenkins, E. W. (2005): Important but not for me: students' attitudes towards secondary school science in England. Research in Science & Technological Education, 23(1), 41-57.
  13. Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.
  14. Molenda, M. (2003). In search of the elusive ADDIE model. Performance improvement, 42(5), 34-37.
  15. Novoselić, D., Bulić, M. & Bastić, M. (2014). Eight Graders' Motivation for Knowledge Acquisition of Elementary School Biology. Modern Approaches to Teaching the Coming Generations (pp. 272 -281). Eduvision International Conference, Ljubljana, Slovenia.
  16. Osborne, J. & Collins, S. (2001). Pupils' views of the role and value of the science curriculum: a focusgroup study. International Journal of Science Education, 23(5), 441-467.
  17. Pajares, F. & Schunk, D. H. (2001). Self-neliefes and school succes: Self-efficacy, self-concept and school achivment. In r. Riding & s. Rayner (Eds.). London: Ablex Publishing.
  18. Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching context. Journal of Education Psychology, 95, 667-686.
  19. Sanfeliz, M., Stalzer, M. (2003). Science motivation in the multicultural classroom. The Science Teacher, 70 (39), 64-66.
  20. Violante, M. G., & Vezzetti, E. (2015). Virtual interactive e-learning application: An evaluation of the student satisfaction. Computer Applications in Engineering Education, 23(1), 72-91. https://doi.org/10.1002/cae.21580 · DOI
  21. Wei, H. C., Peng, H., & Chou, C. (2015). Can more interactivity improve learning achievement in an online course? Effects of college students' perception and actual use of a coursemanagement system on their learning achievement. Computers & Education, 83, 10-21. https://doi.org/10.1016/j.compedu.2014.12.013 · DOI

Cited by

Sorted by citations

Authors
Bulić, M., & Blažević, I.
Affiliations
University of Split (HR)
ORCID
Venue
Revija za elementarno izobraževanje
Publisher
University of Maribor
Published
2020
Volume
13
Issue
1
Pages
73-87
ISSN
  • 1855-4431 (ISSN-L)
  • 2350-4803
DOI
10.18690/rei.13.1.73-87.2020
OpenAlex
W3099004336
Cited by
33
References
0
Citations by year
2026: 1 · 2025: 4 · 2024: 3 · 2023: 15 · 2022: 6 · 2021: 3 · 2020: 1
FWCI
9.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Revija za elementarno izobraževanje (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Education › Online and Blended Learning
  • Social Sciences › Education › Online Learning Methods and Innovations
  • Social Sciences › Education › Technology-Enhanced Education Studies
Keywords
online learning, student motivation, science education, biology education, elementary school students, classroom teaching, active learning methodologies, grade level differences

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