Perceived Benefits of Learning Analytics and Artificial Intelligence-Based Oniline Learning Platforms: Case of Lithuanian General Education Schools
Keywords:
Learning analytics, artificial intelligence online learning platforms, general education schools
Abstract
Online learning platforms with integrated tools of learning analytics (LA) and artificial intelligence (AI) are growing in popularity in general education in Lithuania. Such platforms have a number of advantages in terms of the teaching-learning process, however, there is a lack of research about such advantages after direct use of the platforms in general education schools. Thus, the purpose of the current study is to find out the perceived benefits of online learning platforms with LA and AI tools. The research was conducted in 11 schools in Lithuania. The students at these schools tested the LearnLab and Eduten Playground online learning platforms for almost three months. Descriptive statistics methods and chi-square (χ2) criteria were applied. Results showed that students claim that their learning achievements have improved thanks to the platforms. Moreover, research results showed, that when working with platforms, it is appropriate to pay attention and, in parallel, to teach students computer literacy from the elementary grades, to develop a relationship with the computer as a work tool. It is also appropriate to start working with LA and AI platforms from the primary grades, which would positively stimulate the growth of digital competence, as well as the interest of students in the educational subject(s) and the positive growth of learning achievements.
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References
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31. Petrušauskaitė, M. (2021). Devintos klasės mokinių įsitraukimo į ugdymo procesą skatinimas pasitelkiant skaitmeninį mokymosi žaidimą „KAHOOT!“ ekonomikos pamokose/ Encouraging the engagement of ninth grade students in the educational process using the digital learning game "KAHOOT!" in economics classes. Master thesis
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33. Selevičienė, E. (2020). Effectiveness and acceptance of Web 2.0 technologies in the studies of English for specific purposes in higher education (Doctoral dissertation, Mykolo Romerio universitetas).
34. Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5-11.
35. Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57 (10), 1380–1400.
36. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Ezioni, O., Hager, G., …, Teller, A. (2016). Artificial intelligence and life in 2030: One hundred year study on artificial intelligence. Report of the 2015 study panel. Access online: https://ai100.stanford.edu/2016-report.
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40. Youssef, Shiban & Schelhorn, Iris & Jobst, Verena & Hörnlein, Alexander & Puppe, Frank & Pauli, Paul & Mühlberger, Andreas. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior. 49. 10.1016/j.chb.2015.01.077.
41. Wang, Y., & Decker, J. R. (2014). Examining digital inequities in Ohio's K-12 virtual schools: Implications for educational leaders and policymakers. International Journal of Educational Reform, 23(4), 294-314.
2. Admiraal, W., Vermeulen, J., Bulterman-Bos, J. (2017). Learning Analytics in Secondary Education: Assessment for Learning in 7th Grade Language Teaching. ECER 2017. Access online: https://eera-ecer.de/ecer-programmes/conference/22/ contribution/39935/.
3. Carpenter, J. P., & Krutka, D. G. (2014). Chat it up. Learning & Leading with Technology, 41(5), 10-15.
4. Christopoulos, A., Kajasilta, H., Salakoski, T., & Laakso, M. J. (2020). Limits and virtues of educational technology in elementary school mathematics. Journal of Educational Technology Systems, 49(1), 59-81.
5. Coccoli, M., Maresca, P., Stanganelli, L. (2016). Cognitive computing in education. Journal of e-Learning and Knowledge Society, 12(2), 55–69.
6. Davis, S. K., Edwards, R. L., Miller, M., Aragon, J. (2018). Considering context and comparing methodological approaches in implementing learning analytics at the University of Victoria. Proceeding
7. Dehler, J., Bodemer, D., Buder, J., Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068–1078.
8. Du Boulay, B. (2016). Artificial intelligence as an effective classroom assistant. IEEE Intelligent Systems, 31(6), 76–81.
9. Ebner, M., Schön, M. (2013). Why learning analytics for primary education matters! Bulletin of the IEEE Technical Committee on Learning Technology, 15(2), 14–17.
10. Ferguson, R., Clow, D., Griffiths, D., Brasher, A. (2019). Moving forward with learning analytics: Expert views. Journal of Learning Analytics, 6(3), 43–59. Access online: http://dx.doi.org/10.18608/jla.2019.63.8.
11. Gulson, K. N., Murphie, A., Taylor, S., Sellar, S. (2018). Education, work and Australian society in an AI world. A review of research literature and policy recommendations (Research Report). Sydney: Gonski Institute for Education, UNSW. Harley, Lajoie, Frasson, Hall.
12. Har Carmel, Y. (2016). Regulating “Big Data education” in Europe: lessons learned from the US. Internet Policy Review, 5(1). Doi: 10.14763/2016.1.402.
13. Hollman, A. K., Hollman, T. J., Shimerdla, F., Bice, M. R., Adkins, M. (2019). Information technology pathways in education: Interventions with middle school students. Computers & Education, 135, 49-60
14. Hylen, J. (2015). The State of Art of Learning Analytics in Danish Schools. Access online: http://www.laceproject.eu/blog/the-state-of-art-of-learning-analytics-in-danish-schools/.
15. Ifenthaler, D., Gibson, D., Prasse, D., Shimada, A., Yamada, M. (2020) Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners. Educational Technology Research and Development, 69, 2131–2150. Access online: https://doi.org/10.1007/s11423-020-09909-8.
16. Kaila, E., Rajala, T., Laakso, M. J., Lindén, R., Kurvinen, E., Karavirta, V., Salakoski, T. (2015). Comparing student performance between traditional and technologically enhanced programming course. ACE, 160, 147- 154.
17. Kliziene, I., Taujanskiene, G., Augustiniene, A., Simonaitiene, B., & Cibulskas, G. (2021). The impact of the virtual learning platform EDUKA on the academic performance of primary school children. Sustainability, 13(4), 2268.
18. Kondratavičienė, R. (2018). Individualization and differentiation of the content of primary education by using virtual learning environment “EDUKA class”. Pedagogika, 130(2), 131-147.
19. Kurvinen, E., Kaila, E., Laakso, M.-J., Salakoskis, T. (2020). Long Term Effects on Technology Enhanced Learning: The Use of Weekly Digital Lessons in Mathematics. Informatics in Education, 19, 51–75. Vilnius: Vilniaus universitetas.
20. Lemay, D. J., Bazelais, P., & Doleck, T. (2021). Transition to online learning during the COVID-19 pandemic. Computers in human behavior reports, 4, 100130.
21. Long, P., Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 31–40
22. Luckin, R., Holmes, W., Griffiths, M., Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education.
23. Mangaroska, K., Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12 (4), 516–534
24. Mangaroska, K., Vesin, B., Giannakos, M. (2019). Cross-platform analytics: A step towards personalization and adaptation in education. Proceedings of the 9th international conference, 2019. Access online: https://ntnuopen.ntnu.no/ntnuxmlui/bitstream/handle/11250/2648295/2019-LAK-Cross-Platform-Analytics. pdf?sequence=1
25. Martin, F., Wang, C., Petty, T., Wang, W., & Wilkins, P. (2018). Middle school students’ social media use. Journal of Educational Technology & Society, 21(1), 213-224.
26. Maseleno, A., Sabani, N., Huda, M., Ahmad, R., Jasmi, K. A., & Basiron, B. (2018). Demystifying learning analytics in personalised learning. International Journal of Engineering and Technology (UAE).
27. Mayer-Schönberger, V., Cukier, K. (2014). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston: Houghton Mifflin Harcourt.
28. Meyers, E., Cahill, M., Subramaniam, M., Stripling, B. (2016). The promise and peril of learning analytics in P-12 education: An uneasy partnership? iConference 2016. Access online https://www.ideals.illinois.edu/bitstream/handle/2142/89459/ Meyer518.pdf?sequence=1&isAllowed=y
29. Papamitsiou, Z., Economides, A. A. (2015). Temporal learning analytics visualizations for increasing awareness during
30. Peng, C. (2021). The academic motivation and engagement of students in English as a foreign language classes: Does teacher praise matter?. Frontiers in Psychology, 12.
31. Petrušauskaitė, M. (2021). Devintos klasės mokinių įsitraukimo į ugdymo procesą skatinimas pasitelkiant skaitmeninį mokymosi žaidimą „KAHOOT!“ ekonomikos pamokose/ Encouraging the engagement of ninth grade students in the educational process using the digital learning game "KAHOOT!" in economics classes. Master thesis
32. Sclater, N., Mullan J. (2017). Learning analytics and student success – assessing the evidence. JISC, Bristol.
33. Selevičienė, E. (2020). Effectiveness and acceptance of Web 2.0 technologies in the studies of English for specific purposes in higher education (Doctoral dissertation, Mykolo Romerio universitetas).
34. Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5-11.
35. Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57 (10), 1380–1400.
36. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Ezioni, O., Hager, G., …, Teller, A. (2016). Artificial intelligence and life in 2030: One hundred year study on artificial intelligence. Report of the 2015 study panel. Access online: https://ai100.stanford.edu/2016-report.
37. Taujanskienė, G., Skripkienė, A., & Klizienė, I. (2020). Virtualios mokymo (si) aplinkos įtaka pradinių klasių mokinių matematikos mokymosi pasiekimams. Jaunųjų mokslininkų darbai, 50(1), 54-60.
38. Vincent-Lancrin, S. (2021). OECD Digital Education Outlook: Pushing the frontiers with AI, blockchain, and robots © OECD. Access online: https://www.oecd.org/education/oecd-digital-education-outlook-7fbfff45-en.htm
39. Williamson, B. (2016). Digital education governance: An introduction. European Educational Research Journal, 15(1), 3-13.
40. Youssef, Shiban & Schelhorn, Iris & Jobst, Verena & Hörnlein, Alexander & Puppe, Frank & Pauli, Paul & Mühlberger, Andreas. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior. 49. 10.1016/j.chb.2015.01.077.
41. Wang, Y., & Decker, J. R. (2014). Examining digital inequities in Ohio's K-12 virtual schools: Implications for educational leaders and policymakers. International Journal of Educational Reform, 23(4), 294-314.
Published
2024-02-20
How to Cite
Batuchina, A., Melnikova, J., Zascerinska, J., & Ahrens, A. (2024). Perceived Benefits of Learning Analytics and Artificial Intelligence-Based Oniline Learning Platforms: Case of Lithuanian General Education Schools. European Scientific Journal, ESJ, 20(37), 340. https://doi.org/10.19044/esj.2024.v20n37p340
Section
Special Editions
Copyright (c) 2024 Aleksandra Batuchina, Julija Melnikova, Jelena Zascerinska, Andreas Ahrens
This work is licensed under a Creative Commons Attribution 4.0 International License.
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