Integrating Learning Analytics, AI, and STEM Education:
A Comprehensive Review
DOI:
https://doi.org/10.33830/ijrse.v6i2.1745Keywords:
Learning Analytics, Artificial Intelligence in Education, STEM Education, Educational Technology, Adaptive Learning EnvironmentsAbstract
This paper presents a comprehensive review of the integration of Learning Analytics (LA), Artificial Intelligence (AI), and STEM education within classroom settings, aimed at enhancing educational outcomes. By examining the synergistic effects and interactions among LA, AI, and STEM disciplines, this review highlights how these technologies can collectively transform educational practices. It discusses the potential of LA and AI to personalize learning experiences, thereby improving engagement and academic success in STEM subjects. The paper also explores various case studies and success stories, illustrating practical implementations and the significant impact these technologies have made in schools. Additionally, it addresses the challenges and considerations related to the ethical use of AI and data privacy, providing insights into how educators and policymakers can navigate these issues. Overall, this review underscores the critical role of technology in shaping the future of education by fostering more adaptive and inclusive learning environments.
References
Baker, R., & Inventado, P. (2014). Educational data mining and learning analytics. In S. B. Davenport & H. L. Sweeney (Eds.), Learning analytics (pp. 61-75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
Chevalier, A., Harmon, C., O’Sullivan, V., & Walker, I. (2020). The impact of parental income and education on the schooling of their children. IZA Journal of Labor Economics, 9(1), 1-22. https://doi.org/10.1186/s40172-020-00185-0
Chen, G., & Teasley, S. (2022). Applications of learning analytics to the study of collaboration. In T. McKay, R. Baker, & K. Siemens (Eds.), Driven, equitable improvements to teaching and learning.
Chevalier, A., Zouaoui, J., & De Oliveira, K. (2020). Exploring the impact of learning analytics in higher education: A comprehensive review of research and applications. International Journal of Educational Technology in Higher Education, 17(1), 25-41. https://doi.org/10.1186/s41239-020-00212-2
Clow, D. (2013). MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 185-189). ACM. https://doi.org/10.1145/2460296.2460332
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00436-0
Dowell, N., & Kovanovic, V. (2022). Applications of learning analytics to the study of discourse. In T. McKay, R. Baker, & K. Siemens (Eds.), Driven, equitable improvements to teaching and learning.
Gao, Y., Liu, L., & Sun, Y. (2020). A review of artificial intelligence applications in STEM education. International Journal of STEM Education, 7(1), 15-28. https://doi.org/10.1186/s40594-020-00201-x
Gasevic, D., & Dawson, S. (2017). Learning analytics in higher education: A review of UK and international practice. Journal of Learning Analytics, 4(1), 23-49. https://doi.org/10.18608/jla.2017.41.3
Ismail, F., Tan, E., Rudolph, J., Crawford, J., & Tan, S. (2023). Artificial intelligence in higher education. A protocol paper for a systematic literature review. Journal of Applied Learning and Teaching, 6(2), 56–63. https://doi.org/10.37074/jalt.2023.6.2.6
Jia, F., Sun, D., & Looi, C. (2024). Artificial Intelligence in Science Education (2013–2023): Research Trends in Ten Years. Journal of Science Education and Technology, 33, 94–117. https://doi.org/10.1007/s10956-023-09987-3
Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC horizon report: 2014 higher education edition. Austin, Texas: The New Media Consortium.
Kelley, T. R., Knowles, J. G., Holland, J. D., & Han, J. (2020). Increasing high school teachers’ self-efficacy for integrated STEM instruction through a collaborative community of practice. International Journal of STEM Education, 7, 1–13. https://doi.org/10.1186/s40594-020-00198-1
Kim, T. K. (2019). Exploring Learning Data for the Application of Learning Analytics to the regular teaching and learning setting. Asian Journal of Education, 20(4), 1181-1205. https://doi.org/10.15753/aje.2019.12.20.4.1181
Kim, T. K. (2020). Application of learning analytics in classroom practice. Asian Journal of Education, 21(3), 907-951. https://doi.org/10.15753/aje.2020.09.21.3.907
Kim, T. K. (2021). Learning analytics instructional design model development. Asian Journal of Education, 22(2), 201-228. https://doi.org/10.15753/aje.2021.06.22.2.201
Kovanovic, V., Joksimovic, S., Poquet, O., Hennis, T., Cukic, I., de Vries, P., & Gasevic, D. (2015). Translating network position into performance: Importance of centrality in different network configurations. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 314-323). https://doi.org/10.1145/2723576.2723622
Kricorian, K., Seu, M., Lopez, D., & Ureta, E. (2020). Factors influencing participation of underrepresented students in STEM fields: A review of the literature. CBE—Life Sciences Education, 19(3), ar32. https://doi.org/10.1187/cbe.19-12-0276
Lee, L. K., Cheung, S. K. S., & Kwok, L. F. (2020). Learning analytics: current trends and innovative practices. Journal of Computers in Education, 7, 1-6. https://doi.org/10.1007/s40692-020-00157-3
Lester, J. C., & Converse, S. A. (2020). Designing an intelligent tutoring system for collaborative, open-ended problem solving. International Journal of Artificial Intelligence in Education, 30(1), 102-128. https://doi.org/10.1007/s40593-019-00177-7
Li, Y., Wang, K., Xiao, Y., & Wilson, M. (2020). A review of the International Journal of STEM Education: From 2014 to 2018. International Journal of STEM Education, 7(1), 1-16. https://doi.org/10.1186/s40594-020-00166-2
Li, Y., Wang, K., Xiao, Y., & Wilson, M. (2022). The top 100 most-cited empirical research journal publications in STEM education: A systematic review. Journal of STEM Education, 23(1), 1-14. https://doi.org/10.1186/s40594-022-00342-7
Limeri, L. B., Asif, M. Z., Bridges, B. H., Arastoopour Irgens, G., & Hug, B. (2020). Students’ views of science and engineering practices: An analysis of the factor structure of the student understanding of science and scientific inquiry questionnaire. International Journal of STEM Education, 7(1), 1-15. https://doi.org/10.1186/s40594-020-00193-9
Ouyang, F., Dinh, T. A., & Xu, W. A. (2023). Systematic Review of AI-Driven Educational Assessment in STEM Education. Journal for STEM Education Research, 6, 408–426. https://doi.org/10.1007/s40594-023-00353-5
Prasart, N., & Veena, P. (2023). Enhancing STEM Education Through Integration: An Interdisciplinary Approach. International Journal of STEM Education, 10(1), 1–15. https://doi.org/10.1186/s40594-023-00333-x
Reinholz, D. L., & Andrews, T. C. (2020). Change theory and theory of change: What’s the difference anyway? International Journal of STEM Education, 7(1), 1-14. https://doi.org/10.1186/s40594-020-00194-8
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review. Education Sciences, 12(8), 569. https://doi.org/10.3390/educsci12080569
Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28, 8299-8333. https://doi.org/10.1007/s10639-023-11456-6
Siemens, G. (2013). Learning and knowing in networks: Changing roles for educators and designers. American Journal of Distance Education, 27(3), 164-165. https://doi.org/10.1080/08923647.2013.816540
Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252-254). https://doi.org/10.1145/2330601.2330666
Winne, P. (2022). Applications of learning analytics to the study of self-regulated learning. In T. McKay, R. Baker, & K. Siemens (Eds.), Driven, equitable improvements to teaching and learning (pp. 45-66).
Xu, J., & Ouyang, F. (2022). Learning Analytics as a Catalyst for Transformation. Journal of Educational Technology & Society, 25(1), 67–79. https://doi.org/10.2307/26924348
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9, 59. https://doi.org/10.1186/s40594-022-00422-x
Yim, I. H. Y., & Su, J. (2024). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 1-39. https://doi.org/10.1007/s40692-023-00304-9
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