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A decade of learning analytics: Structural topic modeling based bibliometric analysis

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Abstract

Learning analytics (LA) has become an increasingly active field focusing on leveraging learning process data to understand and improve teaching and learning. With the explosive growth in the number of studies concerning LA, it is significant to investigate its research status and trends, particularly the thematic structure. Based on 3900 LA articles published during the past decade, this study explores answers to questions such as “what research topics were the LA community interested in?” and “how did such research topics evolve?” by adopting structural topic modeling and bibliometrics. Major publication sources, countries/regions, institutions, and scientific collaborations were examined and visualized. Based on the analyses, we present suggestions for future LA research and discussions about important topics in the field. It is worth highlighting LA combining various innovative technologies (e.g., visual dashboards, neural networks, multimodal technologies, and open learner models) to support classroom orchestration, personalized recommendation/feedback, self-regulated learning in flipped classrooms, interaction in game-based and social learning. This work is useful in providing an overview of LA research, revealing the trends in LA practices, and suggesting future research directions.

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  1. https://gephi.org/

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Appendix

Appendix

Table 4 The most up-to-date reviews on LA
Table 5 The comparison of the 16- and 17-topic models
Table 6 Top publication sources ranked by H-index
Table 7 Top countries/regions ranked by H-index
Table 8 Top institutions ranked by H-index
Table 9 Representative terms for each topics

References for Appendix

Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.

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Chen, X., Zou, D. & Xie, H. A decade of learning analytics: Structural topic modeling based bibliometric analysis. Educ Inf Technol 27, 10517–10561 (2022). https://doi.org/10.1007/s10639-022-11046-z

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