The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief

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Lalhruaitluanga, C. Lalrinawma

Abstract

The words educational data mining and learning analytics are frequently used interchangeably, despite their being an increase in their investigation and implementation. This may be as a result of the fact that both areas have similar conceptual components. One way to ensure precision, homogeneity, and consistency It aims to pinpoint themes that are similar to and different from one other in the two domains as they develop. This a topic modelling study of papers on educational data mining and learning analytics was carried out in the elucidate the two areas' respective themes. In particular, we used structural topic modelling to find the two domains' subjects from the abstracts. For instructional purposes, we use structural topic modelling on N 1 4192 articles. For both educational data and survey data, we infer five-topic models analytics for mining and learning. While there may be disciplinary variations in research, our findings show that beyond their various lineages, there is no evidence to indicate a clear separation between the two disciplines. the area of educational research on the uses of advanced statistical methods is trending toward convergence for improving teaching and learning, discover how to mine massive data streams for insights that may be put to use. Over the past five years, both areas have converged on a growing emphasis on student behaviour. This study topic has advanced greatly, and a variety of related words, including Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science, are now used in the paper. The main publications, significant turning points, cycle of knowledge discovery, primary educational settings, specialised tools, freely accessible datasets, widely used methodologies, primary goals, and anticipated trends in this field of study are reviewed to provide the state of the art at this time.

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How to Cite
Lalhruaitluanga, et al. (2024). The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief. International Journal on Recent and Innovation Trends in Computing and Communication, 12(1), 164–178. https://doi.org/10.17762/ijritcc.v12i1.10077
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