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Cluster-Based Performance of Student Dropout Prediction as a Solution for Large Scale Models in a Moodle LMS

Published: 13 March 2023 Publication History

Abstract

Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS?” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.

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Cited By

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  • (2024)Unpacking student engagement in higher education learning analytics: a systematic reviewInternational Journal of Educational Technology in Higher Education10.1186/s41239-024-00493-y21:1Online publication date: 20-Dec-2024
  • (2023)Knowledge Graphs for Competency-Based Education2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386727(2942-2945)Online publication date: 15-Dec-2023
  • (2023)Using Web Analytics Methods to Design Open Web-Based University Courses: Case Study on Creative Work with Information CourseNew Media Pedagogy: Research Trends, Methodological Challenges and Successful Implementations10.1007/978-3-031-44581-1_16(221-236)Online publication date: 18-Oct-2023

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cover image ACM Other conferences
LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference
March 2023
692 pages
ISBN:9781450398657
DOI:10.1145/3576050
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2023

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Author Tags

  1. Moodle LMS
  2. dropout prediction
  3. engagement
  4. learning analytics

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  • Short-paper
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  • Refereed limited

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  • OBVIA

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LAK 2023

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Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

View all
  • (2024)Unpacking student engagement in higher education learning analytics: a systematic reviewInternational Journal of Educational Technology in Higher Education10.1186/s41239-024-00493-y21:1Online publication date: 20-Dec-2024
  • (2023)Knowledge Graphs for Competency-Based Education2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386727(2942-2945)Online publication date: 15-Dec-2023
  • (2023)Using Web Analytics Methods to Design Open Web-Based University Courses: Case Study on Creative Work with Information CourseNew Media Pedagogy: Research Trends, Methodological Challenges and Successful Implementations10.1007/978-3-031-44581-1_16(221-236)Online publication date: 18-Oct-2023

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