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An Interpretable Online Learner's Performance Prediction Model Based on Learning Analytics

Published: 21 January 2020 Publication History

Abstract

Most of student performance prediction model only focused on the accuracy of prediction results, but achieving an interpretable prediction model may be as important as obtaining high accuracy in learning prediction research. This paper proposed a student performance prediction model based on online learning behavior analytics with 19 behavior indicators. This model consists of four steps: data collection and processing, correlation analysis, data analytics, student performance prediction algorithm, prediction and intervention. Moreover, a case have been taken to predict student performance according to the model with rule-based genetic programming algorithm. The experiment results show that the rule-based genetic programming algorithm has a stronger interpretation in ensuring competitive prediction accuracy. The model achieves a good prediction effect.

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  • (2024)Online Learning Behavior Analysis and Prediction Based on Spiking Neural NetworksJournal of Social Computing10.23919/JSC.2024.00155:2(180-193)Online publication date: Jun-2024
  • (2024)Robust Kernel Extreme Learning Machines For Postgraduate Learning Performance PredictionHeliyon10.1016/j.heliyon.2024.e40919(e40919)Online publication date: Dec-2024
  • (2023)Effective Feature Prediction Models for Student PerformanceEngineering, Technology & Applied Science Research10.48084/etasr.634513:5(11937-11944)Online publication date: 13-Oct-2023
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  1. An Interpretable Online Learner's Performance Prediction Model Based on Learning Analytics

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      cover image ACM Other conferences
      ICETC '19: Proceedings of the 11th International Conference on Education Technology and Computers
      October 2019
      326 pages
      ISBN:9781450372541
      DOI:10.1145/3369255
      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 ACM 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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      Published: 21 January 2020

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      Author Tags

      1. Online learning platform
      2. intervention
      3. learning behavior analytics
      4. prediction algorithm
      5. student performance model

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      Cited By

      View all
      • (2024)Online Learning Behavior Analysis and Prediction Based on Spiking Neural NetworksJournal of Social Computing10.23919/JSC.2024.00155:2(180-193)Online publication date: Jun-2024
      • (2024)Robust Kernel Extreme Learning Machines For Postgraduate Learning Performance PredictionHeliyon10.1016/j.heliyon.2024.e40919(e40919)Online publication date: Dec-2024
      • (2023)Effective Feature Prediction Models for Student PerformanceEngineering, Technology & Applied Science Research10.48084/etasr.634513:5(11937-11944)Online publication date: 13-Oct-2023
      • (2023)Lemorzsolódás előrejelzése személyre szabott értelmezhető gépi tanulási módszerek segítségévelScientia et Securitas10.1556/112.2022.001073:3(270-281)Online publication date: 6-Apr-2023
      • (2023)Stacking Integration Strategy-Based Learning Method for Prediction of Performance in Exams2023 International Conference on Neuromorphic Computing (ICNC)10.1109/ICNC59488.2023.10462748(37-42)Online publication date: 15-Dec-2023
      • (2023)Interpretive Analyses of Learner Dropout Prediction in Online STEM Courses2023 5th International Conference on Computer Science and Technologies in Education (CSTE)10.1109/CSTE59648.2023.00020(1-9)Online publication date: Apr-2023
      • (2023)Interpretable Dropout Prediction: Towards XAI-Based Personalized InterventionInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00331-834:2(274-300)Online publication date: 14-Mar-2023
      • (2022)A Predictive Model Implemented in KNIME Based on Learning Analytics for Timely Decision Making in Virtual Learning EnvironmentsInternational Journal of Information and Education Technology10.18178/ijiet.2022.12.2.159112:2(91-99)Online publication date: 2022
      • (2022)Systematic Literature Review: An Investigation Towards Finding Constructs For Performance Prediction of Students in an Online Engineering Course2022 IEEE IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)10.1109/WEEF-GEDC54384.2022.9996249(1-5)Online publication date: 27-Nov-2022
      • (2021)Predictive Model of Student Academic Performance from LMS data based on Learning AnalyticsProceedings of the 13th International Conference on Education Technology and Computers10.1145/3498765.3498768(13-19)Online publication date: 22-Oct-2021
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