research-article

Mining Moodle Data to Detect the Inactive and Low-performance Students during the Moodle Course

Authors:

Mushtaq Hussain,

Paraskevi Theodorou,

Syed Muhammad Raza AbidiAuthors Info & Claims

ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

Pages 133 - 140

https://doi.org/10.1145/3291801.3291828

Published: 27 October 2018 Publication History

Abstract

In web-based learning systems such as massive open online course (MOOC) and modular object-oriented developmental learning environment (Moodle), monitoring the student's activities as well as predict the low-performance students is an important task because it enables the instructors to award the students when their activities level drops from normal activities levels as well as having lower grades. We used several machine learning (ML) classification and clustering techniques to extract the pattern from student data during completing the Moodle course; which enables the instructor to detect the low-performance student in advance before the examination. The experimental result shows that the fuzzy unordered rule induction algorithm (FURIA) classification technique achieves high accuracy in detecting inactive students as well as predicts the different categories of the student during the Moodle course. The K-means clustering is also able to group the inactive and active users and poorly performed users. The result demonstrates that our proposed system will be easily integrated to Moodle system to send alert to inactive and low- performance students while completing the course and build efficient education environment for the students.

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Karapiperis DTzafilkou KTsoni RFeretzakis GVerykios V(2023)A Probabilistic Approach to Modeling Students’ Interactions in a Learning Management System for Facilitating Distance LearningInformation10.3390/info1408044014:8(440)Online publication date: 4-Aug-2023
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Liashuk X(2023)Relation between Moodle Activity and Student Performance in the Context of EFL Training in Higher EducationJournal of Language and Cultural Education10.2478/jolace-2022-000310:1(25-37)Online publication date: 1-Mar-2023
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Index Terms

Mining Moodle Data to Detect the Inactive and Low-performance Students during the Moodle Course
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Other conferences

ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

October 2018

221 pages

ISBN:9781450364768

DOI:10.1145/3291801

Copyright © 2018 ACM.

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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Shandong Univ.: Shandong University
University of Queensland: University of Queensland
Dalian Maritime University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2018

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Funding Sources

National Natural Science Foundation of China
National Key R&D Program of China

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ICBDR 2018

ICBDR 2018: 2018 The 2nd International Conference on Big Data Research

October 27 - 29, 2018

Weihai, China

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Cited By

Kaur HKaur TBhardwaj VKumar M(2024)An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysisDiscover Applied Sciences10.1007/s42452-024-06274-66:11Online publication date: 7-Nov-2024
https://doi.org/10.1007/s42452-024-06274-6
Karapiperis DTzafilkou KTsoni RFeretzakis GVerykios V(2023)A Probabilistic Approach to Modeling Students’ Interactions in a Learning Management System for Facilitating Distance LearningInformation10.3390/info1408044014:8(440)Online publication date: 4-Aug-2023
https://doi.org/10.3390/info14080440
Liashuk X(2023)Relation between Moodle Activity and Student Performance in the Context of EFL Training in Higher EducationJournal of Language and Cultural Education10.2478/jolace-2022-000310:1(25-37)Online publication date: 1-Mar-2023
https://doi.org/10.2478/jolace-2022-0003
Peraić IGrubišić A(2023)Exploring Student Engagement in Online Programming Courses: A Two-Level K-means Analysis2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)10.23919/SoftCOM58365.2023.10271619(1-6)Online publication date: 21-Sep-2023
https://doi.org/10.23919/SoftCOM58365.2023.10271619
Alhothali AAlbsisi MAssalahi HAldosemani T(2022)Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A ReviewSustainability10.3390/su1410619914:10(6199)Online publication date: 19-May-2022
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Gaftandzhieva STalukder AGohain NHussain STheodorou PSalal YDoneva R(2022)Exploring Online Activities to Predict the Final Grade of StudentMathematics10.3390/math1020375810:20(3758)Online publication date: 12-Oct-2022
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Adnan MUddin MKhan EAlharithi FAmin SAlzahrani A(2022)Earliest Possible Global and Local Interpretation of Students’ Performance in Virtual Learning Environment by Leveraging Explainable AIIEEE Access10.1109/ACCESS.2022.322707210(129843-129864)Online publication date: 2022
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Peraić IGrubišić A(2022)Development and Evaluation of a Learning Analytics Dashboard for Moodle Learning Management SystemHCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games10.1007/978-3-031-22131-6_30(390-408)Online publication date: 26-Jun-2022
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Skalka JDrlik M(2020)Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students’ Outcomes in the Introductory Programming CoursesApplied Sciences10.3390/app1013456610:13(4566)Online publication date: 30-Jun-2020
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Vakaloudis ACahill BO’Leary CChalla D(2020)Preparation and execution of final year student projects on the cloud2020 IEEE Frontiers in Education Conference (FIE)10.1109/FIE44824.2020.9273971(1-7)Online publication date: 21-Oct-2020
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