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Predicting Student Attrition in Higher Education through the Determinants of Learning Progress: A Structural Equation Modelling Approach

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  • Pavlos Nikolaidis

    (Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
    College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Maizatul Ismail

    (Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Liyana Shuib

    (Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Shakir Khan

    (College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
    University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India)

  • Gaurav Dhiman

    (University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 1102 2801, Lebanon
    Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India)

Abstract

Higher education policies are designed to facilitate students’ learning progression and academic success. Following Tinto’s integration theory and Bean’s attrition model, this study proposes a research model to investigate whether students prone to attrition can be pre-emptively identified through self-evaluating academic factors contributing to their learning progress. Theoretically, the learning progress is identified with student success, represented by factors amenable to intervention including the interaction with peers and instructors, teaching effectiveness, exam scores, absenteeism, students’ effort, and academic course-related variables. An exploratory and confirmatory factor analysis of 530 undergraduate students revealed that the indicators of learning progress in such students were channeled into two constructs. The results indicated that the teacher effectiveness and learning materials contributed most to the learning progress. Structural equation modelling revealed that the learning progress variables have a significant impact on students’ attrition status. A multi-group analysis confirmed the academic semesters to be a moderator in the mediating effects of the students’ grade point average (GPA). This model functions as a framework to design a student-oriented learning system promoting students’ learning experience and academic success.

Suggested Citation

  • Pavlos Nikolaidis & Maizatul Ismail & Liyana Shuib & Shakir Khan & Gaurav Dhiman, 2022. "Predicting Student Attrition in Higher Education through the Determinants of Learning Progress: A Structural Equation Modelling Approach," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13584-:d:948349
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    References listed on IDEAS

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    1. Brij B. Gupta & Akshat Gaurav & Prabin Kumar Panigrahi, 2023. "Analysis of the development of sustainable entrepreneurship practices through knowledge and smart innovative based education system," International Entrepreneurship and Management Journal, Springer, vol. 19(2), pages 923-940, June.
    2. María Fernández-Raga & Darija Aleksić & Aysun Kapucugil İkiz & Magdalena Markiewicz & Herbert Streit, 2023. "Development of a Comprehensive Process for Introducing Game-Based Learning in Higher Education for Lecturers," Sustainability, MDPI, vol. 15(4), pages 1-18, February.

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