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

Nikolaidis, Pavlos; Ismail, Maizatul Akmar; Shuib, Liyana; Khan, Shakir

doi:10.3390/su142013584

Cited by 12 publications

(5 citation statements)

References 90 publications

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“…Extant research related drop-out intentions to a variety of causes, ranging from students' perceptions regarding teachers' effectiveness, including the provision of feedback (Nikolaidis et al, 2022) to the effective organisation of learning and putting in the required amount of work, i.e., keeping up to date with academic assignments (Donnelly & Kovacich, 2014). However, maintaining a good work ethic is neither easy, nor to be expected in all people, and in all contexts, and many individuals postpone engaging in certain tasks that are required of them, a phenomenon commonly known as procrastinating.…”

Section: Academic Drop-out and Procrastinationmentioning

confidence: 99%

Predicting Drop-Out Intentions From Students' Perceptions of Academic Feedback and Procrastination Tendencies. 'Can Personalized Feedback Sometimes Be Too Much of a Good Thing?'

2023

Proceedings of the 17 Th International Conference on E-Learning and Digital Learning (ELDL 2023) and 11th International Confere

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In this research, we investigated a three-way relationship between academic feedback, procrastination tendencies, and the intention of dropping out of school in a sample of 532 undergraduate students. We used a cross-sectional, associative research design, with scales developed based on corroborating themes, obtained in previous interviews with the students, with instruments from the literature. The models were tested using variance-based SEM (PLS) and our measurements showed very good psychometric properties. The study's findings indicate that overall, academic feedback is negatively associated with both procrastination and drop-out intention, but when the personalization aspect of the academic feedback is differentiated from the 'impersonal' academic feedback, it might not help mitigate the intention to drop out. Our findings provide additional support to existing research while opening a new line of inquiry into the interplay between the three main constructs mentioned above, which, to date, are scarcely studied together. Albeit with several conceptual and methodological limitations, educational researchers and educators can benefit from the evidence provided in this research.

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Section: Academic Drop-out and Procrastinationmentioning

confidence: 99%

Predicting Drop-Out Intentions From Students' Perceptions of Academic Feedback and Procrastination Tendencies. 'Can Personalized Feedback Sometimes Be Too Much of a Good Thing?'

2023

Proceedings of the 17 Th International Conference on E-Learning and Digital Learning (ELDL 2023) and 11th International Confere

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“…This study presents a research methodology to examine whether at-risk students may be proactively detected via self-evaluation of academic aspects contributing to their learning development, drawing inspiration from Tinto's integration theory and Bean's attrition model. [13].…”

Section: S C Matz Et Al (mentioning

confidence: 99%

Comprehensive analysis of factors influencing the real-world application of machine learning for student success rate calculation and their impacts on student achievement & educational institutions

Divvyam Arora

2023

World J. Adv. Res. Rev.

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The successful application of machine learning for student success rate calculation is contingent upon a comprehensive analysis of several interrelated factors that span the educational landscape. Educational institutions must ensure that they have access to accurate and relevant data, including student demographics, academic performance records, and even extracurricular activities, to build effective predictive models. Moreover, data privacy and security concerns must be addressed to maintain the integrity of sensitive student information. Next, the choice of machine learning algorithms is crucial. The selection should align with the specific objectives of predicting student success, taking into account the type and volume of data available. Factors such as classification algorithms, regression techniques, and deep learning models must be carefully considered, and their performance must be assessed through rigorous testing and validation. Addressing potential biases in machine learning models is crucial to ensure equitable outcomes. Careful attention must be paid to the training data, as biased data can lead to discriminatory predictions. Ongoing monitoring and model refinement are necessary to minimize these biases and promote fairness in student success predictions. Thus present paper is focused on a comprehensive analysis of factors influencing the real-world application of machine learning for student success rate. In order to achieve this goal, relevant researches on machine learning is considered and further enhancement has been made to make the proposed work more efficient.

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“…Research also shows that they have a decreased ability to pay constant cognitive attention [16]. Therefore, educators should follow modern educational trends and use different teaching methods and approaches enhanced by ICT to motivate and engage digital native students in the learning process [17].…”

Section: Introductionmentioning

confidence: 99%

“…It is also quite difficult to integrate course content and learning objectives, especially in technical subjects, into interesting and engaging storytelling on which GBL is based. In addition, learning progress, as measured by academic integration, social integration, performance, and students' persistence in learning, is critical to students' success [17]. Due to the characteristics and speed of implementation of GBL activities, lecturers may have limited insight into students' progress and, therefore restricted ability to create feedback for students, resulting in students not excelling in the GBL activity or even becoming stuck along the way without the lecturer noticing [17].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, learning progress, as measured by academic integration, social integration, performance, and students' persistence in learning, is critical to students' success [17]. Due to the characteristics and speed of implementation of GBL activities, lecturers may have limited insight into students' progress and, therefore restricted ability to create feedback for students, resulting in students not excelling in the GBL activity or even becoming stuck along the way without the lecturer noticing [17]. Moreover, o According to Pho and Dinscore [19], GBL involves developing games for students and designing learning activities that can incrementally introduce concepts and guide users toward an end goal.ne of the important reasons for the limited diffusion of GBL in the educational setting is the fact that the literature in the field is very fragmented, making it very time-consuming for lecturers to become familiar with the method to integrate it into the teaching process.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Comprehensive Process for Introducing Game-Based Learning in Higher Education for Lecturers

et al. 2023

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Emerging trends such as digitalization, globalization, and the COVID-19 pandemic are forcing higher education institutions to undergo constant organizational and technological changes and to introduce innovative pedagogical approaches suitable for teaching a new generation of students—the so-called digital natives. The goal of this paper is to engage in the ongoing debate in higher education about new teaching methods, i.e., game-based learning methods, which meet the needs of digital natives. They have grown up in a fast-paced, technology-driven society, which has affected how they absorb information, their ability to concentrate for extended periods, and their motivation and engagement in the learning process. Existing research suggests that implementing the game-based learning method can be very difficult and costly, as it often requires adapting the freely available game to the requirements of the particular course and additional investment in purchasing appropriate equipment. In this paper, we develop a comprehensive procedure for introducing a cost-effective game-based learning method in higher education, which includes thirteen steps to help lecturers introduce game-based activities straightforwardly into their teaching processes. In addition, we also present security, cultural, and quality assurance issues that need to be considered when implementing game-based learning in higher education.

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

Cited by 12 publications

References 90 publications

Predicting Drop-Out Intentions From Students' Perceptions of Academic Feedback and Procrastination Tendencies. 'Can Personalized Feedback Sometimes Be Too Much of a Good Thing?'

Predicting Drop-Out Intentions From Students' Perceptions of Academic Feedback and Procrastination Tendencies. 'Can Personalized Feedback Sometimes Be Too Much of a Good Thing?'

Comprehensive analysis of factors influencing the real-world application of machine learning for student success rate calculation and their impacts on student achievement & educational institutions

Development of a Comprehensive Process for Introducing Game-Based Learning in Higher Education for Lecturers

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