Early Detection of Under-Performing Students Using Machine Learning Algorithms

Alalawi, Khalid; Chiong, Raymond; Athauda, Rukshan

doi:10.1109/citisia53721.2021.9719896

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…4. The ethical issues of utilizing machine learning to predict student achievement have received little attention (Alalawi et al, 2023). These research gaps suggest that there is a need for more standardized approaches to feature selection and data preprocessing in machine learning models for predicting student performance.…”

Section: Research Gap Identificationmentioning

confidence: 99%

Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

Pandian,

Aziz,

Subramaniam

et al. 2024

Multidiscip. Rev.

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The literature on the role of machine learning in forecasting student performance in education lacks comprehensive data quality, equitable and interpretable models, consideration of contextual and causal factors, and the integration of human expertise. This review will explore machine learning types, algorithms, predictive performance, and impact on student performance in education. A systematic literature review based on articles published in the past 2019–2023 period Methods: The IEEE Xplore database was searched by using keywords such as "Educational Data Mining," "Student Performance Prediction," "Evaluations of Students," "Performance Analysis of Students," and "Learning Curve Prediction." were employed and 50 papers were selected. Results: The analysis of the results highlighted prominent patterns. Half of the studies favored supervised learning methods, with decision trees leading (19 instances), followed by Long Short-Term Memory and Random Forest (16 each), and K-Nearest Neighbor and Naive Bayes (12 and 11 times). Support Vector Machine and Logistic Regression were noted 10 and 9 times, respectively. Noteworthy were ANN, CNN, and Xgboost. Positive impacts were evident in 36 cases; only one showed negative effects, while 13 indicated intricate relationships. This study helped in understanding the prevalent machine learning methods used for predicting student performance, provides a benchmark for assessing the effectiveness of new or alternative techniques. Conclusion: This review highlights the varied machine learning uses in predicting student performance in education, emphasizing supervised methods, diverse algorithms, and complex intervention impacts.

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Section: Research Gap Identificationmentioning

confidence: 99%

Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

Pandian,

Aziz,

Subramaniam

et al. 2024

Multidiscip. Rev.

View full text Add to dashboard Cite

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Evaluating the student performance prediction and action framework through a learning analytics intervention study

Alalawi,

Athauda,

Chiong

et al. 2024

Educ Inf Technol

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Learning analytics intervention (LAI) studies aim to identify at-risk students early during an academic term using predictive models and facilitate educators to provide effective interventions to improve educational outcomes. A major impediment to the uptake of LAI is the lack of access to LAI infrastructure by educators to pilot LAI, which typically requires substantial institution-wide efforts and investment to collect related data sets and develop accurate predictive models that identify at-risk students and also provide tools to facilitate interventions. This paper presents a novel LAI framework, termed Student Performance Prediction and Action (SPPA), that facilitates educators to seamlessly provide LAIs in their courses avoiding the need for large-scale institution-wide efforts and investments. Educators develop course-specific predictive models using historical course assessment data. In learning analytics, providing effective interventions is a challenge. SPPA utilises pedagogy principles in course design and interventions to facilitate effective interventions by providing insights into students’ risk levels, gaps in students’ knowledge, and personalised study/revision plans addressing knowledge gaps. SPPA was evaluated in a large undergraduate course on its ability to predict at-risk students and facilitate effective interventions as well as its ease of use by academics. The results are encouraging with high performance of predictive models, facilitating effective interventions leading to significant improved educational outcomes with positive feedback and uptake by academics. With its advantages, SPPA has the potential to catalyse and influence wide-scale adoption in LAIs.

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An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention

Alalawi,

Athauda,

Chiong

2024

Int J Artif Intell Educ

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The use of educational data mining and machine learning to analyse large data sets collected by educational institutions has the potential to discover valuable insights for decision-making. One such area that has gained attention is to predict student performance by analysing large educational data sets. In the relevant literature, many studies have focused on developing prediction models on student performance but comparatively less work exists on actions taken based on predicted at-risk students and evaluating their impact. Learning Analytics Intervention (LAI) studies have emerged as an approach that aims to address this gap. In LAI studies, student risk levels are predicted and disseminated to relevant stakeholders (academics, administrators and students) using learning analytics (LA) tools for targeted interventions. The interventions themselves are mainly left under the discretion of the academics and/or administrators, who are aware of the learning context and have the authority to make decisions, with LA tools facilitating this process. LAI studies have shown success in improving outcomes (e.g. improve pass rates, retention, grades), but their uptake has been slow. The main impediment to piloting LAIs by academics has been the lack of access to LAI infrastructure, which requires institutional investments to develop predictive models collecting data from diverse IT systems. Another challenge in LAIs is the development of effective interventions. This paper presents an extended LAI framework, termed Student Performance Prediction and Action (SPPA), which provides access to LAI infrastructure for academics to pilot LAIs in their courses without the need for institution-wide efforts. SPPA and its features are seamlessly accessed via a web browser and academics can develop course-specific predictive models based on historical course assessment data. Furthermore, SPPA integrates sound pedagogical approaches and provides relevant information (such as students’ knowledge gaps, personalised study plans) to assist academics in providing effective interventions. SPPA was evaluated by a number of academics piloting LAIs in their courses. Quantitative and qualitative data was collected and analysed. Academics were able to provide effective interventions using SPPA and also had a positive outlook on using SPPA and its features. SPPA is also provided as an open-source project for further development and can be a catalyst for widescale uptake in LAIs. Furthermore, a model for continuous improvement in LAIs is outlined along with a number of areas for future research and development.

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Early Detection of Under-Performing Students Using Machine Learning Algorithms

Cited by 4 publications

References 16 publications

Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

Evaluating the student performance prediction and action framework through a learning analytics intervention study

An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention

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