Predicting student's learning outcome from Learning management system logs

Vasić, Daniel; Kundid, Mirela; Pinjuh, Ana; Šerić, Ljiljana

doi:10.1109/softcom.2015.7314114

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…A 4-class outcome example predicted students with variable risks [101], e.g., high risk (HR), medium risk (MR), low risk (LR), and no risk (NR). Ordinal performance ranks were also predicted; for instance, student outcomes were classified into five performance ranks, specifically fail, satisfactory, good, very good, and excellent [93]. Figure 11 depicts that 80% of the models predicting academic performance standings classified the outcomes into two to four classes.…”

Section: Learning Outcomes As Indicators Of Student Performancementioning

confidence: 99%

“…Fifty-four (87.70%) studies employed single intelligent models for predicting the attainment of learning outcomes. Remarkably, only eight studies (i.e., [60,65,66,80,84,93,96,101]) explored the use of hybrid intelligent models to improve the accuracy of academic perfor-mance predictions. Hybrid or ensemble classifiers involve the integration of heterogeneous learning techniques to boost the predictive performance [106].…”

Section: Predictive Models Of Learning Outcomesmentioning

confidence: 99%

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Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

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Section: Learning Outcomes As Indicators Of Student Performancementioning

confidence: 99%

Section: Predictive Models Of Learning Outcomesmentioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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“…We found six articles that applied the NB method in predicting the academic performance. The highest accuracy was 96.9% [ 49 ] and the lowest was 65.1% [ 42 ]). Table 7 shows the accuracy results of NB methods.…”

Section: Resultsmentioning

confidence: 99%

Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance

Alsariera

Baashar

Alkawsi

et al. 2022

Computational Intelligence and Neuroscience

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Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas.

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