Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Sokkhey, Phauk; Okazaki, Takeo

doi:10.14569/ijacsa.2020.0110704

Cited by 18 publications

(9 citation statements)

References 23 publications

(29 reference statements)

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“…Table 9 summarizes the top and worst-performing prediction models of learning outcomes. Accordingly, the hybrid random forest [101] demonstrated the best classification accuracy, while the linear regression gave the worst predictions [88]. Figure 12 shows that 38 (61.29%) studies did not benchmark the performance of their intelligent models against any baseline competitors.…”

Section: Predictive Models Of Learning Outcomesmentioning

confidence: 99%

“…Examples of binary (dichotomous) classes are 'pass' and 'fail' [86,87], 'certification' and 'no certification' [85], and 'on-time graduation' and 'not on-time graduation' [60]. 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].…”

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%

See 2 more Smart Citations

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: Predictive Models Of Learning Outcomesmentioning

confidence: 99%

Section: Learning Outcomes As Indicators Of Student Performancementioning

confidence: 99%

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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“…In particular, Educational Data Mining (EDM) and Learning Analytics (LA) technologies have been applied to collect, analyse, and visualise student and learning-related data from information systems [56]. EDM and LA studies commonly take advantage of, for example, student admission data for predicting students' academic performances and designing early interventions [57][58][59]. Nonetheless, these systems have also been criticised for causing institutions to bear extra time and financial costs in assessing prior learning, administrative support, and the "hidden costs" of resistance shown by academic staff [60].…”

Section: Literature Review Of Credit Transfer Information Systemsmentioning

confidence: 99%

Prototype Development of a Cross-Institutional Credit Transfer Information System for Community College Transfer Students

Cheung

Benz

et al. 2021

Sustainability

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Credit transfer information systems in higher education are not well studied. This article demonstrates the prototype development of a cross-institutional credit transfer information system (CICIS) for community college transfer (i.e., vertical transfer) students in an Asian educational context. It exhibits credit transfer guidelines and past credit transfer records to enhance the transparency and sustainability of credit transfer information and to facilitate the transfer process of prospective community college transfer students. It also ensures the sustainability of credit transfer information and its application. The four-phase life cycle of the prototyping model was adopted to guide the study. In this paper, we report the first three phases of this development: (1) Users’ needs assessment and pre-prototyping groundwork, (2) prototype development, and (3) unforeseen circumstances and expert review. Challenges and difficulties throughout the whole process are documented and discussed. Based on this prototype development experience, a solid foundation of strategies for future engineering and enhancement of credit transfer information systems can be developed.

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“…MI: Mutual Information (MI) is used to calculate the dependency between random features. It is an asymmetric measurement that can recognize non-linear relationships between features [29]. As it calculates the statistical dependences between the features, so MI feature selection is utilized in selecting the features affecting the satisfaction of teachers on online learning during COVID-19.…”

Section: Feature Selectionmentioning

confidence: 99%

Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection

et al. 2021

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COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

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Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Cited by 18 publications

References 23 publications

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

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

Prototype Development of a Cross-Institutional Credit Transfer Information System for Community College Transfer Students

Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection

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