Learning Analysis as a Tool for Predicting Student Performance

Verhun, Volodymyr; Batyuk, Anatoliy; Voityshyn, Volodymyr

doi:10.1109/stc-csit.2018.8526741

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…With a similar focus, other SL algorithms analyzed datasets directly from websites to evaluate students' performance [21]. Also software platforms in e-learning made it possible to analyze and take advantage of the results of DM and ML algorithms in order to make decisions and justify educational approaches [22].…”

Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

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Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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Quality of distance learning from the point of view of the assessment results of students’ knowledge

Datsko,

Vytrykush,

Romaniv

et al. 2023

2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT)

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Learning Analysis as a Tool for Predicting Student Performance

Cited by 3 publications

References 7 publications

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

A survey on educational data mining methods used for predicting students' performance

Quality of distance learning from the point of view of the assessment results of students’ knowledge

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