Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Intelligent Tutoring Systems and Adaptive Learning · Data Stream Mining Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Escuela Polítécnica, Universidad de Extremadura, 10003 Cáceres, Spain
Funding
Government of Extremadura (Spain) · State Research Agency, Spain
References (69)
Show source-provided references
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