Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
By Juan L. Rastrollo-Guerrero, Juan A. Gómez-Pulido, Arturo Durán-Domínguez. Applied Sciences, 2020
303 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/db067d2912fe8b8350082df2f96125a1c1e8dd87
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In Artificial Intelligence
In Computer Science Applications
Citations in the corpus
- Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review (2020, background): “The intelligent techniques employed in learning analytics to forecast student achievements are generally categorized into supervised learning, unsupervised learning, data mining, and statistical approaches [3,28].”