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Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades

Author:

S. B. KotsiantisAuthors Info & Claims

Artificial Intelligence Review, Volume 37, Issue 4

Pages 331 - 344

https://doi.org/10.1007/s10462-011-9234-x

Published: 01 April 2012 Publication History

Abstract

Use of machine learning techniques for educational proposes (or educational data mining) is an emerging field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns. The stored data (virtual courses, e-learning log file, demographic and academic data of students, admissions/registration info, and so on) can be useful for machine learning algorithms. In this article, we cite the most current articles that use machine learning techniques for educational proposes and we present a case study for predicting students' marks. Students' key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student's performance. Finally, a prototype version of software support tool for tutors has been constructed.

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Published In

cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 37, Issue 4

April 2012

83 pages

ISSN:0269-2821

Issue’s Table of Contents

Copyright © Copyright © 2012 Springer Science+Business Media B.V.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2012

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https://dl.acm.org/doi/10.1007/s00521-024-09861-1
Köktürk-Güzel BBüyük OBozkurt BBaysal O(2023)Automatic assessment of student rhythmic pattern imitation performancesDigital Signal Processing10.1016/j.dsp.2022.103880133:COnline publication date: 1-Mar-2023
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Dervenis CKyriatzis VStoufis SFitsilis P(2022)Predicting Students' Performance Using Machine Learning AlgorithmsProceedings of the 6th International Conference on Algorithms, Computing and Systems10.1145/3564982.3564990(1-7)Online publication date: 16-Sep-2022
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