Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learn...

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Vydáno v:Applied sciences Ročník 9; číslo 24; s. 5523
Hlavní autoři: Buschetto Macarini, Luiz Antonio, Cechinel, Cristian, Batista Machado, Matheus Francisco, Faria Culmant Ramos, Vinicius, Munoz, Roberto
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2019
Témata:
Algorithms
At risk students
Colleges & universities
Computer programming
Computer science
Core curriculum
Data mining
Datasets
Distance learning
Learning management systems
Success
Teaching
ISSN:2076-3417, 2076-3417
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Shrnutí:Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.
Bibliografie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app9245523