Abstract
In the past few years, interest in applying intelligent data-mining techniques to educational datasets has increased rapidly, with goals ranging from identifying students who need further support to being able to infer or predict a student’s final grade based on their behaviour during the learning process. Even more amongst students enrolled from all Latin America. This problem can be solved with solid technical approaches, but blind brute-force data analysis approaches may prove insufficient to accurately predict grades, and even if they managed, instructors may need to further understand why and how these algorithms predict specific grades. In this work, we use an experiment to better understand how different parts of the dataset influence the performance of different grade prediction algorithms. The goal is not to achieve the best possible prediction of student’s individual performance in an online university setting, with premises in half a dozen Latin American countries, and with Latin American students, but rather to identify which types of student activities are better predictors of the student’s actual performance.
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Notes
- 1.
The GradeInsight tool is freely available as an open-source tool: https://github.com/vicgg/GradeInsight.
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Acknowledgements
This work was partially funded by Universidad Internacional de La Rioja (UNIR, http://www.unir.net) through the Research Institute for Innovation & Technology in Education (UNIR iTED, http://ited.unir.net) and the IBM-UNIR Chair on Data Science in Education. We acknowledge the support from the IT department at UNIR, especially Edgar Caballero and Raúl Gómez, who actively helped in extracting the raw data for composing the datasets in this study.
Author Contributions
Each author contributed evenly to this paper. All authors read and approved the final manuscript.
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No external funding was received.
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The authors declare no conflict of interest.
Statements on Open Data, Ethics, and Conflict of Interest
This research work was carried out following strict data protection guidelines. All personal data in the dataset were eliminated and substituted by anonymous identifiers using non-reversible cryptographic algorithms. The protocols and guidelines for data protection were checked and approved by the data protection officer from the university. The educational dataset used to support the findings of this study is available from the corresponding author upon request. The GradeInsight tool is freely available as an open-source project: https://github.com/vicgg/GradeInsight.
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Moreno-Ger, P., Burgos, D. (2021). Machine Learning and Student Activity to Predict Academic Grades in Online Settings in Latam. In: Burgos, D., Branch, J.W. (eds) Radical Solutions for Digital Transformation in Latin American Universities. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-3941-8_13
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