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Authors: Bruno Cabral and Álvaro Figueira

Affiliation: CRACS / INESCTEC, University of Porto, Rua do Campo Alegre 1021/55, Porto and Portugal

Keyword(s): Data Mining, e-Learning, Machine Learning, Online Interaction Comparison, Prediction of Failures, Learning Management System.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Pre-Processing and Post-Processing for Data Mining ; Soft Computing ; Symbolic Systems

Abstract: Nowadays, students commonly use and are assessed through an online platform. New pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be intense feedback from these activities to students, usually it is restricted to the assessments of the online set of tasks. We propose a model that informs students of abnormal deviations of a “correct” learning path. Our approach is based on the vision that, by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. In the major learning management systems available, the interaction between the students and the system, is stored in log. Our proposal uses that logged information, and new one computed by our methodology, such as the time each student spends on an activity, the number and order of resources used, to build a table that a machine learning algorithm can learn from. Results show that our model can predict with more than 86% accuracy the failing situations. (More)

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Paper citation in several formats:
Cabral, B. and Figueira, Á. (2019). Preventing Failures by Predicting Students’ Grades through an Analysis of Logged Data of Online Interactions. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 491-499. DOI: 10.5220/0008356604910499

@conference{kdir19,
author={Bruno Cabral. and Álvaro Figueira.},
title={Preventing Failures by Predicting Students’ Grades through an Analysis of Logged Data of Online Interactions},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR},
year={2019},
pages={491-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008356604910499},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR
TI - Preventing Failures by Predicting Students’ Grades through an Analysis of Logged Data of Online Interactions
SN - 978-989-758-382-7
IS - 2184-3228
AU - Cabral, B.
AU - Figueira, Á.
PY - 2019
SP - 491
EP - 499
DO - 10.5220/0008356604910499
PB - SciTePress