Preventing Failures by Predicting Students’ Grades through an Analysis of Logged Data of Online Interactions

Bruno Cabral, Álvaro Figueira

2019

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.

Paper Citation

in Harvard Style

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) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 491-499. DOI: 10.5220/0008356604910499

in Bibtex Style

@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) - Volume 1: KDIR},
year={2019},
pages={491-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008356604910499},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Preventing Failures by Predicting Students’ Grades through an Analysis of Logged Data of Online Interactions
SN - 978-989-758-382-7
AU - Cabral B.
AU - Figueira Á.
PY - 2019
SP - 491
EP - 499
DO - 10.5220/0008356604910499
PB - SciTePress