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Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches

Soft Computing Research Group at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya—BarcelonaTech, Jordi Girona 29, 08034 Barcelona, Spain
Centro de Investigación en Tecnologías de Información y Sistemas Universidad Autónoma del Estado de Hidalgo, 42184 Pachuca, Hidalgo, Mexico
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6804;
Received: 19 August 2020 / Revised: 18 September 2020 / Accepted: 27 September 2020 / Published: 28 September 2020
(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)
In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. The introductory and didactic planning courses were analyzed using the proposed toolbox. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload. View Full-Text
Keywords: data mining; e-Learning; toolbox; FIR; LR-FIR; CR-FIR data mining; e-Learning; toolbox; FIR; LR-FIR; CR-FIR
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MDPI and ACS Style

Nebot, À.; Mugica, F.; Castro, F. An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches. Appl. Sci. 2020, 10, 6804.

AMA Style

Nebot À, Mugica F, Castro F. An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches. Applied Sciences. 2020; 10(19):6804.

Chicago/Turabian Style

Nebot, Àngela, Francisco Mugica, and Félix Castro. 2020. "An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches" Applied Sciences 10, no. 19: 6804.

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