Abstract
Abstract In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context.
Highlights
E-learning was presented as the best solution to cover the needs and requirements of remote students, and as a helping tool in the teaching-learning process, reinforcing or replacing face-to-face education
It allows extracting linguistic rules that are understandable by experts in an educative domain and that help them to understand students’ learning behaviour. This is performed by means of the Linguistic Rules extraction algorithm (LR-Fuzzy Inductive Reasoning (FIR)) that is an extension of the FIR methodology
The best model identified for the didactic planning course includes the average marks of the co-evaluation (COEV), the initial class plan (IC), and the experience report (ER) features as the most relevant features to predict the final mark of the course (MARK) for each student
Summary
E-learning was presented as the best solution to cover the needs and requirements of remote students, and as a helping tool in the teaching-learning process, reinforcing or replacing face-to-face education. One of the most difficult and time consuming activities for teachers in distance education courses is the evaluation process, due to the fact that the reviewing process in this kind of courses is better accomplished through collaborative resources such as email, discussion forums, chats, etc As a result, this evaluation usually has to be done according to a large number of factors, whose influence in the final mark is not always well defined and/or understood. It would be helpful in order to reduce the intrinsic system evaluation dimensionality to identify the factors that are highly relevant for the students’ evaluation This will help teachers to provide feedback to students in function of their learning behaviour in real time. It allows extracting linguistic rules that are understandable by experts in an educative domain and that help them to understand students’ learning behaviour This is performed by means of the Linguistic Rules extraction algorithm (LR-FIR) that is an extension of the FIR methodology.
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