Learner to learner fuzzy profiles similarity using a hybrid interaction analysis grid

TL;DR

This paper develops a fuzzy logic-based system to analyze remote learner interactions, creating behavioral profiles and measuring similarities to enhance understanding of online educational discussions.

Contribution

It introduces a hybrid interaction analysis grid combining behavioral profiling with fuzzy logic and data mining techniques for remote learning environments.

Findings

Fuzzy logic effectively translates profile descriptions into mathematical formats.

Learner behaviors exhibit irregular patterns.

Eros similarity measure outperforms PCA in precision.

Abstract

The analysis of remote discussions is not yet at the same level as the face-to-face ones. The present paper aspires twofold. On the one hand, it attempts to establish a suitable environment of interaction and collaboration among learners by using the speech acts via a semi structured synchronous communication tool. On the other, it aims to define behavioral profiles and interpersonal skills hybrid grid by matching the BALES' IPA and PLETY's analysis system. By applying the fuzzy logic, we formalize human reasoning and, thus, giving very appreciable flexibility to the reasoning that use it, which makes it possible to take into account imprecisions and uncertainties. In addition, the educational data mining techniques are used to optimize the mapping of behaviors to learner's profile, with similarity-based clustering, using Eros and PCA measures. In order to show the validity of our system, we performed an experiment on real-world data. The results show, among others: (1) the usefulness of fuzzy logic to properly translate the profile text descriptions into a mathematical format, (2) an irregularity in the behavior of the learners, (3) the correlation between the profiles, (4) the superiority of Eros method to the PCA factor in precision.