Learner to learner fuzzy profiles similarity using a hybrid interaction analysis grid
This work addresses the challenge of improving interaction analysis for remote learners, but it is incremental as it combines existing methods like fuzzy logic and data mining for a specific educational domain.
The paper tackled the problem of analyzing remote learner discussions by developing a hybrid grid to define behavioral profiles and interpersonal skills, using fuzzy logic and educational data mining techniques; results demonstrated the superiority of the Eros method over PCA in precision and highlighted correlations between profiles.
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.