Browsing by Author "Bouhlel, M.S"

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Exploring the Impact of Multimodal Adaptive Learning with Tangible Interaction on Learning Motivation
(Institute of Electrical and Electronics Engineers Inc., 12/15/2020) Chettaoui, N; Atia, A; Bouhlel, M.S
Embodied learning defines a contemporary pedagogical theory focusing on ensuring an interactive learning experience through full-body movement. Within this pedagogy, several studies in Human-Computer Interaction have been conducted, incorporating gestures, and physical interaction in different learning fields. This paper presents the design of a multimodal and adaptive space for embodied learning. The main aim is to give students the possibility to use gestures, body movement, and tangible interaction while interacting with adaptive learning content projected on the wall and the floor. Thus, this study aims to explore how tangible interaction, as a form of implementing embodied learning, can impact the motivation of students to learn compared to tablet-based learning. Eighteen primary school students aged nine and ten years old participated in the study. The average percentages of answers on the Questionnaire on Current Motivation (QCM) pointed out a higher motivation among students learning via tangible objects. Results revealed a positive score for the Interest of learning abstract concepts using a tangible approach with a mean score of 4.78, compared to 3.77 while learning via a tablet. Furthermore, Success and Challenge measures, with a mean score of 4.67 and 4.56 indicate that physical interaction via tangible objects leads to significantly higher motivation outcomes. These findings suggest that learning might benefit more from a multimodal and tangible physical interaction approach than the traditional tablet-based learning process. © 2020 IEEE
Predicting Student Performance in an Embodied Learning Environment
(Institute of Electrical and Electronics Engineers Inc., 5/27/2021) Chettaoui, N; Atia, A; Bouhlel, M.S
Implementing machine learning techniques is receiving considerable attention in the educational technology research field. Different systems and techniques were proposed to predict student performance and gain insights regarding their learning needs. However, to the best of our knowledge, no previous research studies explored predicting student performance based on their interaction with different modalities in a real classroom context In this paper, we have proposed an adaptive learning engine to recommend embodied activities and interaction modalities according to students learning profile, prior knowledge, and academic performance. We have firstly conducted a comparative study by analyzing two educational datasets. Among five classification algorithms, Random Forest outperformed with 84% as accuracy. Thus, in a second phase, we have explored predicting students' performance with our dataset generated from embodied learning environment. The accuracy of the proposed model using student profile, academic, and behavioral features reached an accuracy of 62%. These results highlighted the potential of implementing machine learning techniques to classify and recommend embodied interaction