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Classifying Student Sentiments in Learning on Video-Conference Platforms: Human-AI Synergistic Approach Through Machine Learning and Self-Determination Theory
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Author(s): Bosede Iyiade Edwards (Universiti Sains Malaysia, Malaysia), Daniel Vong (Wangi Lai PLT, Malaysia), Olanrewaju Steve Edwards (Arrows Education, Malaysia)and Margaret Olusola Abimbola (University of Ilesa, Nigeria)
Copyright: 2026
Pages: 10
Source title:
Generators, Bots, and Tutors: Creative Approaches to Human-AI Synergy in Classroom Instruction
Source Author(s)/Editor(s): Bosede Iyiade Edwards (Universiti Sains Malaysia, Malaysia), Hassan Abuhassna (Sunway University, Malaysia), Damola Olugbade (University of South Africa, Pretoria, South Africa), Olayinka Anthony Ojo (University of Bolton, United Kingdom)and Wan Ahmad Jaafar Wan Yahaya (Universiti Sains Malaysia, Malaysia)
DOI: 10.4018/979-8-3373-0847-0.ch009
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Abstract
This study analyzes learner sentiments on video-conference platforms (VCPs) in online education, contributing to discourse on “Creative Approaches to Human-AI Synergy in Classroom Instruction” by showing how AI/ML tools can evaluate student feedback to enhance VCPs and AI-powered instruction. Using a mixed-methods design, it integrated Azure Machine Learning (AML) with a multidimensional sentiment model and mapped findings to Self-Determination Theory (SDT). Feedback from 264 students was analyzed via AML's three-category sentiment system (positive, neutral, negative) and a five-category qualitative model (positive, negative, neutral, insightful, confused). Sentiments were linked to SDT's autonomy, competence, and relatedness. While learners valued VCP flexibility (autonomy), they reported issues like limited interaction and technical barriers (competence and relatedness). Sentiment patterns varied by demographics. The study highlights the benefit of hybrid analytical models and recommends improving VCP design to support diverse learner needs and enhance all three SDT dimensions.
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