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Adaptive learning systems increasingly rely on learner models that assess personal traits to tailor instruction. This study examined an online adaptive learning framework that uses self-esteem, emotional intelligence, and demographic data to predict student performance and adjust instruction accordingly. A machine learning model was developed and deployed online, then evaluated through an experiment involving 146 high school students studying computer science and French as a foreign language. The predictive model achieved a 90 percent accuracy rate, showing significant links between learner traits, demographics, and final grades. Students learning via the adaptive platform outperformed peers in control groups, achieving average scores of 15.78 compared to 12.53 out of 20 in computer science, and 13.78 compared to 10.47 out of 20 in French. The findings support the development of multifactorial adaptive educational platforms.
Personalising educational content to individual emotional and demographic characteristics can substantially boost learning outcomes. By proving that traits like self-esteem and emotional intelligence directly influence academic success, this research demonstrates that digital learning environments can be made more effective when algorithms adapt instructional delivery to individual student profiles rather than relying on one-size-fits-all curricula.
The research presents an applied and tested online adaptive learning solution suited for educational technology companies, schools, and digital learning platforms. By integrating psychological metrics like emotional intelligence with performance prediction, the method could enhance digital tutoring systems and learning management software. However, the system remains at an experimental stage tested on high school subjects, and further refinement through diagnostic evaluations is planned before broader market deployment.
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Introduction:E landscapes have been transformed by technological advancements, enabling adaptive and flexible learning through AI-based and decision-oriented adaptive learning systems. The increasing importance of this solutions is underscored by the pivotal role of the learner model, representing the core of the teaching-learning dynamic. This model, encompassing qualities, knowledge, abilities, behaviors, preferences, and unique distinctions, plays a crucial role in customizing the learning experience. It influences decisions related to learning materials, teaching strategies, and presentation styles. Objective: This study meets the need for applying AI-driven adaptive learning in education, implementing a novel method that uses self-esteem (ES), emotional intelligence (EQ), and demographic data to predict student performance and adjust the learning process. Methods: Our study involved collecting and processing data, constructing a predictive machine learning model, implementing it as an online solution, and conducting an experimental study with 146 high school students in computer science and French as foreign language. The aim was to tailor the teaching-learning process to the learners' performance. Results: significant correlations were observed between self-esteem, emotional intelligence, demographic data, and final grades. The predictive model demonstrated a 90 % accuracy rate. In the experimental group, the results indicated higher scores, with an average of 15,78/20 compared to the control group's 12,53/20 in computer science. Similarly, in French as a foreign language, the experimental group achieved an average of 13,78/20, surpassing the control group's 10,47/20. Conclusion: the achieved results motivate the creation of a multifactorial AI-driven adaptive learning platform. Recognizing the necessity for improvement, we aim to refine the predicted performance score through the incorporation of a diagnostic evaluation, ensuring an optimal grouping of learners
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DOI: 10.56294/dm2023164
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