Artificial Intelligence and Persuasive Computing Approach for Motivating Students and Enhancing their Learning Experience
Notice bibliographique
Résumé
Computing technologies offer promising approaches that could be leveraged to improve education and learning globally. Online educational systems, including learning management systems (LMS), have transformed education at all levels by making learning resources and services more accessible to diverse students across various levels of education. Universities widely employ LMS to deliver learning materials and resources to students. Recognizing that lectures alone may not be sufficient for deep understanding; students are encouraged to actively engage with the provided content and resources to acquire the necessary knowledge and skills. However, sustaining students’ motivation and engagement over time has become one of the largest barriers to effective learning with the systems. Without sustained motivation, students may disengage, leading to poor learning outcomes and diminished educational experiences. Identifying strategies that promote continuous motivation and meaningful student engagement is critical to unlocking the full potential of LMS and ensuring long-term educational success. Artificial intelligence (AI) and persuasive technology (PT) present elegant solutions that can be employed to make online educational systems more motivating and engaging for students to sustain them in achieving desired learning goals. An interesting aspect of the AI approach is that the learning states of students can be modelled in real-time based on their learning behaviour without distraction to their learning, and this will enable personalized interventions to be tailored to their needs. PT, on the other hand, leverages motivational appeals (such as self-monitoring, social comparison, and competition) in sustaining students’ engagement to achieve their learning goals. To contribute to improving online educational systems to better motivate students and enhance their learning experience, I explored AI techniques and motivational appeals of PT strategies in supporting students' engagement in learning. My research involved several key components: First, using a dataset of 924 university students and the self-determination theory framework, I employed machine learning techniques to examine the impact of motivation dimensions on students’ study strategies and academic performance. Second, building on the findings from the previous study, I applied machine learning methods to analyze learning interaction logs from two different sources: 488 students’ data from an eBook platform and over 125,000 students’ data from massive open online courses. This analysis aimed to investigate the relationship between data-driven engagement measures and academic performance, as well as to model students' engagement levels using a data-driven approach. Third, in an empirical study with 628 university students, I investigated the effects of three PT strategies (social comparison, social learning, and competition) operationalized in system design and integrated into a learning management system for real-world university course on students’ engagement and academic performance. Fourth, I examined the persuasiveness of four PT strategies and developed models that explored their relationship with self-determination theory constructs. I also developed and evaluated persuasive messages based on three strategies: self-monitoring, commitment & consistency, and social comparison. Fifth, I developed machine learning models to predict students' motivation levels based on their tracked learning behaviour. I then applied the model and personalized persuasive intervention to investigate the potential of leveraging AI and personalized persuasive intervention in promoting student motivation and engagement in a real-world university course using an LMS. The results of my evaluations demonstrated that PT strategies effectively increased students' motivation and engagement. Personalized persuasive interventions and machine learning models demonstrated to be an effective approach for providing adaptive support tailored to students' learning contexts. These findings suggest that AI techniques and PT are viable approaches that can be incorporated into online educational systems to improve their effectiveness in motivating students and enhancing their learning experience.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».