8. Literature
Literature:
- Alifah, N. & Hidayat, A. R. (2025). Effectiveness of Artificial Intelligence-Based Learning Analytics Tool in Supporting Personalized Learning in Higher Education. Jurnal Pendidikan Progresif, 15(1), 74-84. Description: This study examines the effectiveness of artificial intelligence-based learning analytics tools in supporting personalised learning in higher education.
- Paradiso Solutions. (2025). AI-powered learning analytics shaping the future of education.
Description: Explains the main benefits of AI analytics in real-time personalisation, automated assessment, and interventions.
- Baker, R. S. & Siemens, G. (2024). Educational data mining and learning analytics.
Description: A detailed review of methods, models, and applications in the field of learning analytics. - Digital Learning Edge. (2024). Role of AI in learning analytics.
Description: A review of tools and approaches that use AI to track student progress, engagement, and predict success. - Restack.io. (2024). AI vs traditional analytics: A comparison.
Description: A detailed comparison of traditional and AI-supported data analytics methods. - Inisoft Global. (2024). AI-powered data analytics vs traditional analytics in education.
Description: Explains the technical and functional advantages of AI analytics in education compared to classical methods, with an emphasis on efficiency, scalability, and more accurate prediction of educational outcomes. - Dev.to. (2024). AI analytics vs traditional analytics: Which is right for you?
Description: A brief guide to the key differences and applications of both types of analytics, with practical advice for choosing the appropriate approach. - Times of India. (2024). Beyond screen time: 10 smart ways AI can revolutionise your studying.
Description: Award-winning article on the benefits of AI analytics in understanding and driving emotional engagement.
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