Personalized Instructional Strategy Adaptation Using TOPSIS: A Multi-Criteria Decision-Making Approach for Adaptive Learning Systems DOI Creative Commons
Christos Troussas, Akrivi Krouska, Phivos Mylonas

et al.

Information, Journal Year: 2025, Volume and Issue: 16(5), P. 409 - 409

Published: May 15, 2025

The growing number of educational technologies presents possibilities and challenges for personalized instruction. This paper a learner-centered decision support system selecting adaptive instructional strategies, that embeds the Technique Order Preference by Similarity to Ideal Solution (TOPSIS) in real-time learning environment. uses multi-dimensional learner performance data, such as error rate, time-on-task, mastery level, motivation, dynamically analyze recommend best pedagogical intervention from pool which includes hints, code examples, reflection prompts, targeted scaffolding. In developing system, we chose employ it one-off postgraduate Java programming course, this represents defined cognitive load structure samples spectrum learners. A robust evaluation was conducted with 100 students an compared static/no control condition. TOPSIS yielded statistically higher outcomes (normalized gain g = 0.49), behavioral engagement (28.3% increase tasks attempted), satisfaction. total 85.3% expert evaluators agreed decisions lecturer’s preferred teaching response towards prescribed problems behaviors. comparison rule-based approach, clear framework provided more granular effective adaptation. findings validate use multi-criteria decision-making underscore transparency, flexibility, potential proposed across broader domains.

Language: Английский

What Influences College Students Using AI for Academic Writing? - A Quantitative Analysis Based on HISAM and TRI Theory DOI Creative Commons
Yulu Cui

Computers and Education Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 100391 - 100391

Published: March 1, 2025

Language: Английский

Citations

1
AI-Integrated Personalized Learning for High School Students DOI Open Access

Hanh Dinh Thi My,

Thai Thanh Tuan,

Bao Nguyen Dinh

et al.

World Journal of Engineering and Technology, Journal Year: 2025, Volume and Issue: 13(02), P. 147 - 165

Published: Jan. 1, 2025

Language: Английский

Citations

0
Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions DOI

Nancy Shamaylah,

Sulieman Ibraheem Shelash Al-Hawary, Badrea Al Oraini

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 886 - 886

Published: April 4, 2025

Introduction: AI-driven training and HR analytics have revolutionized employee development by offering personalized learning experiences optimizing skill enhancement. Public institutions are increasingly leveraging AI-based recommendations adaptive algorithms to improve workforce training. However, the effectiveness challenges of these approaches in real-world applications require further investigation.Methods: This study employed a descriptive analytical research design, utilizing both quantitative qualitative methods. Data was collected from 385 employees Jordanian public using structured surveys sentiment analysis feedback. Statistical techniques, including regression analysis, ANOVA, correlation were applied assess impact data analytics, recommendations, personalization on effectiveness.Results: The findings indicate that significantly effectiveness. Skill emerged as strongest predictor success (β = 0.7282, p < 0.001). Sentiment revealed 82% responded positively training, while 10% expressed concerns about content relevance interactivity. ANOVA results confirmed no significant differences across job roles, indicating equitable experiences.Conclusion: AI-powered is widely accepted but requires refinement address engagement concerns. Organizations should adopt hybrid approach, integrating with instructor-led guidance. Future explore long-term impacts performance organizational enhance digital strategies.

Language: Английский

Citations

0
Personalized Instructional Strategy Adaptation Using TOPSIS: A Multi-Criteria Decision-Making Approach for Adaptive Learning Systems DOI Creative Commons
Christos Troussas, Akrivi Krouska, Phivos Mylonas

et al.

Information, Journal Year: 2025, Volume and Issue: 16(5), P. 409 - 409

Published: May 15, 2025

The growing number of educational technologies presents possibilities and challenges for personalized instruction. This paper a learner-centered decision support system selecting adaptive instructional strategies, that embeds the Technique Order Preference by Similarity to Ideal Solution (TOPSIS) in real-time learning environment. uses multi-dimensional learner performance data, such as error rate, time-on-task, mastery level, motivation, dynamically analyze recommend best pedagogical intervention from pool which includes hints, code examples, reflection prompts, targeted scaffolding. In developing system, we chose employ it one-off postgraduate Java programming course, this represents defined cognitive load structure samples spectrum learners. A robust evaluation was conducted with 100 students an compared static/no control condition. TOPSIS yielded statistically higher outcomes (normalized gain g = 0.49), behavioral engagement (28.3% increase tasks attempted), satisfaction. total 85.3% expert evaluators agreed decisions lecturer’s preferred teaching response towards prescribed problems behaviors. comparison rule-based approach, clear framework provided more granular effective adaptation. findings validate use multi-criteria decision-making underscore transparency, flexibility, potential proposed across broader domains.

Language: Английский

Citations

0