Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners DOI Creative Commons
Fawad Naseer, Sarwar Khawaja

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4473 - 4473

Опубликована: Апрель 18, 2025

Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive in mitigating these gaps by providing personalized learning support struggling learners. A analytics-based evaluation was conducted on 700 undergraduate students enrolled STEM-related courses across three different departments at Beaconhouse International College (BIC). employed a quasi-experimental design, where 350 received while control group followed traditional instructor-led methods. Data were collected over 20 weeks, utilizing pre- and post-assessments, real-time engagement tracking, survey responses. Results indicate that receiving demonstrated 28% improvement conceptual mastery, compared 14% group. Additionally, student increased 35%, with 22% reduction cognitive overload. Analysis interaction logs revealed frequent AI-generated led 40% increase retention rates. Despite benefits, variations impact observed based prior knowledge levels consistency. findings highlight potential smart environments enhance educational equity. Future research should explore long-term effects, scalability, ethical considerations AI-based systems.

Язык: Английский

The mediating influence of self-efficacy and self-regulation in the relationship between perfectionism and listening anxiety DOI Creative Commons
Elham Movafagh Ardestani, Hamed Barjesteh, Mahmood Dehqan

и другие.

Frontiers in Education, Год журнала: 2025, Номер 10

Опубликована: Фев. 14, 2025

Introduction This study investigates the complex relationship between perfectionism and Listening Anxiety (LA) in context of English as a Foreign Language (EFL), with focus on mediating roles Self-Efficacy (LS) Self-Regulated Learning (SRL). Method A sample 350 EFL learners from various language institutes Iran was selected through cluster random sampling completed four validated questionnaires measuring SRL, LS, perfectionism, LA. Structural Equation Modeling (SEM) employed to analyze data provide detailed insights into interrelationships among these variables. Results The results revealed significant linear relationships variables under study. Specifically, SRL were found directly influence LA, LS serving stronger predictors LA than perfectionism. In terms predictive power, ranked just behind LS. Discussion Both mediators suggesting that learners’ self-regulation listening self-efficacy play key how affects findings have important implications for instruction: by prioritizing educators can create more supportive, enjoyable, effective learning environment students, ultimately reducing

Язык: Английский

Процитировано

0
A qualitative descriptive analysis on generative artificial intelligence: bridging the gap in pedagogy to prepare students for the workplace DOI Creative Commons
Andrea L. Irish, Michele W. Gazica,

Vega Becerra

и другие.

Discover Education, Год журнала: 2025, Номер 4(1)

Опубликована: Март 3, 2025

Язык: Английский

Процитировано

0
The Impact of Generative AI on Essay Revisions and Student Engagement DOI Creative Commons
Noble Lo,

Alan Wong,

S. T. Chan

и другие.

Computers and Education Open, Год журнала: 2025, Номер unknown, С. 100249 - 100249

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Generative artificial intelligence in pedagogical practices: a systematic review of empirical studies (2022–2024) DOI Creative Commons
Wang Xiaoyu, Zamzami Zainuddin,

Chin Hai Leng

и другие.

Cogent Education, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0
Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners DOI Creative Commons
Fawad Naseer, Sarwar Khawaja

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4473 - 4473

Опубликована: Апрель 18, 2025

Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive in mitigating these gaps by providing personalized learning support struggling learners. A analytics-based evaluation was conducted on 700 undergraduate students enrolled STEM-related courses across three different departments at Beaconhouse International College (BIC). employed a quasi-experimental design, where 350 received while control group followed traditional instructor-led methods. Data were collected over 20 weeks, utilizing pre- and post-assessments, real-time engagement tracking, survey responses. Results indicate that receiving demonstrated 28% improvement conceptual mastery, compared 14% group. Additionally, student increased 35%, with 22% reduction cognitive overload. Analysis interaction logs revealed frequent AI-generated led 40% increase retention rates. Despite benefits, variations impact observed based prior knowledge levels consistency. findings highlight potential smart environments enhance educational equity. Future research should explore long-term effects, scalability, ethical considerations AI-based systems.

Язык: Английский

Процитировано

0