Student Information System for Computer Studies with Integrated Programming Anxiety Level Prediction and Balanced Group Recommendations DOI

Eduardo R. Yu,

Elmerito D. Pindea,

Isagani Tano

и другие.

International Journal of Latest Technology in Engineering Management & Applied Science, Год журнала: 2025, Номер 14(3), С. 572 - 579

Опубликована: Апрель 21, 2025

Abstract— Programming anxiety is a recognized challenge in computer studies, often affecting students’ academic performance and retention. Addressing this issue requires structured technology-driven approach that enables faculty to identify at-risk students implement targeted interventions. This study aimed provide solution by developing web-based system integrates predictive analytics support decision-making. Specifically, it incorporated pre-developed machine learning-based prediction model, automate student group formation using custom heterogeneous algorithm, featured data visualization dashboard for analysis. The was developed the Spiral Model ensure iterative improvements evaluated based on ISO/IEC 25010 Software Quality Model, focusing key software quality attributes. Expert evaluation of system’s resulted grand mean score 3.70, indicating strong across all metrics. findings demonstrate effectively assist higher education institutions addressing programming anxiety. By enabling real-time identification facilitating support, contributes fields educational technology learning analytics, offering scalable improving outcomes computing education.

Язык: Английский

Technology-driven support: exploring the impact of artificial intelligence on mental health in higher education DOI
Shaoguo Zhai, Shuang Zhang, Yi Rong

и другие.

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Янв. 24, 2025

Язык: Английский

Процитировано

1
Machine Learning for Combating Mental Health Stigma DOI
Ankur Kumar, Abhinav Sharma, Sanjay Dhanka

и другие.

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 333 - 366

Опубликована: Янв. 3, 2025

Stigma around mental health hinders care through discrimination, misconceptions, and shame. Machine learning (ML) offers data-driven solutions to address this, using sentiment analysis NLP analyze public attitudes, identify stigmatizing language, guide awareness campaigns. In healthcare, ML reduces biases, enhances patient interactions, fosters inclusivity. Personalized education via AI combats misinformation, while advocacy campaigns leverage assess impact. By addressing ethical concerns like bias privacy, can transform stigma reduction.

Язык: Английский

Процитировано

0
Student Information System for Computer Studies with Integrated Programming Anxiety Level Prediction and Balanced Group Recommendations DOI

Eduardo R. Yu,

Elmerito D. Pindea,

Isagani Tano

и другие.

International Journal of Latest Technology in Engineering Management & Applied Science, Год журнала: 2025, Номер 14(3), С. 572 - 579

Опубликована: Апрель 21, 2025

Abstract— Programming anxiety is a recognized challenge in computer studies, often affecting students’ academic performance and retention. Addressing this issue requires structured technology-driven approach that enables faculty to identify at-risk students implement targeted interventions. This study aimed provide solution by developing web-based system integrates predictive analytics support decision-making. Specifically, it incorporated pre-developed machine learning-based prediction model, automate student group formation using custom heterogeneous algorithm, featured data visualization dashboard for analysis. The was developed the Spiral Model ensure iterative improvements evaluated based on ISO/IEC 25010 Software Quality Model, focusing key software quality attributes. Expert evaluation of system’s resulted grand mean score 3.70, indicating strong across all metrics. findings demonstrate effectively assist higher education institutions addressing programming anxiety. By enabling real-time identification facilitating support, contributes fields educational technology learning analytics, offering scalable improving outcomes computing education.

Язык: Английский

Процитировано

0