Factors influencing student engagement and behavioral differences based on K-means cluster analysis DOI
Wei Li,

Zhentong Xue

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2024, Volume and Issue: 25(2), P. 1835 - 1844

Published: Dec. 2, 2024

Student engagement is a crucial predictor of learning outcomes. The study constructed measurement indicators primarily based on five dimensions: beliefs, behavioral tendencies, emotional attitudes, self-efficacy in ability, and behavior. It collected questionnaire data from 347 students who had been promoted junior college to undergraduate studies. survey were processed using correlation analysis, regression the K-means clustering algorithm. found that attitudes are significantly positively correlated with can predict student at an 82% level; population mainly divided into three types: proactive explorers, passive obstacles, general passives, proportions 32.56%, 26.51%, 40.92%, respectively; different types groups show significant differences ( p < 0.05), explorers having highest level engagement, passives middle, obstacles lowest. To enhance it suggested teachers should pay attention fostering correct students, take measures students’ learning, adhere educational concept personalized teaching throughout process, implementing differentiated strategies such as layered instruction more effectively meet individualized needs.

Language: Английский

How AI‐Enhanced Social–Emotional Learning Framework Transforms EFL Students' Engagement and Emotional Well‐Being DOI Open Access

Yue Zong,

Lei Yang

European Journal of Education, Journal Year: 2025, Volume and Issue: 60(1)

Published: Jan. 12, 2025

ABSTRACT This study explores the transformative role of AI‐enhanced social–emotional learning (SEL) frameworks in improving engagement and emotional well‐being English as a foreign language (EFL) students China. A survey was conducted among 816 undergraduate postgraduate from universities across five provinces, utilising convenience sampling. The research focused on how AI tools integrated into contribute to student stability. Data were analysed using SPSS for descriptive regression analyses AMOS structural equation modelling. findings highlight that SEL significantly boosts well‐being. By providing tailored experiences based students' cognitive needs, systems facilitate better regulation, increased focus improved academic performance. results suggest offer personalised support not only enhances outcomes but also creates more emotionally supportive environment, contributing overall success

Language: Английский

Citations

2
Analyzing factors influencing students’ decisions to adopt smart classrooms in higher education DOI
Long Kim,

Rungrawee Jitpakdee,

Wasin Praditsilp

et al.

Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

Language: Английский

Citations

0
The effects of self-efficacy, teacher support, and positive academic emotions on student engagement in online courses among EFL university students DOI
Oqab Alrashidi, Sultan Hammad Alshammarı

Education and Information Technologies, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 11, 2024

Language: Английский

Citations

3
Does Technological Innovation Matter to Smart Classroom Adoption? Implications of Technology Readiness and Ease of Use DOI Creative Commons
Long Kim,

Rungrawee Jitpakdee,

Wasin Praditsilp

et al.

Journal of Open Innovation Technology Market and Complexity, Journal Year: 2024, Volume and Issue: unknown, P. 100448 - 100448

Published: Dec. 1, 2024

Language: Английский

Citations

2
Construction of a Multimedia Instructional Effect Assessment System of College Foreign Language Translation Based on Artificial Intelligence DOI Open Access
Shuang Liu, Man Yi

International Journal of Information Systems and Supply Chain Management, Journal Year: 2024, Volume and Issue: 17(1), P. 1 - 20

Published: May 22, 2024

The teaching evaluation module is an important guarantee of effectiveness, and as part the multimedia system, it needs special attention. This paper combines artificial intelligence effect evaluation, proposes a foreign language translation model based on improved support vector machine (SVM) algorithm, provides theoretical for construction system. results show that accuracy this assessment algorithm by 22.18% compared with traditional algorithm. Therefore, theoretically feasible to use SVM analyse application mode in teaching. It can be found automatic acquisition data, cumulative search spatial knowledge adaptive control phase optimised have also been significantly improved, conclusions drawn are more reliable.

Language: Английский

Citations

0
Good stress or bad stress? An empirical study on the impact of time pressure on doctoral students’ innovative behavior DOI Creative Commons
Xin Zhang, Zhixing Zhao, Jie Sun

et al.

Frontiers in Psychology, Journal Year: 2024, Volume and Issue: 15

Published: Nov. 8, 2024

In recent years, with rapid societal advancement and profound transformations in knowledge production, doctoral students are increasingly facing significant time pressures. These pressures not only stem from an escalation research tasks but also urgent demands for innovative outputs. Grounded Affective Events Theory, this study explores the dual impact of pressure on behaviors China. It specifically examines how challenge hindrance affect students' behavior through mediating role self-efficacy moderating supervisor support. This employed SPSS 26.0 Mplus 8.3 statistical analysis, analyzing multi-time point data collected 452 Chinese between May August 2023. The results reveal that significantly positively impacts behavior, while has a negative impact. Furthermore, partially mediates relationship both behavior. process, support is significant, enhancing positive effects mitigating pressure, highlighting importance optimizing promoting findings enrich theoretical framework field provide practical guidance universities supervisors to effectively managing fostering their innovation.

Language: Английский

Citations

0
Factors influencing student engagement and behavioral differences based on K-means cluster analysis DOI
Wei Li,

Zhentong Xue

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2024, Volume and Issue: 25(2), P. 1835 - 1844

Published: Dec. 2, 2024

Student engagement is a crucial predictor of learning outcomes. The study constructed measurement indicators primarily based on five dimensions: beliefs, behavioral tendencies, emotional attitudes, self-efficacy in ability, and behavior. It collected questionnaire data from 347 students who had been promoted junior college to undergraduate studies. survey were processed using correlation analysis, regression the K-means clustering algorithm. found that attitudes are significantly positively correlated with can predict student at an 82% level; population mainly divided into three types: proactive explorers, passive obstacles, general passives, proportions 32.56%, 26.51%, 40.92%, respectively; different types groups show significant differences ( p < 0.05), explorers having highest level engagement, passives middle, obstacles lowest. To enhance it suggested teachers should pay attention fostering correct students, take measures students’ learning, adhere educational concept personalized teaching throughout process, implementing differentiated strategies such as layered instruction more effectively meet individualized needs.

Language: Английский

Citations

0