A profile analysis of computer-supported collaborative learning motivation in Chinese higher education: integrating achievement goal and expectancy-value perspectives

被引:0
作者
Wang, Kai [1 ]
Duan, Boyuan [2 ]
Li, Xian [3 ]
Xue, Yuan [4 ]
机构
[1] Beijing Normal Univ, Fac Educ, Ctr Teacher Educ, Beijing, Peoples R China
[2] Renmin Univ China, Sch Educ, Beijing, Peoples R China
[3] Beijing Jiaotong Univ, Sch Languages & Commun Studies, Beijing, Peoples R China
[4] Vrije Univ Brussel, Fac Law & Criminol, Brussels, Belgium
基金
中国国家自然科学基金;
关键词
Computer-supported collaborative learning; motivation; self-regulated learning; latent profile analysis; college students; ACADEMIC-ACHIEVEMENT; STUDENT MOTIVATION; SELF; STRATEGIES; METACOGNITION; PERFORMANCE; BEHAVIOR;
D O I
10.1080/10494820.2025.2521342
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The rise of digital technology has significantly reshaped educational practices, making self-regulated learning (SRL) critical for success in computer-supported collaborative learning environments. This study employed latent profile analysis to explore whether students with different motivation profiles exhibit variations in SRL. Drawing on achievement goal theory and expectancy-value theory, the analysis focused on five motivational variables: mastery approach, performance approach, work avoidance, expectancy belief, and value belief. The study took place with 539 Chinese college students (56.8% women) to identify homogeneous latent motivation profiles. Four distinct profiles were identified: (1) Amotivated, (2) Goal-driven but doubtful, (3) Value-oriented/Goal-reserved, and (4) Comprehensively motivated. Furthermore, BCH analysis revealed significant variations in SRL behaviors across profiles. Students in the Comprehensively motivated group displayed the strongest SRL behaviors across all dimensions, while those in the Amotivated group exhibited the weakest performance. Students in the Goal-driven but doubtful and Value-oriented/Goal-reserved groups demonstrated overall average SRL abilities but minor differences in specific SRL behaviors. These results provide a nuanced understanding of how motivation profiles influence student behaviors and outcomes in CSCL environments. Insights from the study may help educators tailor their instructional practices and support strategies to enhance student engagement and learning efficacy.
引用
收藏
页数:23
相关论文
共 102 条
[61]  
MAEHR ML, 1991, EDUC PSYCHOL, V26, P399
[62]   Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning [J].
Malmberg, Jonna ;
Jarvela, Sanna ;
Jarvenoja, Hanna .
CONTEMPORARY EDUCATIONAL PSYCHOLOGY, 2017, 49 :160-174
[63]   Promoting socially shared regulation of learning in CSCL: Progress of socially shared regulation among high- and low-performing groups [J].
Malmberg, Jonna ;
Jarvela, Sanna ;
Jarvenoja, Hanna ;
Panadero, Ernesto .
COMPUTERS IN HUMAN BEHAVIOR, 2015, 52 :562-572
[64]   Classroom goal structure, student motivation, and academic achievement [J].
Meece, JL ;
Anderman, EM ;
Anderman, LH .
ANNUAL REVIEW OF PSYCHOLOGY, 2006, 57 :487-503
[65]   The social contagion of work avoidance goals in school and its influence on student (dis)engagement [J].
Mendoza, Norman B. ;
King, Ronnel B. .
EUROPEAN JOURNAL OF PSYCHOLOGY OF EDUCATION, 2022, 37 (02) :325-340
[66]   Expectancy value interactions and academic achievement: Differential relationships with achievement measures [J].
Meyer, Jennifer ;
Fleckenstein, Johanna ;
Koeller, Olaf .
CONTEMPORARY EDUCATIONAL PSYCHOLOGY, 2019, 58 :58-74
[67]   Singapore Primary Students' Pursuit of Multiple Achievement Goals: A Latent Profile Analysis [J].
Ning, Hoi Kwan .
JOURNAL OF EARLY ADOLESCENCE, 2018, 38 (02) :220-237
[68]   Deciding on the number of classes in latent class analysis and growth mixture modeling:: A Monte Carlo simulation study [J].
Nylund, Karen L. ;
Asparoutiov, Tihomir ;
Muthen, Bengt O. .
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2007, 14 (04) :535-569
[69]   Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models [J].
Nylund-Gibson, Karen ;
Grimm, Ryan P. ;
Masyn, Katherine E. .
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2019, 26 (06) :967-985
[70]  
O'Malley C., 2012, Computer supported collaborative learning, V128