Cluster analysis identifies two antagonistic student profiles differing in level of technology-supported self-regulated learning¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2017 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2022: baselines and conditions change

Subclaims¶

q2 i? A high-SRL profile comprises 84 students (11.86%) with the greatest mean frequency of implementation in all factors, including low-use factors such as sharing information and monitoring and feedback. → Carmen Yot-Domínguez and Carlos Marcelo 2017 q2 i? A low-SRL profile comprises 238 students (33.62%) showing an intermediate level on superficial use and collaborative learning factors. → Carmen Yot-Domínguez and Carlos Marcelo 2017 (2)

Evidence¶

Carmen Yot-Domínguez and Carlos Marcelo 2017¶

Carmen Yot-Domínguez and Carlos Marcelo. (2017). University students' self-regulated learning using digital technologies. International Journal of Educational Technology in Higher Education 14:38. https://doi.org/10.1186/s41239-017-0076-8

q2 · i? · associational · r2

K-means cluster analysis (after hierarchical Ward-method clustering; three outliers removed) on factor use frequencies identified four conglomerates; the two most different are reported. High-profile students use instant messaging (M = 4.78), translators (M = 4.12) and multimedia search (M = 4.10) heavily.

"They represent 84 students, which is 11.86% of the respondents. Of these, 65.5% are female, and 59.5% are between 21 and 25 years of age while 17.9% are under 20. They stand out because they have the greatest mean frequency of implementation in all the factors"

Carmen Yot-Domínguez and Carlos Marcelo 2017 (2)¶

Carmen Yot-Domínguez and Carlos Marcelo. (2017). University students' self-regulated learning using digital technologies. International Journal of Educational Technology in Higher Education 14:38. https://doi.org/10.1186/s41239-017-0076-8

q2 · i? · associational · r2

The contrasting low-use cluster still uses instant messaging actively (M = 4.27) and Wikipedia frequently (M = 3.41), showing that even the lesser group uses some technology-based SRL strategies regularly.

"This group includes 238 students, which corresponds to 33.62% of the respondents. Of these, 62.2% are between 21 and 25 years of age. This group of students shows an intermediate level regarding the use of strategies for the superficial use of information and collaborative learning factors."

Discussion¶

Related Claims¶

• Social-support strategies are the most generalised technology-based SRL strategies, with most students interacting via instant messaging and sharing material via the cloud — related
• University students make limited use of digital technologies to self-regulate their learning despite being frequent technology users — a broader claim this one bears on
• Superficial information use with limited processing is the most used technology-based SRL strategy, while self-evaluation is among the least used — related
• Cluster analysis identifies three stable learner profiles: Engaged and Literate, Uncritical Reliant, and Skeptical Minimal users — related
• Exploratory K-means clustering identifies four heterogeneous student AI-user profiles differing in experience, competence, usefulness, trust, and control, but the solution's optimality and stability are not established — related
• The four profiles differ in academic self-efficacy, time/study management, and effort regulation, with HL highest, but not in help seeking — related
• Students' SRL profile under automated scoring was uneven, and only Execution-and-Monitoring was significantly associated with score gains — related
• Latent profile analysis of pre-service teachers' CT skills identifies three profiles (Novice, Developing, Proficient), revealing heterogeneous, non-linear skill acquisition — related
• SRL writing-stage clusters differed in production and revision but not in pausing — related
• A two-dimensional profiling method based on initial artifact quality and criterion-aligned revision categorizes student groups into four analytic profiles in an AI-supported RQ/hypothesis revision activity — related