Self-regulated learner profiles in Moocs
A cluster analysis based on the item response theory
Bibliographic Data
| ID | 21632614 |
|---|---|
| Authors | Hengtao Tang (0000-0002-8846-7654, University of South Carolina, corresponding author), Yu Bao (0000-0002-6245-5356, James Madison University) |
| Year | 2022 |
| Pages | 1-17 |
| Publication date | 2022-10-10 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2022.2129394 |
| OpenAlex | W4304143218 |
| Language | EN |
| Citations received | 7 |
| References cited | 50 |
Self-regulated learning is a crucial skill that may enable massive numbers of learners to thrive in MOOCs, but MOOC learners differ in their self-regulated learning skills, as low self-regulated learners need support to regulate their learning process in MOOCs. Designing self-regulated learning scaffoldings builds upon an accurate appraisal of learners’ self-regulated learning and also a person-centered understanding of self-regulated learner profiles in MOOCs. Therefore, this study applied a two-parameter item response theory model to accurately evaluate online learners’ self-regulated learning in MOOCs and then performed a cluster analysis to identify learner profiles in terms of their traits in each phase of self-regulated learning. The findings of this study identified five clusters of self-regulated learner profiles, upon which a further statistical analysis result indicated that the learners with lower forethought skills might be less committed to completing course assignments. Implications for self-regulated learning in MOOCs are discussed at the end
Educational technology · Mathematics education · Self-regulated learning · Computer Science · Innovative Teaching and Learning Methods · Online and Blended Learning · Online Learning and Analytics · Psychology
Identifying university students’ online self-regulated learning profiles
Unified Mooc utilization model (Umum) for exploring computer-supported learning with educational technology in terms of Mooc utilization in higher education
Motivation in online course design using self-determination theory
Investigating students’ performance at self-regulated learning and its effects on learning outcomes in chemistry class at the senior secondary school
Using an avatar-based digital collaboration platform to foster ethical education for university students
Analysis and discovery of procrastination patterns in a language learning Mooc
Self-regulated learning and video annotation in a high school acting classroom
Item Response Theory
Statistical Approaches to Measurement Invariance
Further Investigation of the Performance of S - X2
Learning in Moocs
A Short Version of the Occupational Self-Efficacy Scale
Assessing Metacognitive Awareness
Measuring self-regulation in online and blended learning environments
The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence
Mirt
Limited Information Goodness-of-fit Testing in Multidimensional Contingency Tables
Profiles in self-regulated learning in the online learning environment
NbClust
Self-Regulation and Academic Learning
Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses
The Explanatory and Predictive Scope of Self-Efficacy Theory
Attaining Self-Regulation
A Reliability Coefficient for Maximum Likelihood Factor Analysis
The Role of Goal Orientation in Self-Regulated Learning
Motivational and self-regulated learning components of classroom academic performance.
Expectancy–Value Theory of Achievement Motivation
Person-centered analysis of self-regulated learner profiles in Moocs
Understanding higher education learners' acceptance and use of mobile devices for language learning
Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced Moocs
Analysing self-regulated learning strategies of Mooc learners through self-reported data
Comparative fit indexes in structural models
Learning strategies, learning anxiety and knowledge acquisition
Examining students' self-set goals for self-regulated learning
| Unique citing works | 7 |
|---|---|
| Citations per year | 2.33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |