Understanding self-regulation strategies in problem-based learning through dispositional learning analytics
Bibliographic Data
| ID | 22168282 |
|---|---|
| Authors | Dirk Tempelaar (0000-0001-8156-4614, Maastricht University, corresponding author), Anikó Bátori (0000-0001-9451-9556, Maastricht University), Bas Giesbers (0000-0002-8077-9039, Maastricht University) |
| Year | 2024 |
| Volume | 9 |
| Publication date | 2024-06-05 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2024.1382771 |
| OpenAlex | W4399356069 |
| Language | EN |
| Citations received | 1 |
| References cited | 41 |
In the ongoing discussion about how learning analytics can effectively support self-regulated student learning and which types of data are most suitable for this purpose, this empirical study aligns with the framework who advocated the inclusion of both behavioral trace data and survey data in learning analytics studies. By incorporating learning dispositions in our learning analytics modeling, this research aims to investigate and understand how students engage with learning tasks, tools, and materials in their academic endeavors. This is achieved by analyzing trace data, which captures digital footprints of students’ interactions with digital tools, along with survey responses from the Study of Learning Questionnaire (SLQ), to comprehensively examine their preferred learning strategies. Additionally, the study explores the relationship between these strategies and students’ learning dispositions measured at the start of the course. An innovative aspect of this investigation lies in its emphasis on understanding how learning dispositions act as antecedents and potentially predict the utilization of specific learning strategies. The data is scrutinized to identify patterns and clusters of such patterns between students’ learning disposition and their preferred strategies. Data is gathered from two cohorts of students, comprising 2,400 first year students. This analytical approach aims to uncover predictive insights, offering potential indicators to predict and understand students’ learning strategy preferences, which holds value for teachers, educational scientists, and educational designers. Understanding students’ regulation of their own learning process holds promise to recognize students with less beneficial learning strategies and target interventions aimed to improve these. A crucial takeaway from our research underscores the significance of flexibility, which entails the ability to adjust preferred learning strategies according to the learning environment. While it is imperative to instruct our students in deep learning strategies and encourage autonomous regulation of learning, this should not come at the expense of acknowledging situations where surface strategies and controlled regulation may prove to be more effective
Analytics · Data science · Learning analytics · Machine learning · Self-regulated learning · Computer Science · Innovative Teaching and Learning Methods · Online and Blended Learning · Online Learning and Analytics · Psychology · Social Psychology · Artificial Intelligence
Metacognitive, cognitive and affective aspects of learning styles and strategies
Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning
Problem-Based Learning
The current landscape of learning analytics in higher education
Academic control and action control in the achievement of college students
Improving Students’ Learning With Effective Learning Techniques
A Systematic Review of Empirical Studies on Learning Analytics Dashboards
Factors that influence teachers’ adoption and integration of ICT in teaching/learning process
Measuring emotions in students’ learning and performance
Deep and surface learning in problem-based learning
Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation
Becoming a self-regulated learner
Digital technologies, innovation, and skills
A Review of Self-regulated Learning
The Control-Value Theory of Achievement Emotions
Dispositional Learning Analytics for Supporting Individualized Learning Feedback
The scalable implementation of predictive learning analytics at a distance learning university
Investigating students' use of self‐assessments in higher education using learning analytics
Students' self‐report and observed learning orientations in blended university course design
Students’ approaches to learning in problem-based learning
The Academic Motivation Scale
Let’s not forget
Examining a multidimensional model of student motivation and engagement using a construct validation approach
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |