S H E Dijkstra
Biographic Data
| ID | 9623310 |
|---|---|
| NAME | S H E Dijkstra |
| GIVEN NAMES | S H E |
| FAMILY NAME | Dijkstra |
| SIGNATURE | DIJKSTRA S H E |
| AFFILIATIONS | Radboud University Nijmegen |
| ORCID | 0000-0003-3688-5456 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
How does co-regulation with Adaptive Learning Technologies affect primary school students' goal-setting, regulation of practice behavior and learning outcomes
Introduction Monitoring and controlling learning is often difficult for primary school students. This issue is partially resolved when Adaptive Learning Technologies (ALTs) take over part of these self-regulated learning (SRL) processes. Trace data in ALTs provides elaborate information on students' learning process, which can be translated into monitoring support. However, this data does not provide insight into students' goal-setting behavior, …
Enacting control with student dashboards: The role of motivation
Clustering children's learning behaviour to identify self-regulated learning support needs
When children are learning using adaptive learning technologies (ALTs), the technology builds a learner model, which creates temporal trajectories providing insight into how children's knowledge develops. Based on this learner model, ALTs adjust the difficulty of problems for each child, yet children still need to regulate their practice behaviour and uphold effort and accuracy. The temporal trajectories are consequently likely to, besides showin…
No prominent works on this page.
Clustering children's learning behaviour to identify self-regulated learning support needs
When children are learning using adaptive learning technologies (ALTs), the technology builds a learner model, which creates temporal trajectories providing insight into how children's knowledge develops. Based on this learner model, ALTs adjust the difficulty of problems for each child, yet children still need to regulate their practice behaviour and uphold effort and accuracy. The temporal trajectories are consequently likely to, besides showin…
How does co-regulation with Adaptive Learning Technologies affect primary school students' goal-setting, regulation of practice behavior and learning outcomes
Introduction Monitoring and controlling learning is often difficult for primary school students. This issue is partially resolved when Adaptive Learning Technologies (ALTs) take over part of these self-regulated learning (SRL) processes. Trace data in ALTs provides elaborate information on students' learning process, which can be translated into monitoring support. However, this data does not provide insight into students' goal-setting behavior, …
Enacting control with student dashboards: The role of motivation
Artificial Intelligence (3 works) · Computer Science (3 works) · Innovative Teaching and Learning Methods (3 works) · Psychology (3 works) · Knowledge management (2 works) · Online Learning and Analytics (2 works) · Adaptive Learning (1 works) · Cluster analysis (1 works) · Educational and Psychological Assessments (1 works) · Educational Games and Gamification (1 works)