Abstract
While computer-based differentiation is increasingly common in education, no actual evidence on the effects on the learning process is established yet. This study investigates the effect of data-driven differentiation on students' learning activity, and its relation with obtained summative grades. This study takes place over the course of one school year, in the context of the lower grades of secondary education and the courses biology, economics, and history. Students were randomly assigned to data-driven differentiation within an existing digital learning environment. Analyses were disaggregated into quartiles of students average achievement level and based on a longitudinal hierarchical regression model (N = 606), yielding the proportion of variance between and within students (over time). Results suggest that datadriven differentiation positively affects learning activity amongst certain - mostly high-achieving - students. Future research is required in order to fully explain these results and optimise datadriven differentiation in education.
Translated title of the contribution | Does a student practice more by level differentiation? The effect of data driven differentiation on learning effort and the role of previously obtained figures |
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Original language | Dutch |
Pages (from-to) | 182-195 |
Number of pages | 14 |
Journal | Pedagogische Studiën |
Volume | 94 |
Issue number | 3 |
Publication status | Published - 2017 |
Externally published | Yes |
Keywords
- Data-driven differentiation
- adaptive
- practice software
- learning activity
- summative assessment
- field experiment
- ACHIEVEMENT
- INSTRUCTION
- EFFICACY
- BELIEFS