Iancu, Stefania Denisa (2022) Towards reducing mathematical anxiety through adaptive learning. Master thesis, Psychology.
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Abstract
Adaptive learning algorithms have gained popularity in educational contexts to effectively improve declarative fact learning. Such systems can improve learning by determining individual schedules of facts repetition, based on individual parameters, such as reaction time and accuracy. While studying declarative facts with an adaptive algorithm has been repeatedly proven to outperform less adaptive systems when using these performance parameters, it has never been examined when learning procedural knowledge, such as mathematical multiplications. Here, we are particularly interested in individuals with mathematical anxiety, their association with lower performance on mathematical tasks, and whether using adaptive methods might minimize their perceived mathematical anxiety. Our results indicate that differences between the two algorithms are small, with a slight advantage in accuracy and reaction time on test with the non-adaptive one. However, the adaptive algorithm shows a slight improvement in reaction time on learning. Additionally, our results point out that individuals with higher scores on mathematical anxiety tend to forget slightly faster when learning new facts. Contrary to previous results, we did not observe any association between performance parameters and mathematical anxiety. The current results help us better understand the differences in memory retention between declarative and procedural facts, and possible links between poor performance in math-related tasks and anxiety.
| Item Type: | Thesis (Master) |
|---|---|
| Supervisor name: | Wilschut, T.J. |
| Degree programme: | Psychology |
| Differentiation route: | Cognitive Psychology and Psychophysiology (CPP) [Master Psychology] |
| Date Deposited: | 01 Sep 2023 09:50 |
| Last Modified: | 01 Sep 2023 09:50 |
| URI: | http://gmwpublic.studenttheses.ub.rug.nl/id/eprint/2855 |
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