Armani, Federica (2024) Physiological correlates of Math Anxiety for neuroadaptive learning systems. Doctoral thesis, University of Essex.
Armani, Federica (2024) Physiological correlates of Math Anxiety for neuroadaptive learning systems. Doctoral thesis, University of Essex.
Armani, Federica (2024) Physiological correlates of Math Anxiety for neuroadaptive learning systems. Doctoral thesis, University of Essex.
Abstract
Mathematical competence is important to acquire for everyday and professional purposes, but often represents a considerable hurdle for students, who may associate it with unpleasant experiences. Such unpleasant feelings can lead to a state of Math Anxiety, which impacts the life and well being of many people. The initial part of this manuscript will describe the problem of Math Anxiety, discussing its development, maintenance, effects on the mindset and performance of students and investigate available solutions applied both in family and school settings. The second part will focus on describing Brain Computer Interfaces (BCI), what they are, how they work and their role as a technology to improve mathematical understanding in students. The rest of the manuscript focuses on the research I performed to investigate the problem of Math Anxiety using a combination of neuroscience and neural engineering. The first experiment presented focuses on aiding teachers by investigating the optimal way in which some learning content has to be presented to students with Math Anxiety, in order to increase their final performance. The second experiment focuses on a multitude of cognitive states arising during a learning experience and represents the first example of investigation monitoring multiple cognitive states and Math Anxiety at the same time. EEG patterns found suggest that the combination of workload, attention, fatigue and changes in gamma band-power can be useful to identify both meaningful cognitive states and identify students with Math Anxiety.
Item Type: | Thesis (Doctoral) |
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Subjects: | L Education > L Education (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
Depositing User: | Federica Armani |
Date Deposited: | 25 Sep 2024 14:28 |
Last Modified: | 25 Sep 2024 14:28 |
URI: | http://repository.essex.ac.uk/id/eprint/39256 |