Evaluation of User Experience, Cognitive Load, and Training Performance of a Gamified Cognitive Training Application for Children With Learning Disabilities

Shaban, Adel and Pearson, Elaine and Chang, Victor (2021) Evaluation of User Experience, Cognitive Load, and Training Performance of a Gamified Cognitive Training Application for Children With Learning Disabilities. Frontiers in Computer Science, 3. ISSN 2624-9898

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Abstract

This study presents a gamified application for children with learning disabilities, designed to train and improve their working memory capacity. The application takes the form of a treasure hunt and is designed according to a framework incorporating a set of guidelines derived from accessibility, usability, and cognitive load theory principles, and from gamification techniques. The aim is to motivate and engage the children in working memory-training activities and exploit their working memory capacity. The main focus of this study is the evaluation of the cognitive load level induced by the application, the children’s perceived experience, and their training performance over the training period. A sample of 12 Egyptian children with learning disabilities completed a five-week training period using the application, followed by an evaluation process. The evaluation took the form of a simple usability survey, an unstructured observation, and a cognitive load measurement scale. The purpose was to evaluate the children’s perceived experience, assess the level of cognitive load experienced in each of the activities, and measure the expected improvement in the children’s training performance. The results revealed that all the children enjoyed playing the gamified application, were eager to participate in the daily training, and the cognitive load experienced during the training was found to be generally appropriate, although some areas for improvement were identified. Finally, the children’s training performance and their perceived experience were better in the gamified activities with a lower cognitive load level.

Item Type: Article
Subjects: STM Repository > Computer Science
Depositing User: Managing Editor
Date Deposited: 18 Feb 2023 12:11
Last Modified: 24 Jul 2024 09:04
URI: http://classical.goforpromo.com/id/eprint/1810

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