Acumenous Game-Based Learning in Simulation Games and Applied Statistics

Slyman, Souad. 2022. Acumenous Game-Based Learning in Simulation Games and Applied Statistics. Doctoral thesis, Goldsmiths, University of London [Thesis]

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Abstract or Description

This thesis is concerned with how mathematical/ applied statistical learning can be addressed using games. Advocates of computer game-based learning argue that games have the potential to increase self-efficacy, motivation, and engagement in Mathematics/ Applied Statistics. The research described grounded in the fields of computing, human-computer interaction and game design, and education, questions this assumption and considers the case for game-based learning in Higher Education. In total, 120 participants took part in this research from 2016-19. Empirical data were collected using qualitative, quantitative, and mixed research methods and there were four studies. My findings identify two main types of game-based learning which Itermed ‘Acumenous’ and ‘Inquisitional.’ ‘Acumenous Game-Based Learning’ (AGBL) is a type of GBL within a new proposed ‘Acumenous’ and ‘Inquisitional’ Theory. Acumenous refers to the ability to understand, reason and decide whilst Inquisitional refers to question-answer formats type of learning, a factual recall or a ‘quiz type’ of learning. Findings also indicate game design issues that stimulate players’ discourse of how they portray their gaming experience, i.e., lack of usability heuristics and HCI (Human Computer Interaction) principles. These informed the development of a Game Analysis, Design, Development, Implementation and Evaluation (GADDIE) Model, and all subsequent areas of work. Findings suggest that the investigated Mathematics Educational Games (MEGs) are disengaging due to preconceptions towards mathematical learning, i.e., lack of self-efficacy in these games. Findings also indicate that AGBL can significantly address students’ understanding of Mathematics/ AppliedStatistics, and their self-efficacy increased. Implications and recommendations for policy and practice are discussed.

Item Type:

Thesis (Doctoral)

Identification Number (DOI):


Simulation games, applied statistics, mathematics, preconceptions, game design, grounded theory, game-based learning

Departments, Centres and Research Units:



31 January 2022

Item ID:


Date Deposited:

22 Feb 2022 12:22

Last Modified:

07 Sep 2022 17:19


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