USING SERIOUS GAMES DESIGNED THROUGH THE GAME ELC+ FRAMEWORK TO ENHANCE DEEP LEARNING IN HUMAN RESOURCES DEVELOPMENT

Gemade, Mamfe-ter 2022. USING SERIOUS GAMES DESIGNED THROUGH THE GAME ELC+ FRAMEWORK TO ENHANCE DEEP LEARNING IN HUMAN RESOURCES DEVELOPMENT. PhD thesis University of Westminster Computer Science and Engineering https://doi.org/10.34737/w0zvw

TitleUSING SERIOUS GAMES DESIGNED THROUGH THE GAME ELC+ FRAMEWORK TO ENHANCE DEEP LEARNING IN HUMAN RESOURCES DEVELOPMENT
TypePhD thesis
AuthorsGemade, Mamfe-ter
Abstract

The traditional method of learning has been widely criticised for its limitations and inflexibility to application in non-educational settings. These observations about the traditional modes of learning have necessitated the contemplation and discovery of new approaches embracing technological tools that advances better learning experiences. Hence, new technological innovations, such as Stronger Game or Serious Games (SGs) have been embraced as more effective methods of achieving deep learning. The application of serious game has indeed, gained traction in both the formal educational and human resource (HR) settings, especially for employees’ training and development. Thus, the core question of this PhD research is hinged on whether the SGs are more effective in creating deep learning in adult learners, compared to the more traditional teaching methods. To respond to this query, the study examines the traditional and SGs learning approaches, in order to ascertain which is more effective in creating deep learning in adults, in addition to achieving human resource training and development. To guide the design and development of SGs to support adult DL, this research proposes a pedagogical framework referred to as the Game ELC+ framework that comprises four learning theories namely: The Game (Elements) within the Yu Kai Chou's Octalysis Framework; Bloom Taxonomy’s Player (Learning) Levels; (Cognitive) Theory of Multimedia Learning; and the Ruskov’s four evidence of Deep Learning (+). This framework provides the standard for measuring DL in the design of SGs.

The research instruments developed include a traditional andragogical test which uses e-Learning materials containing ten different learning scenarios in the context of workplace HR scenarios, and a digital Serious Game using exactly the same content and scenarios with the traditional andragogical test.

ANOVA was utilized as the data analytical approach for comparing the mean score of learners using serious games and the tradition eLearning platforms. The study hypothesised that deep learning can be achieved through the SGs and that it is more effective than the traditional andragogy. It further asserts that participants who used the SGs achieved a higher learning outcome than participants in traditional process. Participant observation during the testing phase suggests that the participants interacting with the SGs demonstrated high level of engagement and curiosity, when compared to participants who used the traditional eLearning platform. The study findings validate the hypotheses. By implication, the SGs designed according to the Game ELC+ framework results in improved learning outcomes. In summary, the findings claim that incorporating SG elements in HR training and development can improve professional practices and mitigate some of the challenges experienced by human resource in the traditional learning environment.

Year2022
File
File Access Level
Open (open metadata and files)
ProjectUSING SERIOUS GAMES DESIGNED THROUGH THE GAME ELC+ FRAMEWORK TO ENHANCE DEEP LEARNING IN HUMAN RESOURCES DEVELOPMENT
PublisherUniversity of Westminster
Publication dates
Published2022
Digital Object Identifier (DOI)https://doi.org/10.34737/w0zvw

Related outputs

A methodology to evaluate the use of Serious Games in achieving Deep Learning: An Application for Andragogy in Human Resource Development
Gemade, M. and Mentzelopoulos, M. 2020. A methodology to evaluate the use of Serious Games in achieving Deep Learning: An Application for Andragogy in Human Resource Development. 6th International Conference of the Immersive Learning Research Network. OnLine 21 - 25 Jun 2020 IEEE . https://doi.org/10.23919/iLRN47897.2020.9155118

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