Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756907
Title: Adaptive serious educational games using machine learning
Author: Ar Rosyid, Harits
ISNI:       0000 0004 7429 7644
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2018
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Abstract:
The ultimate goals of adaptive serious educational games (adaptive SEG) are to promote effective learning and maximising enjoyment for players. Firstly, we develop the SEG by combining knowledge space (learning materials) and game content space to be used to convey learning materials. We propose a novel approach that serves toward minimising experts' involvement in mapping learning materials to game content space. We categorise both content spaces using known procedures and apply BIRCH clustering algorithm to categorise the similarity of the game content. Then, we map both content spaces based on the statistical properties and/or by the knowledge learning handout. Secondly, we construct a predictive model by learning data sets constructed through a survey on public testers who labelled their in-game data with their reported experiences. A Random Forest algorithm non-intrusively predicts experiences via the game data. Lastly, it is not feasible to manually select or adapt the content from both spaces because of the immense amount of options available. Therefore, we apply reinforcement learning technique to generate a series of learning goals that promote an efficient learning for the player. Subsequently, a combination of conditional branching and agglomerative hierarchical clustering select the most appropriate game content for each selected education material. For a proof-of-concept, we apply the proposed approach to producing the SEG, named Chem Dungeon, as a case study to demonstrate the effectiveness of our proposed methods.
Supervisor: Chen, Ke ; Shapiro, Jonathan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.756907  DOI: Not available
Keywords: adaptation ; machine learning ; development framework ; serious educational game ; non-intrusive assessment
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