Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.582814
Title: Playphysics: an emotional student model for game-based learning
Author: Esquivel, Karla Cristina Munoz
Awarding Body: University of Ulster
Current Institution: Ulster University
Date of Award: 2012
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Abstract:
Game-based learning (GBL) environments introduced a new generation of Intelligent Tutor- ing Systems (ITSs) that provide personalised instruction by being constantly aware of stu- dent reactions to the system. Student motivation, attitudes, self-efficacy and affective state have been the key focus of such developments. Current models of student emotion have shown promise in laboratory environments. However, the problem of accurately recognising and inferring student emotions within learning environments persists. The majority of already existing computational models of student emotion employ cognitive theories that are not de- rived from the learning context. Control-value theory (Pekrun et al. 2007) assumes that control and value appraisals are the most meaningful for determining emotions in educational settings. Our proposed compu- tational emotional student model uses the Control-value theory for reasoning about learners' emotions in GBL environments settings. The main hypothesis is that this model will recog- nise student achievement emotions, i.e. emotions relevant to the educational context, with reasonable accuracy (not random). The definition, implementation and evaluation of our computational emotional student model in PlayPhysics, an emotional game-based learning environment for teaching physics, are discussed. Our emotional model is implemented with a dynamic sequence of Bayesian networks for representation of learners' achievement emo- tions. Probabilistic Relational Models (PRMs) are employed to facilitate their derivation. The Necessary Path Condition algorithm is employed in combination with Pearson correlations and Binary and Multinomial logistic regression for defining network structure. The Expecta- tion Maximisation (EM) learning algorithm is employed for network parameter learning. Our model employs answers to questions in-game dialogues, contextual variables and physio- logical variables for recognising student emotion. Results show a fair accuracy of classification of student achievement emotions for the PlayPhysics' emotional student model when only contextual and behaviour variables are considered (values of Cohen's Kappa in a range larger than 0.2 but lower than or equal to 0.4), which then improves when physiological variables, i.e. Galvanic Skin Response (GSR), are incorporated (values of Cohen's Kappa in a range larger than 0.4 and lower than or equal to 0.6). Our emotional model provides enhanced understanding about the factors involved in reasoning about emotion. PlayPhysics GBL environment is assessed to attain an enhanced understanding of the student experience of achievement emotions. Future work may focus on creating further game challenges, identifying enhanced predictors for control and value, e.g. using sentiment analysis and analysis of facial expressions. Numerous applications, in areas ranging from biology to e-commerce, are envisioned for the application of our ap- proach to create intelligible and dynamic genetic and emotional consumer data models.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.582814  DOI: Not available
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