Digital Educational Games: Adopting Pedagogical Agent to Infer Leaner‘s Motivation and Emotional State.

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Bibliographic Details
Title:	Digital Educational Games: Adopting Pedagogical Agent to Infer Leaner‘s Motivation and Emotional State.
Authors:	Tumenayu, Ogar Ofut¹ ofuriti4u@yahoo.com, Shabalina, Olga¹ O.A.Shabalina@gmail.com
Source:	Proceedings of the European Conference on Games Based Learning. 2021, p546-552. 7p.
Subject Terms:	Educational games, Active learning, COVID-19 pandemic, Motivation (Psychology), Emotional state
Abstract:	Digital educational games (DEGs) possess the ability of providing an attractively and essentially motivating learning context. However, an adaptive learning game would increase the probability of a DEG being actually motivating and emotionally appealing. A pedagogical agent‐based environment suggests a new opportunity for computer mediated learning emphasizing virtual social relations between learners and computers. The overall goal of this work is to provide pedagogical agents with social intelligence, so that they can decide when is an appropriate time to interact with the learner, be sensitive to the motivational and emotional state of the learner, and try to develop a positive social relationship with the learner. The study examine the effects of the competency (low vs. high) and interaction type (proactive vs. responsive) of pedagogical agents as learning companions (PALs) on learning, self‐efficacy, and attitudes. Interactions are been analyzed as a series of interaction tactics, where the speaker seeks to address one or more informational, motivational, or social goals, and monitors the listener’s response to ensure that these goals are achieved. This is followed by brief look at Affective Human Agent Interaction to have Existing agent systems typically infer human affect by sensing and reasoning about the state of a game or an outcome related to an action taken by the user within the learning environment. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	Digital educational games (DEGs) possess the ability of providing an attractively and essentially motivating learning context. However, an adaptive learning game would increase the probability of a DEG being actually motivating and emotionally appealing. A pedagogical agent‐based environment suggests a new opportunity for computer mediated learning emphasizing virtual social relations between learners and computers. The overall goal of this work is to provide pedagogical agents with social intelligence, so that they can decide when is an appropriate time to interact with the learner, be sensitive to the motivational and emotional state of the learner, and try to develop a positive social relationship with the learner. The study examine the effects of the competency (low vs. high) and interaction type (proactive vs. responsive) of pedagogical agents as learning companions (PALs) on learning, self‐efficacy, and attitudes. Interactions are been analyzed as a series of interaction tactics, where the speaker seeks to address one or more informational, motivational, or social goals, and monitors the listener’s response to ensure that these goals are achieved. This is followed by brief look at Affective Human Agent Interaction to have Existing agent systems typically infer human affect by sensing and reasoning about the state of a game or an outcome related to an action taken by the user within the learning environment. [ABSTRACT FROM AUTHOR]
ISSN:	20490992