Enhancing electronic intelligent tutoring systems by responding to affective states

Tao, Xiaomei (2016) Enhancing electronic intelligent tutoring systems by responding to affective states. Doctoral thesis, Birmingham City University.

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Abstract

The overall aim of this research is the exploration mechanisms which allow an understanding of the emotional state of students and the selection of an appropriate cognitive and affective feedback for students on the basis of students' emotional state and cognitive state in an affective learning environment. The learning environment in which this research is based is one in which students learn by watching an instructional video. The main contributions in the thesis include:
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- A video study was carried out to gather data in order to construct the emotional models in this research. This video study adopted a methodology in qualitative research called “Quick and Dirty Ethnography”(Hughes et al., 1995). In the video study, the emotional states, including boredom, frustration, confusion, flow, happiness, interest, were identified as being the most important to a learner in learning. The results of the video study indicates that blink frequencies can reflect the learner's emotional states and it is necessary to intervene when students are in self-learning through watching an instructional video in order to ensure that attention levels do not decrease. 

- A novel emotional analysis model for modeling student’s cognitive and emotional state in an affective learning system was constructed. It is an appraisal model which is on the basis of an instructional theory called Gagne’s theory (Gagne, 1965). 

- A novel emotion feedback model for producing appropriate feedback tactics in affective learning system was developed by Ontology and Influence Diagram ii approach. On the basis of the tutor-remediation hypothesis and the self-remediation hypothesis (Hausmann et al., 2013), two feedback tactic selection algorithms were designed and implemented.

The evaluation results show: the emotion analysis model can be used to classify negative emotion and hence deduce the learner’s cognitive state; the degree of satisfaction with the feedback based on the tutor-remediation hypothesis is higher than the feedback based on self-remediation hypothesis; the results indicated a higher degree of satisfaction with the combined cognitive and emotional feedback than cognitive feedback on its own.

Item Type:	Thesis (Doctoral)
Additional Information:	Primarily, I would like to thank my main supervisor, Mike Jackson, who has been an unceasing source of advice, feedback, guidance, support and encouragement during the whole Phd research stage. I am also very grateful to Dr Martyn Ratcliffe, my study director, and Prof Qinzhou Niu, my local supervisor, who both supplied lots of advice, guidance, support, idea and disscussions to me. And I also express my thanks to Dr. Bin Hu for his help when he was in the supervisor team. I would like to thank the teachers and students who participated in the video study and the evaluation in the research work, especially my colleges: Jili Chen, Hengzhou Ye, Xinxiao Li, Xiaohui Chen, Yu Wang, Mingliang Zou, Qiu Lu, Qiang Guo, Hanying Liu, Yunsheng Ge. And I also want to express my thanks to the developers of the evaluation system, Jian Huang, Hai Lu. I would like to express my appreciation to Prof. Paul Bowker, Prof. Peter J. Larkham, Prof. John Sparrow, for their advice to my research. Finally, in particular, I would like to thank to all of my family, and especially my parents, my husband David and my son, my husband's parents, for their consistent support and encouragement that has helped to make this all possible.
Dates:	Date Event 12 October 2016 Completed
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Doctoral Research College > Doctoral Theses Collection
Depositing User:	Kip Darling
Date Deposited:	13 Mar 2019 12:45
Last Modified:	12 Jan 2022 12:53
URI:	https://www.open-access.bcu.ac.uk/id/eprint/7217

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