4. How to use socio-emotional signals for adaptive training
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Tobias Moebert
Abstract
A closer alignment of mutual expectations between technical systems and their users regarding functionality and interactions is supposed to improve their overall performance. In general, such an alignment is realized by automatically adapting the appearance and the behavior of a system. Adaptation may be based on parameters regarding the task to be fulfilled, the surrounding context, or the user himself. Among the latter, current emphasis of research is shifting from a user’s trails in the system (for instance, to derive his level of expertise) towards transient aspects (like his current mental or emotional state). For educational technology, in particular, adapting the presented information and the tasks to be solved to the current personal needs of a learner promises a higher motivation and thus a better learning outcome. Tasks which are equally challenging and motivating can keep the users in a state of flow and thus foster enduring engagement. This is of certain importance for difficult topics and/or learners with disabilities. The chapter explains the complex cause-and-effect models behind adaptive training systems, the mechanisms that can be facilitated to implement them, and empirical results from a clinical study. We exemplify this for the training of emotion recognition by people with autism, but not limited to this user group. For this purpose, we present two approaches. One is to extent the Elo algorithm regarding dimensions of difficulty in social cognition. This allows not only to judge the difficulty of tasks and the skills of users, but also to freely generate well-suited tasks. The second approach is to make use of socio-emotional signals of the learners in order to further adapt the training system. We discuss current possibilities and remaining challenges for these approaches.
Abstract
A closer alignment of mutual expectations between technical systems and their users regarding functionality and interactions is supposed to improve their overall performance. In general, such an alignment is realized by automatically adapting the appearance and the behavior of a system. Adaptation may be based on parameters regarding the task to be fulfilled, the surrounding context, or the user himself. Among the latter, current emphasis of research is shifting from a user’s trails in the system (for instance, to derive his level of expertise) towards transient aspects (like his current mental or emotional state). For educational technology, in particular, adapting the presented information and the tasks to be solved to the current personal needs of a learner promises a higher motivation and thus a better learning outcome. Tasks which are equally challenging and motivating can keep the users in a state of flow and thus foster enduring engagement. This is of certain importance for difficult topics and/or learners with disabilities. The chapter explains the complex cause-and-effect models behind adaptive training systems, the mechanisms that can be facilitated to implement them, and empirical results from a clinical study. We exemplify this for the training of emotion recognition by people with autism, but not limited to this user group. For this purpose, we present two approaches. One is to extent the Elo algorithm regarding dimensions of difficulty in social cognition. This allows not only to judge the difficulty of tasks and the skills of users, but also to freely generate well-suited tasks. The second approach is to make use of socio-emotional signals of the learners in order to further adapt the training system. We discuss current possibilities and remaining challenges for these approaches.
Chapters in this book
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of user modeling
- 1. Theory-grounded user modeling for personalized HCI 1
- 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
-
Part II: User input and feedback
- 3. Automated personalization of input methods and processes 67
- 4. How to use socio-emotional signals for adaptive training 103
- 5. Explanations and user control in recommender systems 133
-
Part III: Personalization approaches
- 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
- 7. Pictures as a tool for matching tourist preferences with destinations 183
- 8. Towards personalized virtual reality touring through cross-object user interfaces 201
- 9. User awareness in music recommender systems 223
- 10. Personalizing the user interface for people with disabilities 253
- 11. Adaptive workplace learning assistance 283
- Index 303
Chapters in this book
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of user modeling
- 1. Theory-grounded user modeling for personalized HCI 1
- 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
-
Part II: User input and feedback
- 3. Automated personalization of input methods and processes 67
- 4. How to use socio-emotional signals for adaptive training 103
- 5. Explanations and user control in recommender systems 133
-
Part III: Personalization approaches
- 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
- 7. Pictures as a tool for matching tourist preferences with destinations 183
- 8. Towards personalized virtual reality touring through cross-object user interfaces 201
- 9. User awareness in music recommender systems 223
- 10. Personalizing the user interface for people with disabilities 253
- 11. Adaptive workplace learning assistance 283
- Index 303
