5 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, the current emphasis of research is shifting from a user’s traces in the system (for instance, to derive his level of expertise) toward 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 the users can keep them 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, as well as 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 extend 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, the current emphasis of research is shifting from a user’s traces in the system (for instance, to derive his level of expertise) toward 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 the users can keep them 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, as well as 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 extend 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 personalization
- 1 Theory-grounded user modeling for personalized HCI 1
- 2 User-centered recommender systems 33
- 3 Fairness of information access systems 59
-
Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes 81
- 5 How to use socio-emotional signals for adaptive training 99
- 6 Explanations and user control in recommender systems 129
- 7 Feedback loops and mutual reinforcement in personalized interaction 153
-
Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities 175
- 9 Personalized persuasion for behavior change 205
- 10 Personalization approaches for remote collaborative interaction 237
-
Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends 279
- 12 Tourist trip recommendations – foundations, state of the art and challenges 313
- 13 Pictures as a tool for matching tourist preferences with destinations 337
- Index 355
Chapters in this book
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of personalization
- 1 Theory-grounded user modeling for personalized HCI 1
- 2 User-centered recommender systems 33
- 3 Fairness of information access systems 59
-
Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes 81
- 5 How to use socio-emotional signals for adaptive training 99
- 6 Explanations and user control in recommender systems 129
- 7 Feedback loops and mutual reinforcement in personalized interaction 153
-
Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities 175
- 9 Personalized persuasion for behavior change 205
- 10 Personalization approaches for remote collaborative interaction 237
-
Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends 279
- 12 Tourist trip recommendations – foundations, state of the art and challenges 313
- 13 Pictures as a tool for matching tourist preferences with destinations 337
- Index 355
