Introducing Vicky: A Pedagogical Conversational Agent for the Classification of Learning Styles
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Prototype Papers International Conference on Design Science Research in Information Systems and Technology. 2022.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Introducing Vicky: A Pedagogical Conversational Agent for the Classification of Learning Styles
AU - Khosrawi-Rad, Bijan
AU - Keller, Paul Felix
AU - Grogorick, Linda
AU - Robra-Bissantz, Susanne
PY - 2022
Y1 - 2022
N2 - Learners are faced with the challenge of processing a large amount of knowledge. However, they often lack individual support, and teaching is not tailored to their learning styles. Conversational Agents (CAs) could be a way to identify personal learning styles through a dialog between the CA and the learner, and to support him/her accordingly. This paper investigates whether learning styles can be determined through a dialog with a CA and how the conversation is perceived by users. For this purpose, a CA called Vicky was developed using the platform Rasa and the intent classifier DIET. Vicky determines the user’s learning styles through a questionnaire as well as a quiz and acts human-like to be perceived as a virtual companion. The prototype was evaluated in an experiment with 25 participants. They predominantly perceived themselves to be correctly classified and treated kindly by the CA. Overall, we contribute to science and practice by showing that CAs acting as virtual companions can be used to better understand learner preferences.
AB - Learners are faced with the challenge of processing a large amount of knowledge. However, they often lack individual support, and teaching is not tailored to their learning styles. Conversational Agents (CAs) could be a way to identify personal learning styles through a dialog between the CA and the learner, and to support him/her accordingly. This paper investigates whether learning styles can be determined through a dialog with a CA and how the conversation is perceived by users. For this purpose, a CA called Vicky was developed using the platform Rasa and the intent classifier DIET. Vicky determines the user’s learning styles through a questionnaire as well as a quiz and acts human-like to be perceived as a virtual companion. The prototype was evaluated in an experiment with 25 participants. They predominantly perceived themselves to be correctly classified and treated kindly by the CA. Overall, we contribute to science and practice by showing that CAs acting as virtual companions can be used to better understand learner preferences.
M3 - Aufsätze in Konferenzbänden
BT - Prototype Papers International Conference on Design Science Research in Information Systems and Technology
ER -