1. Personalizing conversational agent based e-learning applications
11/22/2018 2:49 AM
Personalizing conversational agent based e-learning applications
How can information about the user, and their affect, personality, performance, learning styles be used to improve the experience for a student interacting with a conversational agent in a learning activity?
Mike Procter
AU Graduate Student Conference 2015
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2. Conversational agents and students
CAs allow natural language interaction with regard to some domain of knowledge
Animated pedagogical agents, Intelligent tutor systems, Game-base learning Role-playing actors
Appearance of understanding, reply reasonably, follow rules of conversation, appear intelligent
Non-verbal communication, emotional awareness, understanding personality, goals help maintain the illusion and can be used to direct “conversation”

3. Proposed solution

4. Student model data sources
DSA
DSA
DSA
Student Model Agent
DSA

5. Student model
The DSA agents will form the essence of the model (Model = DSA agents + integration agent)
The selection of data sources is based on what is available, and what the CA can use
A base set will always be available. These will use the conversational record (because it’s always available).
Engagement classifier based on ML
Computational model of emotion

6. Evaluation An existing CA (Freudbot) will be tested
Students will chat with standard version of Freudbot, or one enhanced by the described system
Chats will be followed up by a questionnaire to measure students impression of the experience
Conversation logs will be analyzed for evidence of the effectiveness of intervention actions