Affective Learning Recommendation Bot

December 14th, 2021

Recommending which resource to chose next to maximise learning effect based on sentiment and intent detection

Thesis Type
  • Bachelor
Juan Stücker
Submitted in
Presentation on
21/06/2022 2:45 pm
Presentation room
Seminar room
Ralf Klamma
Stefan Decker
Alexander Neumann
Michal Slupczynski

In our currently running project MILKI-PSY, we focus on multimodal immersive mentoring to facilitate Self Regulated Learning (SRL). To support mentors with the tools they need to provide adaptive and personalized tutoring, a set of learning services was developed and is currently under development. These services utilize our flagship peer-to-peer community platform las2peer.

At our chair, we developed a Social Bot Framework, which allows for a model-driven construction and utilization of social bots in the domain of technology enhanced learning (TEL). Recent developments in TEL saw a rise in popularity of Distance Education (DE), which is characterized by the intense use of communication technologies and information systems to facilitate SRL. The main premise of SRL is for students to mostly work on their own accord supported by a Learning Management System (LMS). LMS (e.g. Moodle) is used to facilitate administration, delivery and tracking of educational courses. We collect multimodal learner data and store them in a learning record store (LRS). The application of social bots in LMS can be used to provide direct, context-specific feedback in SRL. With the latest enhancements, the bot is able to perform Moodle quizzes within a conversational channel (Slack or Rocket.Chat).

The emotions of a learner play a fundamental role in the learning process. Recent advances in emotion recognition allow information systems to react to the emotional state of students and respond by providing context-specific personalized suggestions of content to maximise learning effects. While e-commerce systems have seen a rise in affective recommendation systems, there has been little research of such applications in the educational field.

The goal of this thesis is to compare existing recommender algorithms and extend our existing mentoring bot framework to provide personalized recommendations regarding learning content.

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