TSConnect: An Enhanced MOOC Platform for Bridging Communication Gaps Between Instructors and Students in Light of the Curse of Knowledge
Project Overview
The document explores the integration of generative AI in education through the TSConnect platform, which enhances traditional MOOCs by addressing the communication gap between instructors and students, often exacerbated by the 'curse of knowledge' bias. This cognitive bias results in instructors underestimating the difficulties students face due to their own deep understanding of subjects. TSConnect employs AI-driven tools, such as dynamic knowledge graphs, to visualize relationships between prerequisite knowledge, thereby improving feedback mechanisms and fostering a clearer understanding of course content. The platform aims to boost student engagement and enhance the effectiveness of feedback, leading to improved teaching strategies and better learning outcomes. Overall, the findings highlight the potential of generative AI to transform educational experiences by facilitating clearer communication and deeper understanding, ultimately striving for more effective learning environments.
Key Applications
TSConnect - an adaptable interactive MOOC learning system.
Context: Used in online educational settings targeting students and instructors in higher education.
Implementation: Developed based on surveys and interviews with students and instructors, integrating feedback channels and dynamic knowledge graphs.
Outcomes: Increased student feedback, enhanced understanding of prerequisite knowledge, and improved teaching strategies.
Challenges: Initial setup complexity, potential limitations in capturing handwritten content, and reliance on technology adoption by instructors.
Implementation Barriers
Cognitive Bias
Instructors may struggle to recognize gaps in students' understanding due to their own expertise, leading to ineffective communication.
Proposed Solutions: TSConnect aims to raise awareness of the curse of knowledge and facilitate better communication through structured feedback mechanisms.
Technological Limitations
Challenges with processing handwritten content and ensuring accurate feedback collection.
Proposed Solutions: Future improvements may include audio processing and enhanced visualizations to support comprehension.
Project Team
Qianyu Liu
Researcher
Xinran Li
Researcher
Xiaocong Du
Researcher
Quan Li
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Qianyu Liu, Xinran Li, Xiaocong Du, Quan Li
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai