Student Knowledge Prediction for Teacher-Student Interaction

Authors

  • Seonghun Kim Korea University
  • Woojin Kim Korea University
  • Yeonju Jang Korea University
  • Seongyune Choi Korea University
  • Heeseok Jung Korea University
  • Hyeoncheol Kim Korea University

Keywords:

Teacher-Student Interaction, Harrykim@korea.ac.kr, Student Knowledge Prediction, EXplainable Artificial Intelligence, Intelligent Tutoring Systems (ITS)

Abstract

The constraint in sharing the same physical learning environment with students in distance learning poses difficulties to teachers. A significant teacher-student interaction without observing students' academic status is undesirable in the constructivist view on education. To remedy teachers' hardships in estimating students' knowledge state, we propose a Student Knowledge Prediction Framework that models and explains student's knowledge state for teachers. The knowledge state of a student is modeled to predict the future mastery level on a knowledge concept. The proposed framework is integrated into an e-learning application as a measure of automated feedback. We verified the applicability of the assessment framework through an expert survey. We anticipate that the proposed framework will achieve active teacher-student interaction by informing student knowledge state to teachers in distance learning.

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Published

2021-05-18

How to Cite

Kim, S., Kim, W., Jang, Y., Choi, S., Jung, H., & Kim, H. (2021). Student Knowledge Prediction for Teacher-Student Interaction. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15560-15568. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17832