Student Knowledge Prediction for Teacher-Student Interaction

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

EAAI Symposium: Full Papers

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.

10.1609/aaai.v35i17.17832