Student Knowledge Prediction for Teacher-Student Interaction

Seonghun Kim, Woojin Kim, Yeonju Jang, Seongyune Choi, Heeseok Jung, Hyeoncheol Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages15560-15568
Number of pages9
ISBN (Electronic)9781713835974
Publication statusPublished - 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2021 Feb 22021 Feb 9

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume17B

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period21/2/221/2/9

ASJC Scopus subject areas

  • Artificial Intelligence

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