Dual-channel feature enhancement cognitive diagnosis framework for student performance prediction

Chengfeng Liu; Junbo Wang; Jinru Hu; Jianrui Chen

doi:10.1117/12.3035228

11 July 2024 Dual-channel feature enhancement cognitive diagnosis framework for student performance prediction

Chengfeng Liu, Junbo Wang, Jinru Hu, Jianrui Chen

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102E (2024) https://doi.org/10.1117/12.3035228
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Cognitive diagnosis is a critical focus for smart education systems, with the goal of evaluating students’ cognitive states and predicting scores based on their responses. Recent studies have primarily concentrated on leveraging neural networks to forge student-exercise interactions models. However, further exploration is needed to identify effective strategies for distinguishing correct and incorrect student responses. To address this challenge, we propose a Dual-channel Cognitive Diagnosis (DCD) framework that enhances features to determine students’ cognitive states by analyzing correct and incorrect responses. Initially, we utilize two types of response data to create adjacency matrices for heterogeneous graphs, distinguishing the characteristics of accurate and erroneous student responses. Subsequently, the students’ cognitive states are inferred by modeling two channels: the correct response channel and the incorrect response channel. Ultimately, the outcomes of experiments conducted across diverse authentic datasets validate the efficacy of our proposed framework.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengfeng Liu, Junbo Wang, Jinru Hu, and Jianrui Chen "Dual-channel feature enhancement cognitive diagnosis framework for student performance prediction", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102E (11 July 2024); https://doi.org/10.1117/12.3035228

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available