A Diagnostic Question Analysis Model based on a Modified Item Response Theory

DOI

10.2991/978-2-494069-31-2_371How to use a DOI?

Keywords

Item Response Theory; Missing Value Prediction; Education Study

Abstract

Digital technologies are being more widely used in education, allowing students all around the world to access individualized, high-quality educational resources. This paper analyzes a massive amounts of data derived from students’ interactions with these diagnostic questions can help us more accurately understand the students’ learning status and thus allow us to automate learning curriculum recommendations, as evidenced by thousands of examples of students’ answers to mathematics questions provided by The NeurlIPS 2020 Education Challenge [1, 2]. In this paper, a new generated model based on Item Response Theory (IRT) is put forward. Additionally, with discrimination parameter added and classification by groups, the model on real-world dataset is verified.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haonan Gao
PY  - 2022
DA  - 2022/12/29
TI  - A Diagnostic Question Analysis Model based on a Modified Item Response Theory
BT  - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)
PB  - Atlantis Press
SP  - 3164
EP  - 3169
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-31-2_371
DO  - 10.2991/978-2-494069-31-2_371
ID  - Gao2022
ER  -