UKTF: Unified Knowledge Tracing Framework for Subjective and Objective Assessments

TL;DR

This paper introduces a unified knowledge tracing framework that effectively models both subjective and objective assessments, enhancing personalized education by accurately tracking learners' knowledge states.

Contribution

It proposes a novel unified model that integrates subjective and objective questions using a shared backbone network and modified training methods, addressing limitations of classical models.

Findings

Model effectively handles both question types in real datasets

Improves accuracy of knowledge state estimation

Addresses challenges in subjective question data representation

Abstract

With the continuous deepening and development of the concept of smart education, learners' comprehensive development and individual needs have received increasing attention. However, traditional educational evaluation systems tend to assess learners' cognitive abilities solely through general test scores, failing to comprehensively consider their actual knowledge states. Knowledge tracing technology can establish knowledge state models based on learners' historical answer data, thereby enabling personalized assessment of learners. Nevertheless, current classical knowledge tracing models are primarily suited for objective test questions, while subjective test questions still confront challenges such as complex data representation, imperfect modeling, and the intricate and dynamic nature of knowledge states. Drawing on the application of knowledge tracing technology in education, this study aims to fully utilize examination data and proposes a unified knowledge tracing model that integrates both objective and subjective test questions. Recognizing the differences in question structure, assessment methods, and data characteristics between objective and subjective test questions, the model employs the same backbone network for training both types of questions. Simultaneously, it achieves knowledge tracing for subjective test questions by universally modifying the training approach of the baseline model, adding branch networks, and optimizing the method of question encoding. This study conducted multiple experiments on real datasets, and the results consistently demonstrate that the model effectively addresses knowledge tracing issues in both objective and subjective test question scenarios.