UKTF: Unified Knowledge Tracing Framework for Subjective and Objective Assessments
Zhifeng Wang, Jiaqin Wan, Yang Yang, Chunyan Zeng, Jialiang Shen

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…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning
