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.

翻译：随着智慧教育理念的不断深化与发展，学习者的全面发展和个体需求日益受到关注。然而，传统教育评价体系往往仅通过统一的测试分数来评估学习者的认知能力，未能全面考量其实际知识状态。知识追踪技术能够基于学习者的历史作答数据建立知识状态模型，从而实现对学习者的个性化评估。然而，当前经典的知识追踪模型主要适用于客观测试题，而主观测试题仍面临数据表征复杂、建模不完善以及知识状态复杂动态等挑战。本研究借鉴知识追踪技术在教育中的应用，旨在充分利用考试数据，提出一种融合客观与主观测试题的统一知识追踪模型。该模型认识到客观题与主观题在题目结构、评估方式和数据特征上的差异，采用相同的主干网络对两类题目进行训练。同时，通过通用化地修改基线模型的训练方式、增加分支网络并优化题目编码方法，实现了对主观测试题的知识追踪。本研究在真实数据集上进行了多次实验，结果一致表明该模型能有效解决客观与主观测试题场景下的知识追踪问题。