应试心理状态增强的学生表现预测模型

张维; 宋玲玲; 曾鑫耀; 胡森

doi:10.12263/DZXB.20250299

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应试心理状态增强的学生表现预测模型

学术论文 | 更新时间：2025-12-27

- 应试心理状态增强的学生表现预测模型
- Test-taking Psychological State Enhanced Student Performance Prediction Model
- 电子学报 2025年53卷第8期页码：2864-2878
- 作者机构：
  
  华中师范大学人工智能教育学部，湖北武汉 430079
- 作者简介：
  
  [ "张维男，1975年出生于湖北省仙桃市.博士.现为华中师范大学人工智能教育学部教授、博士生导师.主要研究方向为教育数据挖掘、智能教育评估、大数据分析等. E-mail: zwccnu@ccnu.edu.cn" ]
  [ "宋玲玲女，1994年出生于湖北省天门市.现为华中师范大学人工智能教育学部博士研究生.主要研究方向为知识追踪、认知诊断. E-mail: linglingsong@mails.ccnu.edu.cn" ]
  [ "曾鑫耀男，1998年出生于湖北省仙桃市.现为华中师范大学人工智能教育学部博士研究生.主要研究方向为知识追踪、推荐算法. E-mail: zengxy_98@mails.ccnu.edu.cn" ]
  [ "胡森男，1997年出生于湖北省仙桃市.硕士.主要研究方向为知识追踪. E-mail: hsen@mails.ccnu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62377024)
- DOI：10.12263/DZXB.20250299
  中图分类号： TP391;
- 收稿：2025-04-19，
  
  录用：2025-07-16，
  
  纸质出版：2025-08-25
- 稿件说明：
移动端阅览
张维, 宋玲玲, 曾鑫耀, 等. 应试心理状态增强的学生表现预测模型[J]. 电子学报, 2025, 53(08): 2864-2878.

ZHANG Wei, SONG Ling-ling, ZENG Xin-yao, et al. Test-taking Psychological State Enhanced Student Performance Prediction Model[J]. Acta Electronica Sinica, 2025, 53(08): 2864-2878.
张维, 宋玲玲, 曾鑫耀, 等. 应试心理状态增强的学生表现预测模型[J]. 电子学报, 2025, 53(08): 2864-2878. DOI：10.12263/DZXB.20250299

ZHANG Wei, SONG Ling-ling, ZENG Xin-yao, et al. Test-taking Psychological State Enhanced Student Performance Prediction Model[J]. Acta Electronica Sinica, 2025, 53(08): 2864-2878. DOI：10.12263/DZXB.20250299

摘要

准确预测学生答题表现是智能导学系统为学生提供个性化学习服务的先决条件.认知诊断和知识追踪作为主流的学生表现预测方法，均将学生表现仅归因于知识状态，而忽视了学生答题过程中的应试心理状态，限制了模型预测精准性的进一步提升.为此，本文将学生的应试心理状态融入以知识为中心的学生表现预测模型中，并结合认知诊断可解释与知识追踪动态预测的互补优势，提出了一种应试心理状态增强的学生表现预测模型（Test-taking psychological state enhanced Student Performance Prediction model，TSPP）.该模型通过捕捉习题与学生答题行为之间的复杂高阶关系，对学生应试心理状态进行建模；同时通过提取异构知识图中丰富的节点间关系对学生动态知识状态进行建模；最后设计了一种渐进式融合门，其采用可解释渐进式的方式融合应试心理状态与知识状态，得到可解释的预测结果.在3个真实世界数据集上的大量实验结果表明，TSPP模型在AUC（Area Under the Curve）和ACC（ACCuracy）2项指标上，相较于9种对比模型的平均表现，分别提升了6.05%和7.27%，在RMSE（Root Mean Square Error）指标上降低了6.76%.此外，通过对TSPP中的应试心理状态和知识状态进行可视化分析，并探究其可解释性参数的优势，本文进一步验证了TSPP的可解释性.

Abstract

Accurately predicting student performance is a prerequisite for intelligent tutoring systems to provide students with personalized learning services. As mainstream methods for student performance prediction

both cognitive diagnosis and knowledge tracing attribute student performance solely to knowledge states

neglecting students’ test-taking psychological states during the answering process

thereby limiting further improvements in prediction accuracy. To this end

this paper proposes a test-taking psychological state enhanced student performance prediction model (TSPP)

which integrates students’ test-taking psychological states into the knowledge-centered student performance prediction model and combines the complementary advantages of the interpretability of cognitive diagnosis with dynamic prediction capability of knowledge tracing. The model models students’ test-taking psychological states by capturing complex high-order relations between exercises and their answering behaviors. Meanwhile

it models students’ dynamic knowledge states by extracting rich inter-node relations in heterogeneous knowledge graphs. Finally

we design a progressive fusion gate that employs an interpretable progressive approach to integrate test-taking psychological states and knowledge states to obtain interpretable prediction results. Extensive experimental results on three real-world datasets demonstrate that the TSPP model achieves 6.05% and 7.27% improvements in AUC (Area Under the Curve) and ACC (Accuracy)

respectively

and a 6.76% reduction in RMSE (Root Mean Square Error)

compared to the average performance of nine baseline models. Additionally

we further validate the explainability of TSPP by visually analyzing the test-taking psychological state and knowledge state in TSPP

and by investigating the advantages of the explainability parameters designed in the model.

关键词

Keywords

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