Test-taking Psychological State Enhanced Student Performance Prediction Model

ZHANG Wei; SONG Ling-ling; ZENG Xin-yao; HU Sen

doi:10.12263/DZXB.20250299

您当前的位置：

首页 >

文章列表页 >

Test-taking Psychological State Enhanced Student Performance Prediction Model

PAPERS | 更新时间：2025-12-27

- Test-taking Psychological State Enhanced Student Performance Prediction Model
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2864-2878(2025)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62377024)
- DOI：10.12263/DZXB.20250299
  CLC： TP391;
- Received：19 April 2025，
  
  Accepted：16 July 2025，
  
  Published：25 August 2025
- 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

references

王炼红 , 罗志辉 , 刘畅 . 面向慕课学习者评估的认知反应模型 [J ] . 电子学报 , 2023 , 51 ( 1 ): 18 - 25 .

WANG L H , LUO Z H , LIU C . Cognitive and response model for evaluation of MOOC learners [J ] . Acta Electronica Sinica , 2023 , 51 ( 1 ): 18 - 25 . (in Chinese)

刘铁园 , 陈威 , 常亮 , 等 . 基于深度学习的知识追踪研究进展 [J ] . 计算机研究与发展 , 2022 , 59 ( 1 ): 81 - 104 .

LIU T Y , CHEN W , CHANG L , et al . Research advances in the knowledge tracing based on deep learning [J ] . Journal of Computer Research and Development , 2022 , 59 ( 1 ): 81 - 104 . (in Chinese)

SU Y , SHEN S H , ZHU L B , et al . Global and local neural cognitive modeling for student performance prediction [J ] . Expert Systems with Applications , 2024 , 237 : 121637 .

LI X Y , GUO S Y , WU J , et al . An interpretable polytomous cognitive diagnosis framework for predicting examinee performance [J ] . Information Processing & Management , 2025 , 62 ( 1 ): 103913 .

贾瑞 , 董永权 , 刘源 , 等 . 知识点相关性与遗忘程度融合的深度知识追踪模型 [J ] . 计算机研究与发展 , 2025 , 62 ( 2 ): 364 - 373 .

JIA R , DONG Y Q , LIU Y , et al . Deep knowledge tracing model with the integration of skills relation and forgetting degree [J ] . Journal of Computer Research and Development , 2025 , 62 ( 2 ): 364 - 373 . (in Chinese)

闫秋艳 , 司雨晴 , 袁冠 , 等 . 基于学生-问题关联的异构图知识追踪模型 [J ] . 电子学报 , 2023 , 51 ( 12 ): 3549 - 3556 .

YAN Q Y , SI Y Q , YUAN G , et al . Student-problem association based heterogeneous graph knowledge tracing model [J ] . Acta Electronica Sinica , 2023 , 51 ( 12 ): 3549 - 3556 . (in Chinese)

SCHUNK D H , DIBENEDETTO M K . Self-efficacy theory in education [M ] // Handbook of Motivation at School . New York : Routledge , 2016 : 34 - 54 .

SLAVIN R E . Educational Psychology: Theory and Practice [M ] . 12th ed . New York : Pearson , 2018 .

LIU S , YU J W , LI Q , et al . Ability boosted knowledge tracing [J ] . Information Sciences , 2022 , 596 : 567 - 587 .

ZHAO W Z , XIA J , JIANG X P , et al . A novel framework for deep knowledge tracing via gating-controlled forgetting and learning mechanisms [J ] . Information Processing & Management , 2023 , 60 ( 1 ): 103114 .

孙建文 , 周建鹏 , 刘三女牙 , 等 . 基于多层注意力网络的可解释认知追踪方法 [J ] . 计算机研究与发展 , 2021 , 58 ( 12 ): 2630 - 2644 .

SUN J W , ZHOU J P , LIU S N Y , et al . Hierarchical attention network based interpretable knowledge tracing [J ] . Journal of Computer Research and Development , 2021 , 58 ( 12 ): 2630 - 2644 . (in Chinese)

CLARK K R . Learning theories: Behaviorism [J ] . Radiologic Technology , 2018 , 90 ( 2 ): 172 - 175 .

CHUNG J , GULCEHRE C , CHO K , et al . Empirical evaluation of gated recurrent neural networks on sequence modeling [EB/OL ] . ( 2024-12-11 )[ 2025-03-19 ] . https://arXiv.org/abs/1412.3555 https://arXiv.org/abs/1412.3555 .

ACKERMAN T A , GIERL M J , WALKER C M . Using multidimensional item response theory to evaluate educational and psychological tests [J ] . Educational Measurement: Issues and Practice , 2003 , 22 ( 3 ): 37 - 51 .

BANDURA A . Self-Efficacy: The Exercise of Control [M ] . New York : PWorth Publishers , 1997 .

ZHAN P D , JIAO H , LIAO D D . Cognitive diagnosis modelling incorporating item response times [J ] . British Journal of Mathematical and Statistical Psychology , 2018 , 71 ( 2 ): 262 - 286 .

HUANG T , HU S Z , YANG H L , et al . Response speed enhanced fine-grained knowledge tracing: A multi-task learning perspective [J ] . Expert Systems with Applications , 2024 , 238 : 122107 .

LIU Z C , WANG S Y , ZHANG S M , et al . A Mixture Fluency model using responses and response times with cognitive diagnosis model framework [J ] . Behavior Research Methods , 2024 , 56 ( 4 ): 3396 - 3451 .

HAMBLETON R K , SWAMINATHAN H , ROGERS H J . Fundamentals of Item Response Theory [M ] . California : Sage , 1991 .

SONG L Y , HE M T , SHANG X Q , et al . A deep cross-modal neural cognitive diagnosis framework for modeling student performance [J ] . Expert Systems with Applications , 2023 , 230 : 120675 .

DE LA TORRE J . DINA model and parameter estimation: A didactic [J ] . Journal of Educational and Behavioral Statistics , 2009 , 34 ( 1 ): 115 - 130 .

DE LA TORRE J , DOUGLAS J A . Higher-order latent trait models for cognitive diagnosis [J ] . Psychometrika , 2004 , 69 ( 3 ): 333 - 353 .

TEMPLIN J L , HENSON R A . Measurement of psychological disorders using cognitive diagnosis models [J ] . Psychological Methods , 2006 , 11 ( 3 ): 287 - 305 .

LIU Q , WU R Z , CHEN E H , et al . Fuzzy cognitive diagnosis for modelling examinee performance [J ] . ACM Transactions on Intelligent Systems and Technology , 2018 , 9 ( 4 ): 1 - 26 .

MEDSKER L , JAIN L C . Recurrent Neural Networks: Design and Applications [M ] . Boca Raton : CRC Press , 1999 .

SANTORO A , BARTUNOV S , BOTVINICK M , et al . Meta-learning with memory-augmented neural networks [C ] // International Conference on Machine Learning . Cambridge : PMLR , 2016 : 1842 - 1850 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // 31st Conference on Neural Information Processing Systems (NIPS 2017 ), LongBeach : ACM , 2017 : 6000 - 6010 .

SCARSELLI F , GORI M , TSOI A C , et al . The graph neural network model [J ] . IEEE Transactions on Neural Networks , 2009 , 20 ( 1 ): 61 - 80 .

PIECH C , BASSEN J , HUANG J , et al . Deep knowledge tracing [J ] . Advances in Neural Information Processing Systems , 2015 , 28 : 05908 .

CORBETT A T , ANDERSON J R . Knowledge tracing: Modeling the acquisition of procedural knowledge [J ] . User Modeling and User-Adapted Interaction , 1994 , 4 ( 4 ): 253 - 278 .

王炼红 , 罗志辉 , 林飞鹏 , 等 . 采用多头注意力机制的C&RM-MAKT预测算法 [J ] . 电子学报 , 2023 , 51 ( 5 ): 1215 - 1222 .

WANG L H , LUO Z H , LIN F P , et al . C & RM-MAKT prediction algorithm using multi-head attention mechanism [J ] . Acta Electronica Sinica , 2023 , 51 ( 5 ): 1215 - 1222 . (in Chinese)

张峰 , 姜婷婷 . DKVMN-PLC: 一种融合学习者个性化学习特征的动态键值记忆网络知识追踪模型 [J ] . 小型微型计算机系统 , 2024 , 45 ( 5 ): 1272 - 1280 .

ZHANG F , JIANG T T . DKVMN-PLC: A dynamic key-value memory networks with personalized learning characteristics of learners for knowledge tracking [J ] . Journal of Chinese Computer Systems , 2024 , 45 ( 5 ): 1272 - 1280 . (in Chinese)

ZHANG J N , SHI X J , KING I , et al . Dynamic key-value memory networks for knowledge tracing [C ] // Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee , 2017 : 765 - 774 .

PANDEY S , KARYPIS G . A self-attentive model for knowledge tracing [EB/OL ] . ( 2019-07-16 )[ 2025-03-19 ] . https://arXiv.org/abs/1907.06837 https://arXiv.org/abs/1907.06837 .

NAKAGAWA H , IWASAWA Y , MATSUO Y . Graph-based knowledge tracing: Modeling student proficiency using graph neural network [C ] // IEEE/WIC/ACM International Conference on Web Intelligence . New York : ACM , 2019 : 156 - 163 .

WANG W T , MA H F , ZHAO Y , et al . SEEP: Semantic-enhanced question embeddings pre-training for improving knowledge tracing [J ] . Information Sciences , 2022 , 614 : 153 - 169 .

ZHAO Y , MA H F , WANG J , et al . Question-response representation with dual-level contrastive learning for improving knowledge tracing [J ] . Information Sciences , 2024 , 658 : 120032 .

SEDGWICK P M . Pearson’s correlation coefficient [J ] . British Medical Journal , 2012 , 345 : e4483 .

SUBAKAN C , RAVANELLI M , CORNELL S , et al . Attention is all you need in speech separation [C ] // ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing . Piscataway : IEEE , 2021 : 21 - 25 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [EB/OL ] . ( 2017-02-22 )[ 2025-03-19 ] . https://arXiv.org/abs/1609.02907 https://arXiv.org/abs/1609.02907 .

CHEN J H , LIU Z T , HUANG S Y , et al . Improving interpretability of deep sequential knowledge tracing models with question-centric cognitive representations [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2023 , 37 ( 12 ): 14196 - 14204 .

QI T L , REN M R , GUO L J , et al . ICD: A new interpretable cognitive diagnosis model for intelligent tutor systems [J ] . Expert Systems with Applications , 2023 , 215 : 119309 .

KINGMA D P , BA J . Adam: A method for stochastic optimization [EB/OL ] . ( 2017-01-30 )[ 2025-03-19 ] . https://arXiv.org/abs/1412.6980 https://arXiv.org/abs/1412.6980 .

WANG F , LIU Q , CHEN E H , et al . NeuralCD: A general framework for cognitive diagnosis [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 8 ): 8312 - 8327 .

WANG S S , ZENG Z , YANG X , et al . Boosting neural cognitive diagnosis with student’s affective state modeling [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2024 , 38 ( 1 ): 620 - 627 .

KE F C , WANG W Q , TAN W C , et al . HiTSKT: A hierarchical transformer model for session-aware knowledge tracing [J ] . Knowledge-Based Systems , 2024 , 284 : 111300 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Attention Mechanism Optimized Generative Adversarial Networks and Their Application in Sea Clutter Simulation

A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis

Student-Problem Association Based Heterogeneous Graph Knowledge Tracing Model

Related Author

WANG Wei

DONG Zi-ying

CHEN Peng

ZHANG Su-kai

PENG Jun-jie

ZHANG Huan-xiang

SI Yu-qing

WANG Zhi-xiao

Related Institution

School of Information Engineering, Chang’an University

School of Innovation and Entrepreneurship Education， Inner Mongolia University of Science and Technology

Shanghai Institute for Advanced Communication and Data Science

School of Computer Engineering and Science，Shanghai University

School of Computer Science & Technology， China University of Mining and Technology

⁰