Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 170-177.doi: 10.11896/jsjkx.240300121

• Technology and Application of Intelligent Education • Previous Articles     Next Articles

Speed-Accuracy Tradeoff-based Deep Cognitive Diagnostic Model

CHENG Yan1,2,3, ZHOU Ziwei2,3, MA Mingyu2,3, LIN Qinglong2,3, ZHAN Yongxin2,3, WAN Lingfeng2,3   

  1. 1 School of Software,Jiangxi Normal University,Nanchang 330022,China
    2 School of Computer Information Engineering,Jiangxi Normal University,Nanchang 330022,China
    3 Provincial Key Laboratory of Intelligent Information Processing and Affective Computing of Jiangxi Province,Nanchang 330022,China
  • Received:2024-03-18 Revised:2024-07-07 Online:2024-10-15 Published:2024-10-11
  • About author:CHEN Yan,born in 1976,Ph.D,professor,Ph.D supervisor.Her main reaserch interests include artificial intelligence education,deep learning and emotional computing,artificial intelligence and big data,intelligent information processing,etc.
  • Supported by:
    National Natural Science Foundation of China(62167006),Jiangxi Provincial Science and Technology Innovation Base Program Project－Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing(2024SSY03131),Jiangxi Province Leading Talent Project of Major Academic Disciplines and Technologies(20213BCJL22047) andNatural Science Foundation of Jiangxi Province,China(20212BAB202017).

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Abstract: In intelligent education,cognitive diagnosis analyzes students' learning behavior data to understand their cognitive state.Existing cognitive diagnostic models based on deep learning methods assume by default that students have enough reaction time to fully exert the level of knowledge mastery during the response process,and do not consider the impact of the trade-off strategy between the speed and accuracy of student's response during the response process on the exertion of the level of know-ledge mastery.Aiming at the above problem,a deep cognitive diagnostic model based on speed-accuracy trade-off is proposed,which firstly constructs a cognitive style fuzzy set to explain the students' trade-off strategy,and then simulates the speed-accuracy trade-off relationship in the process of the learners' response through the dynamic logistic regression function,so as to rea-lize the differentiated diagnosis of the students' theoretically highest level of knowledge mastery from the level of knowledge mastery they have played out in the actual response.In addition,the reaction time attribute and exercise type attribute are introduced to more accurately characterize the topic parameters in the cognitive diagnostic interaction function.Numerous experiments show that the model not only improves the accuracy by 2.58%,2.86%,and 5.18% compared to similar optimal models on the three publicly available datasets,but also provides a superior explanation of the prediction results at the level of response time.

Key words: Intelligent education, Deep cognitive diagnostics, Speed-Accuracy trade-off, Fuzzy sets, Logistic regression function

CLC Number: 

  • TP391
[1]JIA T,GU X Q.Data technology-driven reshaping of educational forms:paths and processes[J].China Educational Technology,2021(3):38-45.
[2]LUO Z S.Fundamentals of Cognitive Diagnostic Assessment[M].Beijing:Beijing Normal University Press,2019.
[3]EMBRETSON S E,REISE S P.Item response theory[M].Psychology Press,2013.
[4]DE LA TORRE J.DINA model and parameter estimation:A didactic[J].Journal of Educational and Behavioral Statistics,2009,34(1):115-130.
[5]RECKASE M D.The past and future of multidimensional item response theory[J].Applied Psychological Measurement,1997,21(1):25-36.
[6]DE LA TORRE J.The generalized DINA model framework[J].Psychometrika,2011,76:179-199.
[7]LIU Q.Towards a New Generation of Cognitive Diagnosis[C]//IJCAI.2021:4961-4964.
[8]LIU Q,WU R,CHEN E,et al.Fuzzy cognitive diagnosis for modelling examinee performance[J].ACM Transactions on Intelligent Systems and Technology(TIST),2018,9(4):1-26.
[9]CHENG S,LIU Q,CHEN E,et al.DIRT:Deep learning en-hanced item response theory for cognitive diagnosis[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:2397-2400.
[10]WANG F,LIU Q,CHEN E,et al.NeuralCD:A General Framework for Cognitive Diagnosis[J].IEEE Transactions on Know-ledge and Data Engineering,2023,35(8):8312-8327.
[11]MA H,LI M,WU L,et al.Knowledge-Sensed Cognitive Diagnosis for Intelligent Education Platforms[C]//Proceedings of the 31st ACM International Conference on Information & Know-ledge Management.2022:1451-1460.
[12]GUO X J,LUO Z S.Psychometric model and application based on speed-accuracy trade-offs[J].Psychological Exploration,2019,39(5):451-460.
[13]WICKELGREN W A.Speed-accuracy tradeoff and information processing dynamics[J].Acta Psychologica,1977,41(1):67-85.
[14]HEITZ R P.The speed-accuracy tradeoff:history,physiology,methodology,and behavior[J].Frontiers in Neuroscience,2014,8:150.
[15]REED A V.Speed-accuracy trade-off in recognition memory[J].Science,1973,181(4099):574-576.
[16]DONKIN C,LITTLE D R,HOUPT J W.Assessing the speed-accuracy trade-off effect on the capacity of information proces-sing[J].Journal of Experimental Psychology:Human Perception and Performance,2014,40(3):1183.
[17]RATCLIFF R,SMITH P L,BROWN S D,et al.Diffusion decision model:Current issues and history[J].Trends in Cognitive Sciences,2016,20(4):260-281.
[18]ZHU Y.Experimental Psychology(2nd ed.)[M].Beijing:Pe-king University Press,2009.
[19]ZHANG S J,YU X H,CHEN E H,et al.A Concept Interac-tion-Based Cognitive Diagnosis Deep Model[J].Pattern Recognition and Artificial Intelligence,2023,36(1):22-33.
[20]WANG W Y,SONG L H,DING S L.Cognitive Diagnostic Test Item Distinction Indicators and Applications from a Categorical Perspective[J].Psychological Science,2018,41(2):475-483.
[21]HE J,MAO X Z,TANG Q,et al.A dual-objective CD-CAT se-lection strategy based on item differentiation[J].Psychological Science,2022,45(1):204-212.
[22]GUO X J,LUO Z S.The speed-accuracy trade-off:evaluationand modeling of subjects' response states[J].Studies of Psycho-logy and Behavior,2019,17(5):589-595.
[23]YAN J H.Cognitive styles affect choice response time and accuracy[J].Personality and Individual Differences,2010(6):747-751.
[24]SULISAWATI D N,LUTFIYAH L,MURTINASARI F.Difference of mistakes reflective-impulsive students in mathematical problem solving[J].International Journal of Trends in Mathematics Education Research,2019(2):101-105.
[25]FENG M,HEFFERNAN N,KOEDINGER K.Addressing theassessment challenge with an online system that tutors as it assesses[J].User Modeling and User-Adapted Interaction,2009,19(3):243-266.
[26]LIU J Y,WANG F,MA H P,et al.A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems[J].Journal of Computer Science and Technology,2023,38(6):1203-1222.
[27]LI Y H,YANG X Y.Educational and Psychological Statistics[M].Jiangxi University Press,2020.
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