知识空间理论综述

doi:10.16451/j.cnki.issn1003-6059.202402002

Abstract
Figure/Table
References (214)
Related Citation (9)

Download: PDF (1044 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Knowledge space theory is a scientific method for studying educational principles, and it yields a series of research findings. This paper is intended to comprehensively review the research efforts on knowledge space theory. Firstly, the methods and principles of constructing knowledge structures are elaborated, the research contents related to the wellgradedness are introduced, and their importance to the study of knowledge space theory is underscored. The related work of surmise relation is summarized, as well as the research methods between problems and between items. Then, research progress of competence-based knowledge space theory is delineated from three aspects: skill maps, skill functions and competence-performance approaches. Furthermore, the researches combinating knowledge space theory with probability models and granular computing are outlined, including the application of knowledge space theory in assisted learning and adaptive testing. Finally, the key scientific issues are explored in the above research fields and some preliminary research ideas are provided, providing beneficial references for subsequent studies in this field.

Key words： Knowledge Space Theory Knowledge Structure Skill Map Wellgradedness Surmise Relation Formal Concept Analysis

Received: 29 January 2024

ZTFLH:

TP182

Fund:

National Natural Science Foundation of China(No.11971211,12171388)

Corresponding Authors: LI Jinhai, Ph.D., professor. His research interests include cognitive computing, granular computing, big data analysis, concept lattice and rough set.

About author:: ZHANG Rui, Master student. His research interests include formal concept analysis, gra-nular computing and knowledge space theory. ZHI Huilai, Ph.D., professor. His research interests include formal concept analysis, rough set and granular computing. SUN Wen, Ph.D., lecturer. His research interests include knowledge space theory, cognitive diagnostic theory and fuzzy set theory.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LI Jinhai
	ZHANG Rui
	ZHI Huilai
	SUN Wen

Cite this article:

LI Jinhai,ZHANG Rui,ZHI Huilai等. A Review of Knowledge Space Theory[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(2): 106-127.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202402002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I2/106

[1] DOIGNON J P, FALMAGNE J C.Spaces for the Assessment of Knowledge. International Journal of Man-Machine Studies, 1985, 23(2): 175-196.
[2] 刘艳花,杨贯中.基于扩展知识空间理论的技能自适应测试过程.计算机系统应用, 2010, 19(7): 69-73, 59.
(LIU Y H, YANG G Z.Adaptive Test Process Based on Extension of Knowledge Space Theory. Computer Systems and Applications, 2010, 19(7): 69-73, 59.)
[3] 刘译蓬. 基于知识空间理论的认知诊断自适应测试选题方法研究.硕士学位论文.锦州:渤海大学, 2019.
(LIU Y P.Research on Selection Method of Cognitive Diagnosis Adaptive Test Based on Knowledge Space Theory. Master Dissertation. Jinzhou, China: Bohai University, 2019.)
[4] 谈成群,谢深泉.超文本教学系统中学生知识的自适应测评研究.计算机工程与设计, 2007, 28(20): 5072-5075.
(TAN C Q, XIE S Q.Adaptive Assessment of Students' Knowledge in Hypertext Tutoring System. Computer Engineering and Design, 2007, 28(20): 5072-5075.)
[5] FALMAGNE J C, DOIGNON J P. Learning Spaces: Interdisciplinary Applied Mathematics. Berlin, Germany: Springer, 2011.
[6] HELLER J.Generalizing Quasi-Ordinal Knowledge Spaces to Poly-tomous Items. Journal of Mathematical Psychology, 2021, 101. DOI: 10.1016/j.jmp.2021.102515.
[7] LIKERT R.A Technique for the Measurement of Attitudes. Archi-ves of Psychology, 1932, 22(140): 5-55.
[8] BERTRAM D.Likert Scales[R/OL].[2023-12-21].https://pages.cpsc.ucalgary.ca/~saul/wiki/uploads/CPSC681/topic-dane-likert.pdf.
[9] FALMAGNE J C, DOIGNON J P.A Class of Stochastic Procedures for the Assessment of Knowledge. British Journal of Mathematical and Statistical Psychology, 1988, 41(1): 1-23.
[10] FALMAGNE J C, DOIGNON J P.A Markovian Procedure for Asse-ssing the State of a System. Journal of Mathematical Psychology, 1988, 32(3): 232-258.
[11] DOIGNON J P, FALMAGNE J C. Knowledge Spaces. Berlin, Ger-many: Springer, 1999.
[12] ALBERT D, LUKAS J.Knowledge Spaces: Theories, Empirical Research, and Applications. London, UK: Psychology Press, 1999.
[13] HELLER J, STEINER C, HOCKEMEYER C, et al. Competence-Based Knowledge Structures for Personalised Learning. Internatio-nal Journal on E-Learning, 2006, 5(1): 75-88.
[14] DOIGNON J P.Knowledge Spaces and Skill Assignments//FISCHER G H, LAMING D, eds. Contributions to Mathematical Psychology, Psychometrics, and Methodology. Berlin, Germany: Springer, 1994: 111-121.
[15] DÜNTSCH I, GEDIGA G. Skills and Knowledge Structures. Bri-tish Journal of Mathematical Psychology, 1995, 48(1): 9-27.
[16] MARTE B, STEINER C M, HELLER J, et al. Activity-and Taxonomy-Based Knowledge Representation Framework. International Journal of Knowledge and Learning, 2008, 4(2/3): 189-202.
[17] WILLE R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts//RIVAL I, ed. Ordered Sets. Berlin, Germany: Springer, 1982: 445-470.
[18] NICOTRA E F, SPOTO A. Connections and Dissimilarities among Formal Concept Analysis, Knowledge Space Theory and Cognitive Diagnostic Models in a Systemic Perspective//MINATI G, AB-RAM M R, PESSA E, eds. Systemics of Incompleteness and Quasi-Systems. Berlin, Germany: Springer, 2019: 235-241.
[19] RUSCH A, WILLE R. Knowledge Spaces and Formal Concept Ana-lysis//BOCK H H, POLASEK W, eds. Data Analysis and Information Systems: Statistical and Conceptual Approaches. Berlin, Germany: Springer, 1996: 427-436.
[20] ALBERT D, SCHREPP M.Structure and Design of an Intelligent Tutorial System Based on Skill Assignments//ALBERT D, LUKAS J, eds. Knowledge Spaces. London, UK: Psychology Press, 1999: 191-208.
[21] ARASASINGHAM R D, TAAGEPERA M, POTTER F, et al. Asse-ssing the Effect of Web-Based Learning Tools on Student Understanding of Stoichiometry Using Knowledge Space Theory. Journal of Chemical Education, 2005, 82(8): 1251-1262.
[22] FALMAGNE J C, KOPPEN M, VILLANO M, et al. Introduction to Knowledge Spaces: How to Build, Test, and Search Them. Psychological Review, 1990, 97(2): 201-224.
[23] KOPPEN M, DOIGNON J P.How to Build a Knowledge Space by Querying an Expert. Journal of Mathematical Psychology, 1990, 34(3): 311-331.
[24] KOPPEN M.Extracting Human Expertise for Constructing Know-ledge Spaces: An Algorithm. Journal of Mathematical Psychology, 1993, 44(3): 1-20.
[25] KAMBOURI M, KOPPEN M, VILLANO M, et al. Knowledge Asse-ssment: Tapping Human Expertise by the Query Routine. International Journal of Human-Computer Studies, 1994, 40(1): 119-151.
[26] DOWLING C E.Applying the Basis of a Knowledge Space for Controlling the Questioning of an Expert. Journal of Mathematical Psychology, 1993, 37(1): 21-48.
[27] SCHREPP M, HELD T.A Simulation Study Concerning the Effect of Errors on the Establishment of Knowledge Spaces by Querying Experts. Journal of Mathematical Psychology, 1995, 39(4): 376-382.
[28] COSYN E, THIERY N.A Practical Procedure to Build a Know-ledge Structure. Journal of Mathematical Psychology, 2000, 44(3): 383-407.
[29] STEFANUTTI L, KOPPEN M.A Procedure for the Incremental Construction of a Knowledge Space. Journal of Mathematical Psychology, 2003, 47(3): 265-277.
[30] ÜNLÜ A, SARGIN A.DAKS: An R Package for Data Analysis Me-thods in Knowledge Space Theory. Journal of Statistical Software, 2010, 37(2). DOI: 10.18637/jss.v037.i02.
[31] FLAMENT C.L'Analyse Booléenne de Questionnaire. Paris, Fran-ce: Mouton, 1976.
[32] BROWN F M. Boolean Reasoning. Boston, USA: Kluwer Acade-mic Publishers, 1990.
[33] VAN LEEUWE J F. Item Tree Analysis[J/OL].[2023-12-21]. https://psycnet.apa.org/record/1975-08628-001.
[34] SCHREPP M.Explorative Analysis of Empirical Data by Boolean Analysis of Questionaires. Zeitschrift für Psychologie, 2002, 210(2): 99-109.
[35] THEUNS P.A Dichotomization Method for Boolean Analysis of Quan-tifiable Co-occurrence Data//FISCHER G H, LAMING D, eds. Contributions to Mathematical Psychology Psychometrics, and Methodology. Berlin, Germany: Springer, 1994: 389-402.
[36] THEUNS P.Building a Knowledge Space via Boolean Analysis of Co-occurrence Data//DOWLING C E, ROBERTS F S, THEUNS P, eds. Recent Progress in Mathematical Psychology. London, UK: Psychology Press, 1998: 173-194.
[37] SCHREPP M.On the Empirical Construction of Implications Between Bi-valued Test Items. Mathematical Social Sciences, 1999, 38(3): 361-375.
[38] SARGIN A, ÜNLÜ A.Inductive Item Tree Analysis: Corrections, Improvements, and Comparisons. Mathematical Social Sciences, 2009, 58(3): 376-392.
[39] SCHREPP M.Extracting Knowledge Structures from Observed Data. British Journal of Mathematical and Statistical Psychology, 1999, 52(2): 213-224.
[40] DE CHIUSOLE D, STEFANUTTI L, SPOTO A.A Class of k-Mo-des Algorithms for Extracting Knowledge Structures from Data. Behavior Research Methods, 2017, 49(4): 1212-1226.
[41] HUANG Z X, NG M K.A Fuzzy k-Modes Algorithm for Clustering Categorical Data. IEEE Transactions on Fuzzy Systems, 1999, 7(4): 446-452.
[42] CHATURVEDI A, GREEN P E, CAROLL J D.k-Modes Clus-tering. Journal of Classification, 2001, 18(1): 35-55.
[43] SPOTO A, STEFANUTTI L, VIDOTTO G.Knowledge Space Theo-ry, Formal Concept Analysis, and Computerized Psychological Assessment. Behavior Research Methods, 2010, 42(1): 342-350.
[44] SPOTO A, STEFANUTTI L, VIDOTTO G.On the Unidentifiability of a Certain Class of Skill Map-Based Probabilistic Knowledge Structures. Journal of Mathematical Psychology, 2012, 56(4), 248-255.
[45] SUCK R.Skills First-An Alternative Approach to Construct Know-ledge Spaces. Journal of Mathematical Psychology, 2021, 101. DOI: 10.1016/j.jmp.2021.102517.
[46] SPOTO A, STEFANUTTI L, VIDOTTO G.An Iterative Procedure for Extracting Skill Maps from Data. Behavior Research Methods, 2016, 48(2): 729-741.
[47] 周银凤,李进金,冯丹露,等.形式背景下的学习路径与技能评估.模式识别与人工智能, 2021, 34(12): 1069-1084.
(ZHOU Y F, LI J J, FENG D L, et al. Learning Paths and Skills Assessment in Formal Context. Pattern Recognition and Artificial Intelligence, 2021, 34(12): 1069-1084.)
[48] 周银凤,李进金.形式背景下的技能约简与评估.计算机科学与探索, 2022, 16(3): 692-702.
(ZHOU Y F, LI J J.Skill Reduction and Assessment in Formal Context. Journal of Frontiers of Computer Science and Technology, 2022, 16(3): 692-702.)
[49] SUN W, LI J J, GE X, et al. Knowledge Structures Delineated by Fuzzy Skill Maps. Fuzzy Sets and Systems, 2021, 407: 50-66.
[50] MARTIN J L, WILEY J A.Algebraic Representations of Beliefs and Attitudes II: Microbelief Models for Dichotomous Belief Data. Sociological Methodology, 2000, 30(1): 123-164.
[51] SCHREPP M.About the Connection Between Knowledge Structures and Latent Class Models. Methodology, 2005, 1(3): 92-102.
[52] ÜNLÜ A.Estimation of Careless Error and Lucky Guess Probabilities for Dichotomous Test Items: A Psychometric Application of a Biometric Latent Class Model with Random Effects. Journal of Mathematical Psychology, 2006, 50(3): 309-328.
[53] TOIVONEN H.Sampling Large Databases for Association Rules//Proceedings of the 22nd International Conference on Very Large Data Bases. San Francisco, USA: Morgan Kaufmann Publishers, 1996: 134-145.
[54] HÁJEK P, HAVEL I, CHYTIL M. The GUHA Method of Automatic Hypotheses Determination. Computing, 1966, 1(4): 293-308.
[55] HÁJEK P, HAVRÁNEK T. On Generation of Inductive Hypotheses. International Journal of Man-Machine Studies, 1977, 9(4): 415-438.
[56] ÜNLÜ A, MALIK W A.Interactive Glyph Graphics of Multivariate Data in Psychometrics. Methodology, 2011, 7(4): 134-144.
[57] ÜNLÜ A, SARGIN A.Interactive Visualization of Assessment Data: The Software Package Mondrian. Applied Psychological Mea-surement, 2009, 33(2): 148-156.
[58] ÜNLÜ A, SARGIN A.Mosaic Displays for Combinatorial Psychometric Models//CERCHIELLO P, TARANTOLA C. Proc of the Classification and Data Analysis Group. Pavia, Italy: Pavia University Press, 2011: 52.
[59] DOWLING C E.On the Irredundant Generation of Knowledge Spaces. Journal of Mathematical Psychology, 1993, 37(1): 49-62.
[60] SCHREPP M.A Generalization of Knowledge Space Theory to Pro-blems with More Than Two Answer Alternatives. Journal of Mathematical Psychology, 1997, 41(3): 237-243.
[61] BARTL E, BELOHLAVEK R.Knowledge Spaces with Graded Know-ledge States. Information Sciences, 2011, 181(8): 1426-1439.
[62] STEFANUTTI L, ANSELMI P, DE CHIUSOLE D, et al. On the Polytomous Generalization of Knowledge Space Theory. Journal of Mathematical Psychology, 2020, 94. DOI: 10.1016/j.jmp.2019.102306.
[63] STEFANUTTI L.On the Assessment of Procedural Knowledge: From Problem Spaces to Knowledge Spaces. British Journal of Mathema-tical and Statistical Psychology, 2019, 72(2): 185-218.
[64] WANG B, LI J J, SUN W, et al. Notes on the Polytomous Gene-ralization of Knowledge Space Theory. Journal of Mathematical Psychology, 2022, 109. DOI: 10.1016/j.jmp.2022.102672.
[65] WANG B, LI J J, SUN W.CD-Polytomous Knowledge Spaces and Corresponding Polytomous Surmise Systems. British Journal of Mathe-matical and Statistical Psychology, 2023, 76(1): 87-105.
[66] GE X.On Galois Connections Between Polytomous Knowledge Stru-ctures and Polytomous Attributions. Journal of Mathematical Psychology, 2022, 110. DOI: 10.1016/j.jmp.2022.102708.
[67] 孙晓燕,李进金.基于程序性知识学习的项目状态转移函数与多分知识结构.模式识别与人工智能, 2022, 35(3): 223-242.
(SUN X Y, LI J J.Item State Transition Functions and Polytomous Knowledge Structures Based on Procedural Knowledge Learning. Pattern Recognition and Artificial Intelligence, 2022, 35(3): 223-242.)
[68] SUN W, LI J J, LIN F C, et al. Constructing Polytomous Know-ledge Structures from Fuzzy Skills. Fuzzy Sets and Systems, 2023, 461. DOI: 10.1016/j.fss.2022.09.003.
[69] DE CHIUSOLE D, SPOTO A, STEFANUTTI L.Extracting Partially Ordered Clusters from Ordinal Polytomous Data. Behavior Research Methods, 2020, 52: 503-520.
[70] DOIGNON J P, FALMAGNE J C. Knowledge Assessment: A Set-Theoretic Framework[M/OL].[2023-12-21].https://books.google.co.uk/books?redir_esc=y&hl=zh-CN&id=JmkoAQAAIAAJ&focus=searchwithinvolume&q=Knowledge+Assessment.
[71] DOIGNON J P.Probabilistic Assessment of Knowledge//ALBERT D, ed. Knowledge Structures. Berlin, Germany: Springer, 1994: 1-57.
[72] FALMAGNE J C.A Latent Trait Theory via a Stochastic Learning Theory for a Knowledge Space. Psychometrika, 1989, 54(2): 283-303.
[73] FALMAGNE J C. Probabilistic Knowledge Spaces: A Review//ROBERTS F, ed. Applications of Combinatorics and Graph Theory to the Biological and Social Sciences. Berlin, Germany: Springer, 1989: 95-101.
[74] COSYN E, UZUN H B.Note on Two Necessary and Sufficient Axioms for a Well-Graded Knowledge Space. Journal of Mathematical Psychology, 2009, 53(1): 40-42.
[75] DOIGNON J P. Learning Spaces, How to Build Them//Proc of the 12th International Conference on Formal Concept Analysis. Berlin, Germany: Springer, 2014: 1-14.
[76] DOIGNON J P, FALMAGNE J C.Knowledge Space and Learning Spaces[C/OL].[2023-12-23].https://arxiv.org/abs/1511.06757.
[77] JAMISON R E.Copoints in Antimatroids[C/OL].[2023-12-23].https://books.google.co.uk/books/about/Proceedings_of_the_Eleventh_Southeastern.html?id=tj2jygAACAAJ&redir_esc=y.
[78] JAMISON R E.A Perspective on Abstract Convexity: Classifying Alignments by Varieties//KAY D C, BREEN M, eds. Convexity and Related Combinatorial Geometry. New York, USA: Marcel Dekker, 1982: 113-150.
[79] KOPPEN M.On Alternative Representations for Knowledge Spaces. Mathematical Social Sciences, 1998, 36(2): 127-143.
[80] OVCHINNIKOV S V.Well-Graded Spaces of Valued Sets. Discrete Mathematics, 2002, 245(1/2/3): 217-233.
[81] BOGART K P.Preference Structures I: Distances Between Transitive Preference Relations. The Journal of Mathematical Sociology, 1973, 3(1): 49-67.
[82] SUCK R.Parsimonious Set Representations of Orders, A Generalization of the Interval Order Concept, and Knowledge Spaces. Discrete Applied Mathematics, 2003, 127(2): 373-386.
[83] EPPSTEIN D, FALMAGNE J C, OVCHINNIKOV S. Media and Well-Graded Families//EPPSTEN D, FALMAGNE J C, OVCHI-NNIKOV S, eds. Media Theory: Interdisciplinary Applied Mathematics. Berlin, Germany: Springer, 2008: 49-71.
[84] EPPSTEIN D, FALMAGNE J C, UZUN H.On Verifying and Engineering the Wellgradedness of a Union-Closed Family. Journal of Mathematical Psychology, 2009, 53(1): 34-39.
[85] OVCHINNIKOV S V.Well-Graded Families of Fuzzy Sets//Proc of the Annual Conference of the North American Fuzzy Information Processing Society Held Jointly with 5th World Conference on Soft Computing. Washington, USA: IEEE, 2015. DOI: 10.1109/NAFIPS-WConSC.2015.7284159.
[86] MATAYOSHI J.On the Properties of Well-Graded Partially Union-Closed Families. Journal of Mathematical Psychology, 2017, 80: 15-21.
[87] GIARLOTTA A, WATSON S.Well-Graded Families of NaP-Pre-ferences. Journal of Mathematical Psychology, 2017, 77: 21-28.
[88] DOIGNON J P, FALMAGNE J C.Well-Graded Families of Relations. Discrete Mathematics, 1997, 173(1/2/3): 35-44.
[89] MATAYOSHI J.Well-Graded Families and the Union-Closed Sets Conjecture. The Electronic Journal of Combinatorics, 2020, 27(1). DOI: https://doi.org/10.37236/8380.
[90] SUN W, LI J J, HE Z R, et al. Well-Graded Polytomous Know-ledge Structures. Journal of Mathematical Psychology, 2023, 114. DOI: 10.1016/j.jmp.2023.102770.
[91] WANG B, LI J J.Exploring Well-Gradedness in Polytomous Know-ledge Structures. Journal of Mathematical Psychology, 2024, 119. DOI: 10.1016/j.jmp.2024.102840.
[92] HELLER J.A Formal Framework for Characterizing Querying Algorithms. Journal of Mathematical Psychology, 2004, 48(1): 1-8.
[93] MÜLLER C E. A Procedure for Facilitating an Expert's Judgments on a Set of Rules//ROSKAM E E, ed. Mathematical Psychology in Progress. Berlin, Germany: Springer, 1989: 157-170.
[94] KOPPEN M G M. Ordinal Data Analysis: Biorder Representation and Knowledge Spaces. Nijmegen, Holland: Katholieke Universiteit Nijmegen, 1989.
[95] DAVEY B A, PRIESTLEY H A.Introduction to Lattices and Order. Cambridge, UK: Cambridge University Press, 1990.
[96] EDELMAN P H, JAMISON R E.The Theory of Convex Geometries. Geometriae Dedicata, 1985, 19(3): 247-270.
[97] VAN DE VEL M. The Theory of Convex Structures. Amsterdam, Holland: North-Holland Publishing, 1993.
[98] SCHREPP M, HELD T, ALBERT D.Component-Based Construction of Surmise Relations for Chess Problems//ALBERT D, LUKAS J, eds. Knowledge Spaces. London, UK: Psychology Press, 1999: 53-78.
[99] 陈惠琴,李进金,林宇静,等.技能背景下学习空间的判别方法和技能评估.南京大学学报(自然科学), 2023, 59(1): 107-119.)
(CHEN H Q, LI J J, LIN Y J, et al. Discrimination Method of Learning Space and Skills Assessment in Skill Context. Journal of Nanjing University(Natural Science), 2023, 59(1): 107-119.)
[100] GE X.On the Correspondence Between Granular Polytomous Spaces and Polytomous Surmising Functions. Journal of Mathematical Psychology, 2023, 113. DOI: 10.1016/j.jmp.2022.102743.
[101] BRANDT S, ALBERT D, HOCKEMEYER C.Surmise Relations Between Tests: Preliminary Results of the Mathematical Mode-ling. Electronic Notes in Discrete Mathematics, 1999, 2: 10-24.
[102] BRANDT S, ALBERT D, HOCKEMEYER C.Surmise Relations Between Tests-Mathematical Considerations. Discrete Applied Mathematics, 2003, 127(2): 221-239.
[103] ÜNLÜ A, BRANDT S, ALBERT D. Test Surmise Relations, Test Knowledge Structures, and Their Characterizations. Augsburg, Germany: Universität Augsburg, 2007.
[104] KOROSSY K.Modeling Knowledge as Competence and Perfor-mance//ALBERT D, LUKAS J, eds. Knowledge Spaces. London, UK: Psychology Press, 1999: 115-144.
[105] HELLER J, AUGUSTIN T, HOCKEMEYER C, et al. Recent Developments in Competence-Based Knowledge Space Theory//FALMAGNE J C, ALBERT D, DOBLE C, et al., eds. Know-ledge Spaces: Applications in Education. Berlin, Germany: Springer, 2013: 243-286.
[106] ROZEBOOM W, LORD F M, NOVICK M R, et al. Statistical Theories of Mental Test Scores. American Educational Research Journal, 1969, 6(1): 112-116.
[107] WAINER W, MESSICK S.Principals of Modern Psychological Measurement: A Festschrift for Frederic M. Lord. London, UK: Routledge, 2012.
[108] WAINER H, DORANS N J, FLAUGHER R, et al. Computerized Adaptive Testing: A Primer. London, UK: Routledge, 2000.
[109] WEISS D J, BOCK R D.New Horizons in Testing: Latent Trait Test Theory and Computerized Adaptive Testing. New York, USA: Academic Press, 1983.
[110] MARSHALL S P.Sequential Item Selection: Optimal and Heuristic Policies. Journal of Mathematical Psychology, 1981, 23(2): 134-152.
[111] ZHOU Y F, LI J J, WANG H K, et al. Skills and Fuzzy Know-ledge Structures. Journal of Intelligent and Fuzzy Systems, 2022, 42(3): 2629-2645.
[112] 李俊杰,杨贯中.基于技能的知识结构.微计算机信息, 2012, 28(2): 77-78, 58.
(LI J J, YANG G Z.Skill-Based Knowledge Structure. Microcomputer Information, 2012, 28(2): 77-78, 58.)
[113] GE X, LI J J.A Note on the Separability of Items in Knowledge Structures Delineated by Skill Multimaps. Journal of Mathematical Psychology, 2020, 98. DOI: 10.1016/j.jmp.2020.102427.
[114] HELLER J, ANSELMI P, STEFANUTTI L, et al. A Necessary and Sufficient Condition for Unique Skill Assessment. Journal of Mathematical Psychology, 2017, 79: 23-28.
[115] XU B C, LI J J.The Inclusion Degrees of Fuzzy Skill Maps and Knowledge Structures. Fuzzy Sets and Systems, 2023, 465. DOI: 10.1016/j.fss.2023.108540.
[116] 孙文. 模糊集在知识空间理论中的应用.博士学位论文.汕头:汕头大学, 2022.
(SUN W.Some Applications of Fuzzy Sets in Knowledge Space Theory. Ph.D. Dissertation. Shantou, China: Shantou University, 2022.)
[117] ANSELMI P, HELLER J, STEFANUTTI L, et al. Constructing, Improving, and Shortening Tests for Skill Assessment. Journal of Mathematical Psychology, 2022, 106. DOI: 10.1016/j.jmp.2021.102621.
[118] ANSELMI P, HELLER J, STEFANUTTI L, et al. Constructing Tests for Skill Assessment with Competence-Based Test Development. British Journal of Mathematical and Statistical Psychology, 2024. DOI: 10.1111/bmsp.12335.
[119] XIE X X, XU W H, LI J J.A Novel Concept-Cognitive Learning Method: A Perspective from Competences. Knowledge-Based Systems, 2023, 265. DOI: 10.1016/j.knosys.2023.110382.
[120] XU F F, MIAO D Q, YAO Y Y, et al. Analyzing Skill Sets with Or-Relation Tables in Knowledge Spaces//Proc of the 8th IEEE International Conference on Cognitive Informatics. Washington, USA: IEEE, 2009: 174-180.
[121] 高纯,王睿智.知识空间理论析取模型下最小技能集的生成.计算机科学与探索, 2010, 4(12): 1109.
(GAO C, WANG R Z.The Formation of Minimal Skill Set in Disjunctive Model of Knowledge Space Theory. Journal of Frontiers of Computer Science and Technology, 2010, 4(12): 1109-1114.)
[122] LIU G L.Rough Set Approaches in Knowledge Structures. International Journal of Approximate Reasoning, 2021, 138: 78-88.
[123] 杨桃丽,李进金,李招文,等.基于技能构建知识结构的两种变精度模型与技能子集约简.模式识别与人工智能, 2022, 35(8): 671-687.
(YANG T L, LI J J, LI Z W, et al. Two Kinds of Variable Precision Models Based on Skill for Constructing Knowledge Structures and Skill Subset Reduction. Pattern Recognition and Artificial Intelligence, 2022, 35(8): 671-687.)
[124] GEDIGA G, DÜNTSCH I. Skill Set Analysis in Knowledge Structures. British Journal of Mathematical and Statistical Psychology, 2002, 55(2): 361-384.
[125] HELLER J, REPITSCH C.Distributed Skill Functions and the Meshing of Knowledge Structures. Journal of Mathematical Psychology, 2008, 52(3): 147-157.
[126] HELLER J, ÜNLÜ A, ALBERT D.Skills, Competencies and Knowledge Structures//FALMAGNE J C, ALBERT D, DOBLE C, et al., eds. Knowledge Spaces: Applications in Education. Berlin, Germany: Springer, 2013: 229-242.
[127] HE Z R, SUN W.Competence-Based Skill Functions and Minimal Sets of Skills. Symmetry, 2022, 14(5). DOI: 10.3390/sym14050884.
[128] STEFANUTTI L, DE CHIUSOLE D.On the Assessment of Lear-ning in Competence Based Knowledge Space Theory. Journal of Mathematical Psychology, 2017, 80: 22-32.
[129] KOROSSY K.Extending the Theory of Knowledge Spaces: A Com-petence-Performance Approach. Zeitschrift Für Psychologie, 1997, 205(1): 53-82.
[130] KOROSSY K.Modellierung Von Wissen als Kompetenz und Performanz[C/OL].[2023-12-07].https://www.researchgate.net/publication/241027818.
[131] CHOMSKY N.Aspects of the Theory of Syntax. Cambridge, USA: MIT Press, 1965.
[132] KICKMEIER-RUST M D, ALBERT D, STEINER C. Lifelong Competence Development: On the Advantages of Formal Competence-Performance Modeling[C/OL].[2023-12-07]. https://core.ac.uk/download/pdf/55533858.pdf.
[133] VILLANO M. Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory//Proc of the International Conference on Intelligent Tutoring Systems. Berlin, Germany: Springer, 1992: 491-498.
[134] STEFANUTTI L, ROBUSTO E.Recovering a Probabilistic Know-ledge Structure by Constraining its Parameter Space. Psychometrika, 2009, 74(1): 83-96.
[135] HELLER J, WICKELMAIER F.Minimum Discrepancy Estimation in Probabilistic Knowledge Structures. Electronic Notes in Discrete Mathematics, 2013, 42: 49-56.
[136] ANSELMI P, STEFANUTTI L, DE CHIUSOLE D, et al. The Assessment of Knowledge and Learning in Competence Spaces: The Gain-Loss Model for Dependent Skills. British Journal of Mathematical and Statistical Psychology, 2017, 70(3): 457-479.
[137] SPOTO A, STEFANUTTI L.On the Necessary and Sufficient Con-ditions for Delineating Forward-and Backward-Graded Knowledge Structures from Skill Maps. Journal of Mathematical Psychology, 2020, 99. DOI: 10.1016/j.jmp.2020.102451.
[138] MATAYOSHI J, UZUN H.Learning, Forgetting,the Correlation of Knowledge in Knowledge Space Theory. Journal of Mathe-matical Psychology, 2022, 109. DOI: 10.1016/j.jmp.2022.102674.
[139] JUNKER B W, SIJTSMA K.Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory. Applied Psychological Measurement, 2001, 25(3): 258-272.
[140] MARIS E.Estimating Multiple Classification Latent Class Models. Psychometrika, 1999, 64(2): 187-212.
[141] FALMAGNE J C. Finite Markov Learning Models for Knowledge Structures//FISCHER G H, LAMING D, eds. Contributions to Mathematical Psychology, Psychometrics, and Methodology. Berlin, Germany: Springer, 1994: 75-89.
[142] FRIES S.Empirical Validation of a Markovian Learning Model for Knowledge Structures. Journal of Mathematical Psychology, 1997, 41(1): 65-70.
[143] KEMENY J G, SNELL J L. Finite Markov Chains: With a New Appendix "Generalization of a Fundamental Matrix". Berlin, Germany: Springer, 1976.
[144] PARZEN E. Stochastic Processes. San Francisco, USA: Holden-Day, 1962.
[145] ROBUSTO E, STEFANUTTI L, ANSELMI P.The Gain-Loss Model: A Probabilistic Skill Multimap Model for Assessing Learning Processes. Journal of Educational Measurement, 2010, 47(3): 373-394.
[146] STEFANUTTI L, ANSELMI P, ROBUSTO E.Assessing Learning Processes with the Gain-Loss Model. Behavior Research Methods, 2010, 43: 66-76.
[147] CORBETT A T, ANDERSON J R.Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 1994, 4(4): 253-278.
[148] YUDELSON M V, KOEDINGER K R, GORDON G J. Individualized Bayesian Knowledge Tracing Models//Proc of the 16th International Conference on Artificial Intelligence in Education. Berlin, Germany: Springer, 2013: 171-180.
[149] ANSELMI P, ROBUSTO E, STEFANUTTI L.A Procedure for Iden-tifying the Best Skill Multimap in the Gain-Loss Model. Electronic Notes in Discrete Mathematics, 2013, 42: 9-16.
[150] DE CHIUSOLE D, ANSELMI P, STEFANUTTI L, et al. The Gain-Loss Model: Bias of the Parameter Estimates. Electronic Notes in Discrete Mathematics, 2013, 42: 33-40.
[151] ANDERSEN E B.Latent Structure Analysis: A Survey. Scandinavian Journal of Statistics, 1982, 9(1): 1-12.
[152] HAGENAARS J A, MCCUTCHEON A L. Applied Latent Class Analysis. Cambridge, UK: Cambridge University Press, 2002.
[153] VERMUNT J K, MAGIDSON J. "Latent Class Analysis". The Sage Encyclopedia of Social Sciences Research Methods, London, England: Sage Publications, 2004: 549-553.
[154] HEINEN T. Latent Class and Discrete Latent Trait Models: Similarities and Differences. London, UK: Sage Publications, 1996.
[155] SCHREPP M.Properties of the Correlational Agreement Coefficient: A Comment to Ünlü and Albert(2004). Mathematical Social Sciences, 2006, 51(1): 117-123.
[156] VERMUNT J K. Log-Linear Models for Event Histories. Thousand Oaks, USA: Sage Publications, 1997.
[157] MACDONALD I L, ZUCCHINI W. Hidden Markov Models and Other Types of Models for Discrete-Valued Time Series. Boca Raton, USA: Chapman and Hall, 1997.
[158] STEFANUTTI L, DE CHIUSOLE D, ANSELMI P, et al. Extending the Basic Local Independence Model to Polytomous Data. Psychometrika, 2020, 85(3): 684-715.
[159] GANTER B, BEDEK M, HELLER J, et al. An Invitation to Know-ledge Space Theory//Proc of the 14th International Conference on Formal Concept Analysis. Berlin, Germany: Springer, 2017: 3-19.
[160] 李进金,孙文.知识空间,形式背景和知识基.西北大学学报(自然科学版), 2019, 49(4): 517-526.
(LI J J, SUN W. KnowledgeSpace, Formal Context and Know-ledge Base. Journal of Northwest University (Natural Science Edi-tion), 2019, 49(4): 517-526.)
[161] OJEDA-HERNÁNDEZ M, PÉREZ-GÁMEZ F, LÓPEZ-RODRÍ-GUEZ D, et al. Minimal Generators from Positive and Negative Attributes: Analysing the Knowledge Space of a Mathematics Course. International Journal of Computational Intelligence Systems, 2022, 15. DOI: 10.1007/s44196-022-00123-3.
[162] 林宇静,李进金,陈惠琴.形式背景下的多分知识结构与学习路径.山东大学学报(理学版), 2023, 58(9): 114-126.
(LIN Y J, LI J J, CHEN H Q.Polytomous Knowledge Structure and Learning Path in Formal Context. Journal of Shandong University(Natural Science), 2023, 58(9): 114-126.)
[163] 冯丹露,李进金,李招文,等.模糊形式背景下的技能层约简与前级 (后级)知识结构的充要条件.模式识别与人工智能, 2023, 36(5): 383-406.
(FENG D L, LI J J, LI Z W,et al. Skill Level Reduction and Necessary and Sufficient Conditions of Forward-Graded(Backward-Graded) Knowledge Structure in Fuzzy Formal Context. Pa-ttern Recognition and Artificial Intelligence, 2023, 36(5): 383-406.)
[164] 王大利,许晴媛,李进金,等.知识点网络下的知识评估和学习路径选择.南京大学学报(自然科学), 2023, 59(4): 629-643.
(WANG D L, XU Q Y, LI J J, et al. Knowledge Assessment and Learning Paths Selection under Knowledge-Point Network. Journal of Nanjing University(Natural Science), 2023, 59(4): 629-643.)
[165] 于亚琪,赵思雨,魏玲.面向属性概念格的概念约简及其在知识空间理论中的应用.西北大学学报(自然科学版), 2023, 53(5): 812-820.
(YU Y Q, ZHAO S Y, WEI L.Concept Reduction of Property-Oriented Concept Lattices and Its Application in Knowledge Space Theory. Journal of Northwest University(Natural Science Edition), 2023, 53(5): 812-820.)
[166] PAWLAK Z. RoughSets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[167] YAO Y Y, MIAO D Q, XU F F. Granular Structures and Appro-ximations in Rough Sets and Knowledge Spaces//ABRAHAM A, FALCÓN R, BELLO R, eds. Rough Set Theory: A True Landmark in Data Analysis. Berlin, Germany: Springer, 2009: 71-84.
[168] 王国胤,姚一豫,于洪.粗糙集理论与应用研究综述.计算机学报, 2009, 32(7): 1229-1246.
(WANG G Y, YAO Y Y, YU H.A Survey on Rough Set Theory and Applications. Chinese Journal of Computers, 2009, 32(7): 1229-1246.)
[169] 智慧来,李金海.面向知识结构分析的模糊概念格模型[J/OL].[2023-12-21]. http://www.jos.org.cn/1000-9825/6899.html.
(ZHI H L, LI J H. Oriented Fuzzy Concept Lattice Models for Knowledge Structure Analysis[J/OL].[2023-12-21]. http://www.jos.org.cn/1000-9825/6899.html
[170] XU B C, LI J J, SUN W, et al. On Delineating Forward-and Backward-Graded Knowledge Structures from Fuzzy Skill Maps. Journal of Mathematical Psychology, 2023, 117. DOI: 10.1016/j.jmp.2023.102819.
[171] DOBLE C, MATAYOSHI J, COSYN E, et al. A Data-Based Simu-lation Study of Reliability for an Adaptive Assessment Based on Knowledge Space Theory. International Journal of Artificial Inte-lligence in Education, 2019, 29(2): 258-282.
[172] DOWLING C E, HOCKEMEYER C, LUDWIG A H. Adaptive Assessment and Training Using the Neighbourhood of Knowledge States//Proc of the 3rd International Conference on Intelligent Tutoring Systems. Berlin, Germany: Springer, 1996: 578-586.
[173] NWANA H S. Intelligent Tutoring Systems: An Overview. Artificial Intelligence Review, 1990, 4(4): 251-277.
[174] HOCKEMEYER C, HELD T, ALBERT D. RATH-A Relational Adaptive Tutoring Hypertext WWW-Environment Based on Know-ledge Space Theory//Proc of the 4th International Conference on Computer Aided Learning and Instruction. Berlin, Germany: Springer, 1998: 417-423.
[175] CONLAN O, HOCKEMEYER C, WADE V, et al. Metadata Driven Approaches to Facilitate Adaptivity in Personalized eLearning Systems. The Journal of Information and Systems in Education, 2002, 1(1): 38-44.
[176] DE CHIUSOLE D, STEFANUTTI L, ANSELMI P, et al. Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Know-ledge Space Theory. International Journal of Artificial Intelligence in Education, 2020, 30(4): 668-700.
[177] FANG Y, REN Z, HU X, et al. A Meta-Analysis of the Effectiveness of ALEKS on Learning. Educational Psychology, 2019, 39(10): 1278-1292.
[178] DE CHIUSOLE D, STEFANUTTI L, ANSELMI P, et al. Testing the Actual Equivalence of Automatically Generated Items. Behavior Research Methods, 2018, 50(1): 39-56.
[179] FALMAGNE J C, COSYN E, DOBLE C, et al. Assessing Mathematical Knowledge in a Learning Space: Validity and/or Reliability[C/OL].[2023-12-21]. https://www.stat.cmu.edu/~brian/NCME07/Validity_in_L_Spaces.pdf
[180] DIBELLO L V, STOUT W. Guest Editors' Introduction and Overview: IRT-Based Cognitive Diagnostic Models and Related Me-thods. Journal of Educational Measurement, 2007, 44 (4): 285-291.
[181] WESIAK G. Ordering Inductive Reasoning Tests for Adaptive Know-ledge Assessments: An Application of Surmise Relations Between Tests. Steiermark, Austria: University of Graz, 2003.
[182] TAAGEPERA M, POTTER F, MILLER G E, et al. Mapping Stu-dents' Thinking Patterns by the Use of the Knowledge Space Theory. International Journal of Science Education, 1997, 19(3): 283-302.
[183] TAAGEPERA M, NOORI S. Mapping Students' Thinking Patterns in Learning Organic Chemistry by the Use of Knowledge Space Theory. Journal of Chemical Education, 2000, 77(9). DOI: 10.1021/ED077P1224.
[184] ARASASINGHAM R D, TAAGEPERA M, POTTER F, et al. Using Knowledge Space Theory to Assess Student Understanding of Stoichiometry. Journal of Chemical Education, 2004, 81(10): 1517-1523.
[185] TóTH Z. Mapping Students' Knowledge Structure in Understan-ding Density, Mass Percent, Molar Mass, Molar Volume and Their Application in Calculations by the Use of the Knowledge Space Theory. Chemistry Education Research and Practice, 2007, 8(4): 376-389.
[186] TóTH Z, SEBESTYéN A. Relationship Between Students' Know-ledge Structure and Problem-Solving Strategy in Stoichiometric Problems Based on the Chemical Equation. International Journal of Physics and Chemistry Education, 2009, 1(1): 8-20.
[187] TAAGEPERA M, ARASASINGHAM R D. Using Knowledge Space Theory to Assess Student Understanding of Chemistry // FALMAGNE J C, ALBERT D, DOBLE C, et al., eds. Knowledge Spaces: Applications in Education. Berlin, Germany: Springer, 2013: 115-128.
[188] SEGEDINAC M T, HORVAT S, RODIC┴' D, et al. Using Know-ledge Space Theory to Compare Expected and Real Knowledge Spaces in Learning Stoichiometry. Chemistry Education Research and Practice, 2018, 19(3): 670-680.
[189] TóTH Z, KISS E. Using Particulate Drawings to Study 13-17 Year Olds' Understanding of Physical and Chemical Composition of Matter as Well as the State of Matter. Practice and Theory in Systems of Education, 2006, 1(1): 109-125.
[190] VAARIK A, TAAGEPERA M, TAMM L. Following the Logic of Student Thinking Patterns about Atomic Orbital Structures. Journal of Baltic Science Education, 2008, 7(1): 27-36.
[191] ALBERT D, HOCKEMEYER C. Adaptive and Dynamic Hypertext Tutoring Systems Based on Knowledge Space Theory[C/OL]. [2023-12-21]. https://telearn.hal.science/file/index/docid/190402/filename/Albert-Dietrich-1997.pdf.
[192] 何庆辉,麦裕华.基于知识空间理论的高一学生离子反应关键学习路径.化学教学, 2018(7): 12-17.
(HE Q H, MAI Y H. Key Study Path of Ionic Reactions for Grade 1 High School Students Based on Knowledge Space Theory. Education in Chemistry, 2018(7): 12-17.)
[193] 麦裕华,何庆辉,肖信.基于知识空间理论的高中生科学原理学习分析——以氧化还原反应为例.化学教育(中英文), 2018, 39(19): 34-40.
(MAI Y H, HE Q H, XIAO X. Analysis of Senior High School Students' Learning of Scientific Principles Based on the Know-ledge Space Theory: A Case of Oxidation-Reduction Reaction. Chinese Journal of Chemical Education, 2018, 39(19): 34-40.)
[194] 陈文梅,陈博,田欣,等.应用知识空间理论探究中学生的关键学习路径——以九年级“化学方程式”学习为例.化学教学, 2022(9): 24-28.
(CHEN W M, CHEN B, TIAN X, et al. Exploring Middle School Students' Key Learning Path by Utilizing Knowledge Space Theory: Take the Study of Chemical Equations in Grade 9 as an Example. Education in Chemistry, 2022(9): 24-28.)
[195] 陈东晓,李进金.基于知识空间理论的微积分关键学习路径描述.黑龙江教育(高教研究与评估), 2023(4): 28-31.
(CHEN D X, LI J J. Description of Calculus Key Learning Path Based on Knowledge Space Theory. Heilongjiang Education(Research and Evaluation of Higher Education), 2023(4): 28-31.)
[196] 孙波,傅骞.扩展知识空间理论研究.中国电化教育, 2004(4): 74-77.
(SUN B, FU Q. Research on the Extension of Knowledge Space Theory. China Educational Technology, 2004(4): 74-77.)
[197] 周弦.ICAI 中基于知识空间理论的知识结构模型.硕士学位论文.湘潭:湘潭大学, 2005.
(ZHOU X. A Knowledge Structure Model Based on Knowledge Space Theory in ICAI. Master Dissertation. Xiangtan, China: Xiangtan University, 2005.)
[198] 周弦,谢深泉.基于知识空间理论的自适应测试过程.计算机应用, 2007, 27(S1): 68-69, 72.
(ZHOU X, XIE S Q. Adaptive Testing Process Based on Know-ledge Space Theory. Journal of Computer Applications, 2007, 27(S1): 68-69, 72.)
[199] CHATZOPOULOU D I, ECONOMIDES A A. Adaptive Assessment of Student's Knowledge in Programming Courses. Journal of Computer Assisted Learning, 2010, 26(4): 258-269.
[200] GATHITU L N. Examination Representation Using Adaptive Testing: Application in Interview Process. Master Dissertation. Nairobi, Kenya: University of Nairobi, 2010.
[201] WU H M, KUO B C, YANG J M. Evaluating Knowledge Structure-Based Adaptive Testing Algorithms and System Development. Journal of Educational Technology and Society, 2012, 15(2): 73-88.
[202] 李爽.基于知识处理的测评系统的研究与应用.硕士学位论文.大连:大连海事大学, 2012.
(LI S. Research and Application of Knowledge-Processing-Based Test System. Master Dissertation. Dalian, China: Dalian Maritime University, 2012.)
[203] SITTHISAK O, GILBERT L, ALBERT D. Learning in Moodle Using Competence-Based Knowledge Space Theory and IMS QTI // Proc of the International Computer Science and Engineering Confe-rence. Washington, USA: IEEE, 2013: 53-57.
[204] SITTHISAK O, GILBERT L, ALBERT D. Adaptive Learning Using an Integration of Competence Model with Knowledge Space Theory // Proc of the 2nd International Conference on Advanced Applied Informatics. Washington, USA: IEEE, 2013: 199-202.
[205] 其勒格尔.基于KST的学习诊断模型在《大学计算机基础》课程中的实践研究.硕士学位论文.呼和浩特:内蒙古师范大学, 2019.
(CHELGER. Practical Research on KST-Based Learning Diagnosis Model in the Course of University Computer Foundation. Master Dissertation. Hohhot, China: Inner Mongolia Normal University, 2019.)
[206] 赵建康.自适应选题和自动阅卷的研究与实现.硕士学位论文.南京:南京师范大学, 2020.
(ZHAO J K. Research and Implementation of Adaptive Topic Selection and Automatic Grading. Master Dissertation. Nanjing, China: Nanjing Normal University, 2020.)
[207] 赵宇航.自适应测试及试题推荐系统的研究与实现.硕士学位论文.南京:南京师范大学, 2021.
(ZHAO Y H. Research and Implementation of Adaptive Testing and Question Recommendation System. Master Dissertation. Nanjing, China: Nanjing Normal University, 2021.)
[208] 孙贝,杨贯中.基于猜测概率和失误概率的学习诊断模型.计算机工程与科学, 2010, 32(4): 154-158.
(SUN B, YANG G Z. A Learning Diagnostic Model Based on the Guess Probability and the Misplay Probability. Computer Engineering and Science, 2010, 32(4): 154-158.)
[209] RONG Q, KONG W R, XIAO Y J, et al. An Adaptive Testing Approach for Competence Using Competence-Based Knowledge Space Theory // Proc of the International Conference on Smart Learning Environments. Singapore, Singapore: Springer Nature Singapore, 2023: 158-163.
[210] DE CHIUSOLE D, SPINOSO M, ANSELMI P, et al. PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training. Brain Sciences, 2024, 14(2). DOI: 10.3390/brainsci14020122.
[211] QU K S, ZHAI Y H, LIANG J Y, et al. Study of Decision Implications Based on Formal Concept Analysis. International Journal of General Systems, 2007, 36(2): 147-156.
[212] XU W H, LI W T. Granular Computing Approach to Two-Way Learning Based on Formal Concept Analysis in Fuzzy Datasets. IEEE Transactions on Cybernetics, 2014, 46(2): 366-379.
[213] NEWELL A, SIMON H A. Human Problem Solving. Englewood Cliffs, USA: Prentice-Hall, 1972.
[214] EBBINGHAUS H. Memory: A Contribution to Experimental Psychology. New York, USA: Columbia University, 1913.