您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(理学版)》

《山东大学学报(理学版)》 ›› 2025, Vol. 60 ›› Issue (7): 116-130.doi: 10.6040/j.issn.1671-9352.7.2024.122

• • 上一篇    

基于FT-粗糙集构建知识结构与寻找学习路径方法

周缪娟1,黄韩亮1,2*,张纪平3,李进金1,3   

  1. 1.闽南师范大学数学与统计学院, 福建 漳州 363000;2.福建省粒计算及其应用重点实验室, 福建 漳州 363000;3.泉州师范学院数学与计算机科学学院, 福建 泉州 362000
  • 发布日期:2025-07-01
  • 通讯作者: 黄韩亮(1980— ),男,教授,硕士生导师,博士,研究方向为模糊集理论及其应用. E-mail:huanghl@mnnu.edu.cn
  • 作者简介:周缪娟(2001— ),女,硕士研究生,研究方向为粗糙集与知识空间理论. E-mail:zhou.2001@qq.com*通信作者:黄韩亮(1980— ),男,教授,硕士生导师,博士,研究方向为模糊集理论及其应用. E-mail:huanghl@mnnu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(12271191,11871259);福建省自然科学基金资助项目(2023J01122,2023J01125,2023J05175,2022J01306,2022J05169,2021J01984);福建省2022本科高校教育教学研究项目(FBJG20220091)

Method for constructing knowledge structures and finding learning paths based on FT-rough set

ZHOU Miaojuan1, HUANG Hanliang1,2*, ZHANG Jiping3, LI Jinjin1,3   

  1. 1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian, China;
    2. Fujian Key Laboratory of Granular Computing and Applications, Zhangzhou 363000, Fujian, China;
    3. School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, Fujian, China
  • Published:2025-07-01

摘要: 提出FT-粗糙集下构建知识结构的方法,讨论如何对学习者进行技能评估和学习路径选择。在模糊近似空间中,利用FT-粗糙集的上逆和下逆模型构建知识结构并研究了知识结构的性质。在已知学习者的知识状态的情形下对学习者的技能的掌握情况进行评估,并给出学习路径图及其算法,通过教学实例说明算法的有效性和可行性。

关键词: 知识空间理论, FT-粗糙集, 知识结构, 技能评估, 学习路径

Abstract: The method of constructing knowledge structures under FT-rough set is proposed, and how to evaluate learners skills and select learning paths is discussed. In fuzzy approximation space, knowledge structures are constructed using the upper and lower inverse models of FT-rough set, and their properties are studied. The mastery of learners skills is evaluated under the condition that their knowledge state is known, and learning paths diagram and its algorithm are provided. The effectiveness and feasibilities of the proposed algorithm are verified by teaching examples.

Key words: knowledge space theory, FT-rough set, knowledge structures, skill assessment, learning path

中图分类号: 

  • TP182
[1] DOIGNON J P, FALMAGNE J C. Spaces for the assessment of knowledge[J]. International Journal of Man-Machine Studies, 1985, 23(2):175-196.
[2] ANSELMI P, STEFANUTTI L, DECHIUSOLE D, et al. The assessment of knowledge and learning in competence spaces: the gain-loss model for dependent skills[J]. British Journal of Mathematical and Statistical Psychology, 2017, 70(3):457-479.
[3] KAMBOURI M, KOPPEN M, VILLANO M, et al. Knowledge assessment: tapping human expertise by the query routine[J]. International Journal of Human-Computer Studies, 1994, 40(1):119-151.
[4] FALMAGNE J C, DOIGNON J P. Learningspaces: interdisciplinary applied mathematics[J]. Berlin: Springer, 2011:22-112.
[5] STEFANUTTI L. On the assessment of procedural knowledge: from problem spaces to knowledge spaces[J]. British Journal of Mathematical and Statistical Psychology, 2019, 72(2):185-218.
[6] HOCKEMEYER C, ALBERT D. The adaptive tutoring system rath-a prototype [C] // ICL99 Workshop Interactive Computer Aided Learning Tools and Applications. Villach: Carinthia Tech Institute, 1999:54-78.
[7] COSYN E, DOBLE C, FALMAGNE J C, et al. Assessing mathematical knowledge in a learning space[M] // FALMAGNE J C, ALBERT D, DOBLE C, et al. Knowledge Spaces. Berlin: Springer, 2013:27-50.
[8] 张治,刘小龙,余明华,等. 研究型课程自适应学习系统: 理念、策略与实践[J]. 中国电化教育,2018(4):119-130. ZHANG Zhi, LIU Xiaolong, YU Minghua, et al. Adaptive learning systems for research-based programs: concepts, strategies, and practices[J]. China Educational Technology, 2018(4):119-130.
[9] FALMAGNE J C, KOPPEN M, VILLANO M, et al. Introduction to knowledge spaces: how to build, test, and search them[J]. Psychological Review, 1990, 97(2):201-224.
[10] SCHREPP M. A method for the analysis of hierarchical dependencies between items of a questionnaire[J]. Methods of Psychological Research Online, 2003, 8(1):43-79.
[11] DOIGNON J P. Knowledge spaces and skill assignments[M] //FISCHER G H, LAMING D. Contributions to Mathematical Psychology, Psychometrics, and Methodology. Berlin: Springer, 1994:111-121.
[12] SUN Wen, LI Jinjin, GE Xun, et al. Knowledge structures delineated by fuzzy skill maps[J]. Fuzzy Sets and Systems, 2021, 407:50-66.
[13] HELLER J, ANSELMI P, STEFANUTTI L, et al. A necessary and sufficient condition for unique skill assessment[J]. Journal of Mathematical Psychology, 2017, 79:23-28.
[14] 何秋红,孙文,郑文彬,等. 学习空间理论的ACM竞赛关键学习路径算法[J]. 山西大学学报(自然科学版),2020,43(4):828-837. HE Qiuhong, SUN Wen, ZHENG Wenbin, et al. An algorithm for critical learning path of ACM contest based on learning space theory[J]. Journal of Shanxi University(Natural Science Edition), 2020, 43(4):828-837.
[15] 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[J]. 黑龙江教育(高教研究与评估),2023(4):28-31. CHEN Dongxiao, LI Jinjin. Description of calculus key learning path based on knowledge space theory[J]. Heilongjiang Education(Research and Evaluation of Higher Education), 2023(4):28-31.
[16] RUSCH A, WILLE R. Knowledge spaces and formal concept analysis[C] //BOCK H H, POLASEK W. Data Analysis and Information Systems. Berlin: Springer, 1996:427-436.
[17] NICOTRA E F, SPOTO A. Connections and dissimilarities among formal concept analysis, knowledge space theory and cognitive diagnostic models in a systemic perspective[M] //MINATI G, ABRAM M R, PESSA E. Systemics of Incompleteness and Quasi-systems, Belin: Springer, 2019:235-241.
[18] XIE Xiaoxian, XU Weihua, LI Jinjin. A novel concept-cognitive learning method: a perspective from competences[J]. Knowledge-Based Systems, 2023, 265:110382.
[19] HELLER J, STEFANUTTI L, ANSELMI P, et al. On the link between cognitive diagnostic models and knowledge space theory[J]. Psychometrika, 2015, 80(4):995-1019.
[20] PAWLAK Z. Rough sets[J]. International Journal of Computer Information Science, 1982, 11(5):341-356.
[21] DÜNTSCH I, GEDIGA G. Skills and knowledge structures[J]. British Journal of Mathematical and Statistical Psychology, 1995, 48(1):9-27.
[22] YAO Yiyu, MIAO Duoqian, XU Feifei. Granular structures and approximations in rough sets and knowledge spaces[M] //ABRAHAM A, FALCÓN R, BELLO R. Rough Set Theory: A True Landmark in Data Analysis. Berlin: Springer, 2009:71-84.
[23] 王国胤,姚一豫,于洪. 粗糙集理论与应用研究综述[J]. 计算机学报,2009,32(7):1229-1246. WANG Guoyin, YAO Yiyu, YU Hong. A survey on rough set theory and applications[J]. Chinese Journal of Computers, 2009, 32(7):1229-1246.
[24] LIU Guilong. Rough set approaches in knowledge structures[J]. International Journal of Approximate Reasoning, 2021, 138:78-88.
[25] 高纯,王睿智. 知识空间理论析取模型下最小技能集的生成[J]. 计算机科学与探索,2010,4(12):1109-1114. GAO Chun, WANG Ruizhi. The formation of minimal skill set in disjunctive model of knowledge space theory[J]. Journal of Frontiers of Computer Science and Technology, 2010, 4(12):1109-1114.
[26] 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[J]. 模式识别与人工智能, 2022,35(8):671-687. YANG Taoli, LI Jinjin, LI Zhaowen, et al. Two kinds of variable precision models based on skill for constructing knowledge structures and skill subset reduction[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(8):671-687.
[27] XU Bochi, LI Jinjin, SUN Wen, et al. Ondelineating forward-and backward-graded knowledge structures from fuzzy skill maps[J]. Journal of Mathematical Psychology, 2023, 117:102819.
[28] DAVVAZ B. A short note on algebraic T-rough sets[J]. Information Sciences, 2008, 178(16):3247-3252.
[29] 张纪平,周缪娟,李进金. FT-粗糙集模型的一些性质[J]. 泉州师范学院学报,2024,42(2): 1-9. ZHANG Jiping, ZHOU Miaojuan, LI Jinjin. Some properties of the FT-rough set model[J]. Journal of Quanzhou Normal College, 2024, 42(2):1-9.
[30] ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3):338-353.
[31] 杨海龙. 双论域粗糙集理论与方法[M]. 北京:科学出版社,2016:33-33. YANG Hailong. Roughset theory and methods for bi-theoretical domains[M]. Beijing: Science Press, 2016:33-33.
[32] EPPSTEIN D, FALMAGNE J C, UZUN H B. On verifying and engineering the well gradedness of a union-closed family[J]. Journal of Mathematical Psychology, 2009, 53(1):34-39.
[33] 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[J]. 模式识别与人工智能,2021,34(12):1069-1084. ZHOU Yinfeng, LI Jinjing, FENG Danlu, et al. Learning paths and skills assessment in formal context[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(12):1069-1084.
[34] 赵青,韩光明,梁娟. 技能映射下学习空间判别定理[J]. 闽南师范大学学报(自然科学版),2021,34(1):20-25. ZHAO Qing, HAN Guangming, LIANG Juan. Learning spatial discrimination theorems under skill mapping[J]. Journal of Minnan Normal University(Natural Science Edition), 2021, 34(1):20-25.
[35] FALMAGNE J C, DOIGNON J P. A class of stochastic procedures for the assessment of knowledge[J]. British Journal of Mathematical and Statistical Psychology, 1988, 41(1):1-23.
[1] 张纪平,吴伟志,周缪娟,李进金. 分布式串行模糊关系和模糊知识结构的网格化[J]. 《山东大学学报(理学版)》, 2025, 60(5): 116-124.
[2] 张纪平,吴伟志,周缪娟,李进金. 模糊知识结构的一些性质[J]. 《山东大学学报(理学版)》, 2025, 60(1): 91-100.
[3] 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[J]. 《山东大学学报(理学版)》, 2023, 58(9): 114-126.
[4] 曹喜燕,林福财,金铭,李进金. 程序性知识评估: 从问题空间到可辨识知识结构[J]. 《山东大学学报(理学版)》, 2023, 58(7): 97-105, 114.
[5] 何秋红,李进金,周银凤,吴靖. 面向属性概念在自适应技能测评中的实践应用[J]. 《山东大学学报(理学版)》, 2023, 58(12): 63-76.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!