Kolb's experiential learning model: critique from a modelling perspective

被引:82
作者
Bergsteiner, Harald [1 ]
Avery, Gayle C. [1 ]
Neumann, Ruth [1 ]
机构
[1] Macquarie Univ, Inst Sustainable Leadership, Macquarie Grad Sch Management, N Ryde, NSW 2109, Australia
关键词
experiential learning theory; Kolb's learning model; models; STYLES;
D O I
10.1080/01580370903534355
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Kolb's experiential learning theory has been widely influential in adult learning. The theory and associated instruments continue to be criticized, but rarely is the graphical model itself examined. This is significant because models can aid scientific understanding and progress, as well as theory development and research. Applying accepted modelling and categorization criteria to Kolb's basic model reveals fundamental graphic syntax errors, a failure to meet modellers' graphic sufficiency and simplification tests, categorization and definitional problems relating to learning activities and typologies, misconstrued bi-polarities and flawed logic. We propose guidelines for recasting the model with a view to overcoming these weaknesses, guiding future research and theory development, and starting to integrate the disparate field of experiential learning.
引用
收藏
页码:29 / 46
页数:18
相关论文
共 50 条
[31]   Parameters Describing Student Learning Environments Through Experiential Learning Technologies for Entrepreneurial Creativity: A Study From a B School [J].
Lakshmi, Dara Vijaya ;
Saradha, M. ;
Karthik, R. ;
Kumar, M. Sandeep ;
Babu, J. Chinna ;
Vilcekova, Lucia .
JOURNAL OF CASES ON INFORMATION TECHNOLOGY, 2023, 25 (01)
[32]   A Brief Tour of Deep Learning from a Statistical Perspective [J].
Nalisnick, Eric ;
Smyth, Padhraic ;
Tran, Dustin .
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 2023, 10 :219-246
[33]   Language learning from the perspective of nonlinear dynamic systems [J].
Hohenberger, Annette ;
Peltzer-Karpf, Annemarie .
LINGUISTICS, 2009, 47 (02) :481-511
[34]   Learning from failure: A context-informed perspective on RCTs [J].
Coldwell, Mike ;
Moore, Nick .
BRITISH EDUCATIONAL RESEARCH JOURNAL, 2024, :1043-1063
[35]   Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective [J].
Piga, Dario ;
Mejari, Manas ;
Forgione, Marco .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2022, 67 (10) :5471-5478
[36]   Pathways to sustainable fuel design from a probabilistic deep learning perspective [J].
Freitas, Rodolfo S. M. ;
Xing, Zhihao ;
Rochinha, Fernando A. ;
Cracknell, Roger F. ;
Mira, Daniel ;
Karimi, Nader ;
Jiang, Xi .
ADVANCES IN APPLIED ENERGY, 2025, 19
[37]   Making Sense of the World: Infant Learning From a Predictive Processing Perspective [J].
Koester, Moritz ;
Kayhan, Ezgi ;
Langeloh, Miriam ;
Hoehl, Stefanie .
PERSPECTIVES ON PSYCHOLOGICAL SCIENCE, 2020, 15 (03) :562-571
[38]   Autobiographical memories and emotions: an investigation from the perspective of the schema model [J].
Kaynar, Gulsen ;
Komurcu, Burcu .
DUSUNEN ADAM-JOURNAL OF PSYCHIATRY AND NEUROLOGICAL SCIENCES, 2019, 32 (02) :129-141
[39]   An Empirical Taxonomy of Video Summarization Model From a Statistical Perspective [J].
Babu Veesam, Sai ;
Satish, Aravapalli Rama .
IEEE ACCESS, 2024, 12 :173850-173866
[40]   INTERNET MARKETING BUDGET ALLOCATION: FROM PRACTITIONER'S PERSPECTIVE [J].
Zhao, Lan ;
Zhu, Jishan .
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2010, 9 (05) :779-797