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Journal articles on the topic 'Action learning'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

Koo, L. C. "Learning action learning." Journal of Workplace Learning 11, no. 3 (May 1999): 89–94. http://dx.doi.org/10.1108/13665629910264244.

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2

Rimanoczy, Isabel, and Carole Brown. "Bringing Action Reflection Learning into action learning." Action Learning: Research and Practice 5, no. 2 (July 2008): 185–92. http://dx.doi.org/10.1080/14767330802185889.

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3

Grzybowski, Anne. "Action learning in action." Perspectives: Policy and Practice in Higher Education 12, no. 4 (October 2008): 110–16. http://dx.doi.org/10.1080/13603100802376592.

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4

Pealtie, Ken. "Action Learning in Action." Management Learning 27, no. 1 (March 1996): 87–112. http://dx.doi.org/10.1177/1350507696271006.

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5

Bunning, Richard L. "Action Learning." Executive Development 7, no. 4 (August 1994): 3–6. http://dx.doi.org/10.1108/09533239410061770.

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6

Revans, Reg. "Action learning." OR Insight 2, no. 1 (January 1989): 22–23. http://dx.doi.org/10.1057/ori.1989.8.

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7

Bourner, Tom, Liz Beaty, John Lawson, and Suzanne O’Hara. "Action learning comes of age: questioning action learning." Education + Training 38, no. 8 (November 1996): 32–35. http://dx.doi.org/10.1108/00400919610146306.

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8

Pedler, Mike, and Sonja Antell. "Action learning for social action." Action Learning: Research and Practice 16, no. 1 (January 2, 2019): 3–4. http://dx.doi.org/10.1080/14767333.2019.1563326.

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9

Wade, Sian, and Marilyn Hammick. "Action Learning Circles: action learning in theory and practice." Teaching in Higher Education 4, no. 2 (April 1999): 163–78. http://dx.doi.org/10.1080/1356251990040202.

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10

Rimanoczy, Isabel. "Action learning and action reflection learning: are they different?" Industrial and Commercial Training 39, no. 5 (July 17, 2007): 246–56. http://dx.doi.org/10.1108/00197850710761936.

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11

Ceely, Brad, Anne Maree Davis, Natalie Hooke, Margaret Kelly, Peter Lewis, and Claudia Watson. "Learning action learning: a journey." Action Learning: Research and Practice 5, no. 1 (March 2008): 57–63. http://dx.doi.org/10.1080/14767330701880317.

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12

Hanaki, Nobuyuki. "Action learning versus strategy learning." Complexity 9, no. 5 (2004): 41–50. http://dx.doi.org/10.1002/cplx.20036.

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13

Wang, Zhenyi, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan, and Changyou Chen. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12281–88. http://dx.doi.org/10.1609/aaai.v34i07.6911.

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Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which can not only generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.
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14

Fox, Steve. "Following the action in action learning: towards ethnomethodological studies of (critical) action learning." Action Learning: Research and Practice 6, no. 1 (March 2009): 5–16. http://dx.doi.org/10.1080/14767330902731251.

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15

Mumford, Alan. "Learning in action." Industrial and Commercial Training 27, no. 8 (September 1995): 36–40. http://dx.doi.org/10.1108/00197859510097081.

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16

Cusins, Peter. "Action learning revisited." Industrial and Commercial Training 27, no. 4 (May 1995): 3–10. http://dx.doi.org/10.1108/00197859510147058.

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17

Pedler, Mike, and Kiran Trehan. "Evaluating action learning." Action Learning: Research and Practice 7, no. 2 (July 2010): 117–19. http://dx.doi.org/10.1080/14767333.2010.488322.

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18

Smith, Sue, Kath Aspinwall, and Kathryn Winterburn. "Action learning experiences." Action Learning: Research and Practice 12, no. 3 (September 2, 2015): 314–16. http://dx.doi.org/10.1080/14767333.2015.1094628.

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19

O’Neil, Judy, and Victoria J. Marsick. "Action Learning Coaching." Advances in Developing Human Resources 16, no. 2 (February 6, 2014): 202–21. http://dx.doi.org/10.1177/1523422313520202.

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The Problem Action learning (AL) continues to be an important learning and development approach within organizations. Many successful AL programs use AL coaches to help facilitate the program and the learning that can occur within such programs. How might AL coaching be similar to, or different from, other types of coaching, and how might AL coaching be most effectively practiced? The Solution A review of the conceptual, theoretical, and empirical research on AL coaching will address these questions and will enable a comparison of AL coaching and other types of coaching. We draw upon the research to demonstrate effective practice and the learning that can result from AL interventions. The Stakeholders AL is used globally for learning, development, and change, and therefore, many organizational leaders are interested in the effective practice of AL and AL coaching. These leaders, human resource development (HRD) professionals, executives, internal and external coaches will benefit from this article.
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20

Bourner, Tom, and Paul Frost. "Experiencing action learning." Employee Counselling Today 8, no. 6 (November 1996): 11–18. http://dx.doi.org/10.1108/13665629610150135.

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21

Farrington, Brian. "Action‐centred learning." Industrial and Commercial Training 35, no. 3 (June 2003): 112–18. http://dx.doi.org/10.1108/00197850310470348.

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22

Trehan, Kiran, Russ Vince, Lisa Anderson, and Clare Rigg. "Critical action learning." Management Learning 49, no. 1 (February 2018): 67–68. http://dx.doi.org/10.1177/1350507617748283.

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23

Walker, Esther. "Learning through action." Development and Learning in Organizations: An International Journal 23, no. 6 (October 2, 2009): 18–21. http://dx.doi.org/10.1108/14777280910994877.

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24

Bell, Sarah, Mark Mattern, and Mike Telin. "Community-Action Learning." Journal of Political Science Education 3, no. 1 (January 19, 2007): 61–78. http://dx.doi.org/10.1080/15512160601115539.

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25

Christiansen, Angela, Trish Prescott, and Judith Ball. "Learning in action: Developing safety improvement capabilities through action learning." Nurse Education Today 34, no. 2 (February 2014): 243–47. http://dx.doi.org/10.1016/j.nedt.2013.07.008.

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26

Clarke, Jean, Richard Thorpe, Lisa Anderson, and Jeff Gold. "It's all action, it's all learning: action learning in SMEs." Journal of European Industrial Training 30, no. 6 (July 2006): 441–55. http://dx.doi.org/10.1108/03090590610688825.

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27

Zuber‐Skenitt, Ortrun. "Improving Learning and Teaching Through Action Learning and Action Research." Higher Education Research & Development 12, no. 1 (January 1993): 45–58. http://dx.doi.org/10.1080/0729436930120105.

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28

Pfister, Roland, Andrea Kiesel, and Joachim Hoffmann. "Learning at any rate: action–effect learning for stimulus-based actions." Psychological Research 75, no. 1 (May 19, 2010): 61–65. http://dx.doi.org/10.1007/s00426-010-0288-1.

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29

Park, Soo Jung, Tae Hoon Kim, Kwang Bok Yi, Young Il Jang, and Han Wook Cho. "Action Research of Instructors’ Learning Community by Action Learning in University." Korean Association For Learner-Centered Curriculum And Instruction 16, no. 11 (November 3, 2016): 1059–82. http://dx.doi.org/10.22251/jlcci.2016.16.11.1059.

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30

O’Hara, Suzanne, Liz Beaty, John Lawson, and Tom Bourner. "Action learning comes of age ‐ part 2: action learning for whom?" Education + Training 39, no. 3 (April 1997): 91–95. http://dx.doi.org/10.1108/00400919710164080.

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31

Beaty John Lawson Tom Bourner, Liz, and Suzanne O’Hara. "Action learning comes of age ‐ part 3: action learning for what?" Education + Training 39, no. 5 (July 1997): 184–88. http://dx.doi.org/10.1108/00400919710169490.

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32

Rimanoczy, Isabel. "Action learning and action reflection learning: are they different? Part 2." Industrial and Commercial Training 39, no. 6 (September 4, 2007): 325–31. http://dx.doi.org/10.1108/00197850710816809.

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33

Pandey, Ritik, Yadnesh Chikhale, Ritik Verma, and Deepali Patil. "Deep Learning based Human Action Recognition." ITM Web of Conferences 40 (2021): 03014. http://dx.doi.org/10.1051/itmconf/20214003014.

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Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this paper we proposed a deep learning based model for Recognition and the main focus is on the CNN model for image classification. The action videos are converted into frames and pre-processed before sending to our model for recognizing different actions accurately..
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34

Liu, Dan, Mao Ye, and Jianwei Zhang. "Improving Action Recognition Using Sequence Prediction Learning." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (March 20, 2020): 2050029. http://dx.doi.org/10.1142/s0218001420500299.

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Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predictors can then output the action trends at the next time step. According to the predictions of these action predictors at each time step and the removal rule, the poor predictors can be eliminated step by step, and the IDentity(ID) number of the last predictor left is considered as the label of the action sequence to be categorized. We compare the proposed action recognition algorithm using sequence prediction learning with other methods on two publicly available datasets. Our experimental results consistently demonstrate the feasibility and effectiveness of the suggested method. It also proves the importance of prediction learning for action recognition.
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35

Yeo, Roland K., and Michael J. Marquardt. "(Re) Interpreting Action, Learning, and Experience: Integrating Action Learning and Experiential Learning for HRD." Human Resource Development Quarterly 26, no. 1 (March 2015): 81–107. http://dx.doi.org/10.1002/hrdq.21199.

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36

Amir, E., and A. Chang. "Learning Partially Observable Deterministic Action Models." Journal of Artificial Intelligence Research 33 (November 20, 2008): 349–402. http://dx.doi.org/10.1613/jair.2575.

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We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model(the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains. Our algorithms take sequences of partial observations over time as input, and output deterministic action models that could have lead to those observations. The algorithms output all or one of those models (depending on our choice), and are exact in that no model is misclassified given the observations. Our algorithms take polynomial time in the number of time steps and state features for some traditional action classes examined in the AI-planning literature, e.g., STRIPS actions. In contrast, traditional approaches for HMMs and Reinforcement Learning are inexact and exponentially intractable for such domains. Our experiments verify the theoretical tractability guarantees, and show that we identify action models exactly. Several applications in planning, autonomous exploration, and adventure-game playing already use these results. They are also promising for probabilistic settings, partially observable reinforcement learning, and diagnosis.
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37

Bando, Hideyuki, Katsumi Inoue, and Hidetomo Nabeshima. "Learning Action Rules in Action Language A." Transactions of the Japanese Society for Artificial Intelligence 18 (2003): 297–304. http://dx.doi.org/10.1527/tjsai.18.297.

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38

Corley, Aileen, and Ann Thorne. "Action learning: avoiding conflict or enabling action." Action Learning: Research and Practice 3, no. 1 (April 2006): 31–44. http://dx.doi.org/10.1080/14767330600574607.

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39

Barbarino, Robert. "Vom Participatory Action Research lernen?" Berichte Geographie und Landeskunde 94, no. 4 (2021): 335. http://dx.doi.org/10.25162/bgl-2021-0017.

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40

Chang, Kyungwon. ""A Model of Action Learning Program Design in Higher Education"." Journal of Educational Technology 27, no. 3 (September 30, 2011): 475–505. http://dx.doi.org/10.17232/kset.27.3.475.

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41

Wolitzky, Alexander. "Learning from Others' Outcomes." American Economic Review 108, no. 10 (October 1, 2018): 2763–801. http://dx.doi.org/10.1257/aer.20170914.

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I develop a simple model of social learning in which players observe others’ outcomes but not their actions. A continuum of players arrives continuously over time, and each player chooses once-and-for-all between a safe action (which succeeds with known probability) and a risky action (which succeeds with fixed but unknown probability, depending on the state of the world). The actions also differ in their costs. Before choosing, a player observes the outcomes of K earlier players. There is always an equilibrium in which success is more likely in the good state, and this alignment property holds whenever the initial generation of players is not well informed about the state. In the case of an outcome-improving innovation (where the risky action may yield a higher probability of success), players take the correct action as K → ∞. In the case of a cost-saving innovation (where the risky action involves saving a cost but accepting a lower probability of success), inefficiency persists as K → ∞ in any aligned equilibrium. Whether inefficiency takes the form of under-adoption or over-adoption also depends on the nature of the innovation. Convergence of the population to equilibrium may be nonmonotone. (JEL D81, D83, O32, Q12, Q16)
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42

Osogami, Takayuki, and Rudy Raymond. "Determinantal Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4659–66. http://dx.doi.org/10.1609/aaai.v33i01.33014659.

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We study reinforcement learning for controlling multiple agents in a collaborative manner. In some of those tasks, it is insufficient for the individual agents to take relevant actions, but those actions should also have diversity. We propose the approach of using the determinant of a positive semidefinite matrix to approximate the action-value function in reinforcement learning, where we learn the matrix in a way that it represents the relevance and diversity of the actions. Experimental results show that the proposed approach allows the agents to learn a nearly optimal policy approximately ten times faster than baseline approaches in benchmark tasks of multi-agent reinforcement learning. The proposed approach is also shown to achieve the performance that cannot be achieved with conventional approaches in partially observable environment with exponentially large action space.
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43

Vince, Russ. "‘Learning-in-action’ and ‘learning inaction’: advancing the theory and practice of critical action learning." Action Learning: Research and Practice 5, no. 2 (July 2008): 93–104. http://dx.doi.org/10.1080/14767330802185582.

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44

van den Berg, Annemieke, Belinda Dewar, and Carolien Smits. "Bring learning into action." International Practice Development Journal 8, no. 1 (May 16, 2018): 1–9. http://dx.doi.org/10.19043/ipdj.81.011.

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45

TOYOTA, Heisuke. "Action Regulation and Learning." Rigakuryoho Kagaku 21, no. 1 (2006): 81–85. http://dx.doi.org/10.1589/rika.21.81.

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46

van den Berg, Annemieke, Belinda Dewar, and Carolien Smits. "Bring learning into action." International Practice Development Journal 8, no. 1 (May 16, 2018): 1–9. http://dx.doi.org/10.19043/ipdj81.011.

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47

Ramírez Espinosa, Alexánder, and Fanny Hernández Gaviria. "Learning strategies in action." Revista Boletín Redipe 11, no. 04 (April 1, 2022): 67–83. http://dx.doi.org/10.36260/rbr.v11i04.1802.

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This paper reports the results of a study which aimed to investigate how an intervention for the development of learning strategies helps students to build self-regulation in learning and to learn English as a foreign language. The intervention was part of a first-year English course in a foreign language program that followed a task-based methodological orientation. The intervention was implemented in four phases: planning, presentation, instruction, and evaluation and engaged students in the development of two tasks, involving contextaware learning with the use of strategies: cognitive, metacognitive, memory, affective, and social. The evaluation of results demonstrated that students develop a positive attitude facing learning and raised awareness about the importance of using strategies. However, the strategies used by students were of direct type, related to linguistic aspects learning. This means that the intervention did not achieve to develop indirect strategies, particularly metacognitive, which demand reflection for making critical learning decisions. This shows that a shortterm intervention is not enough to promote self-regulation in learning and that a long-term intervention would be required.
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48

Thompson, Donna R. "Organizational Learning in Action." Journal of Invitational Theory and Practice 10 (February 18, 2022): 52–73. http://dx.doi.org/10.26522/jitp.v10i.3815.

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Public schools have met with increasingly unfavorable reactionin recent years, while private schools, magnet schoolsand charter schools seem to be gaining interest and publicsupport. This may be due, in part, to the fact that publicschools seem to lack a welcoming atmosphere. This action researchstudy examined how teachers and an administrator atan elementary school in Texas used organizational learningstrategies to implement the philosophy of invitational educationon their campus in an effort to create a more welcomingclimate.
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49

Chivers, Terence S. "Action learning as invigoration." Action Learning: Research and Practice 8, no. 1 (March 2011): 65–68. http://dx.doi.org/10.1080/14767333.2011.549329.

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50

Boshyk, Yury. "ABC of action learning." Action Learning: Research and Practice 11, no. 2 (May 4, 2014): 252–59. http://dx.doi.org/10.1080/14767333.2014.909234.

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