Action Learning Research Articles

Kernels of Motor Memory Formation: Temporal Generalization in Bimanual Adaptation.

In daily life, we coordinate both simultaneous and sequential bimanual movements to manipulate objects. Our ability to rapidly account for different object dynamics suggests there are neural mechanisms to quickly deal with them. Here we investigate how actions of one arm can serve as a contextual cue for the other arm, and facilitate adaptation. Specifically, we examine the temporal characteristics that underlie motor memory formation and recall, by testing the contextual effects of prior, simultaneous, and post contralateral arm movements in both male and female human participants. To do so, we measure their temporal generalization in three bimanual interference tasks. Importantly, the timing context of the learned action plays a pivotal role in the temporal generalization. While motor memories trained with post adaptation contextual movements generalize broadly, motor memories trained with prior contextual movements exhibit limited generalization, and motor memories trained with simultaneous contextual movements do not generalize to prior or post contextual timings. This highlights temporal tuning in sensorimotor plasticity: different training conditions yield substantially different temporal generalization characteristics. Since these generalizations extend far beyond any variability in training times, we suggest that the observed differences may stem from inherent differences in the use of prior, current and post-adaptation contextual information in the generation of natural behavior. This would imply differences in the underlying neural circuitry involved in learning and executing the corresponding coordinated bimanual movements.Significance Statement This study addresses a fundamental question in the field of sensorimotor neuroscience of how multiple movements are temporally linked within a single motor memory. We examine the temporal generalization of motor memory formation by varying the timing of contextual movements associated with a learned motor memory across a range of prior, current, and post adaptation movement times using a bimanual motor learning task. We observed distinct patterns of temporal generalization based on whether the contextual movements occurred prior to, simultaneously with, or after the adaptation movement. For the first time, our findings reveal that the timing of contextual movements is crucial in the formation and generalization of motor memories.

613P Contemporary use of ventricular assist devices in muscular dystrophy: a report from the ACTION learning collaborative

613P Contemporary use of ventricular assist devices in muscular dystrophy: a report from the ACTION learning collaborative

Tennis teaching assistance model based on double chain shared unsupervised action recognition algorithm

The traditional self-supervised methods based on skeleton data frequently categorize the various enhancements of a given sample as positive examples, while the remaining samples are designated as negative examples. This approach results in a significant imbalance in the ratio of positive to negative samples, which in turn constrains the efficacy of samples with identical semantic information. Therefore, to further improve the training quality in tennis teaching and enhance the accuracy of action recognition, a dual chain sharing unsupervised action recognition algorithm has been proposed. This study first designs a skeleton topology data augmentation method based on the physical connections of human joints to obtain advanced semantic embeddings. Then, an improved positive sample expansion strategy is utilized to enhance the diversity and quality of training data. Next, an unsupervised learning mechanism is employed to autonomously learn and recognize complex patterns of tennis movements. To measure the performance of the model, tests were conducted on its accuracy, F1 score, recognition time, and fitting degree. The experimental results showed that the proposed algorithm could reach its optimal state after 23 iterations of training, and its F1 value reached 0.918, with an average accuracy of 92.9 % and an average recognition time of 7.4 s. The research algorithms were superior to the multi-input branch graph convolutional network action recognition algorithms used for comparison, pull-push contrastive loss action recognition algorithms, and multi-granularity anchor contrastive representation learning action recognition algorithms. Its accuracy was leading by 8.6 %-23.5 %, and the recognition time has been reduced by 2.3s-7.4 s. In addition, in terms of the fitting degree of action recognition, compared with other methods, the proposed method had a fitting degree of up to 95.7 %, which was at least 8.1 % higher. This research method can improve the timeliness of feedback from trainers by accurately and quickly recognizing movements, helping coaches develop more targeted training plans, thereby improving the efficiency and quality of tennis teaching.

대학 내 이공계 학생의 액션러닝 참여 경험에 관한 현상학적 연구

The purpose of this study is to gather the experiences of engineering and science students participating in action learning within universities and to explore the factors that indicate the support they require from these experiences. To achieve this objective, the phenomenological research method proposed by Colaizzi(1978) was employed to identify the common experiences of the participants. A purposive sampling method was utilized to select six engineering and science students who engaged in action learning as research participants. Data were collected through unstructured in-depth interviews, participant observation, and a thorough literature review. The results revealed three categories, ten theme clusters, and twenty-five themes. Based on the characteristics of engineering and science students identified through their action learning experiences, their support needs encompass 'a deficiency in communication skills', 'challenges associated with selecting manufacturing companies', 'an increased necessity for persuasion and study due to the specialization of their majors', and 'a discrepancy in perceived task difficulty between instructors and students'. The role of facilitators in addressing these needs was also examined.

Constructing Sacred Containers for Participatory Action Research: An Embodied Mindfulness Arts-Based Practice.

College campuses are sites of institutional betrayal and interpersonal harm for too many survivors of gender-based violence. In pursuit of change aligned with empowerment frameworks and feminist epistemologies, many of us create spaces for impacted students to engage in participatory action research and learning. This article describes a mindfulness, arts-based embodied practice for transforming settings and positioning participants as visionaries and experts on safety, power, and well-being. Through intentional dyadic and collective discussion, the Sacred Containers form the foundation for a coconstructed community. This article describes the rationale, process, impact, and recommendations via student and faculty reflections across four cohorts.

Advancing honour and dignity in death for victims of armed conflicts: Exploring the challenges and opportunities of AI and machine learning in humanitarian forensic action under IHL

Advancing honour and dignity in death for victims of armed conflicts: Exploring the challenges and opportunities of AI and machine learning in humanitarian forensic action under IHL

Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions

Social learning is well established across species. While recent neuroimaging studies show that dorsomedial prefrontal cortex (DMPFC/preSMA) activation correlates with observational learning signals, the precise computations that are implemented by DMPFC/preSMA have remained unclear. To identify whether DMPFC/preSMA supports learning from observed outcomes or observed actions, or possibly encodes even a higher order factor (such as the reliability of the demonstrator), we downregulate DMPFC/preSMA excitability with continuous theta burst stimulation (cTBS) and assess different forms of observational learning. Relative to a vertex-cTBS control condition, DMPFC/preSMA downregulation decreases performance during action-based learning but has no effect on outcome-based learning. Computational modeling reveals that DMPFC/preSMA cTBS disrupts learning the predictability, a proxy of reliability, of the demonstrator and modulates the rate of learning from observed actions. Thus, our results suggest that the DMPFC is causally involved in observational action learning, mainly by adjusting the speed of learning about the predictability of the demonstrator.

Action learning in a broader context: three challenges

ABSTRACT This paper addresses three challenges facing action learning – the implications of its historical roots for the present; the encounter of action learning within different cultures and a tension within action learning itself. It concludes that deeper understanding of all three is necessary for action learning's continuing development.

Challenges of Preparing Students for International Entrepreneurship in the era of Artificial Intelligence

International entrepreneurship competencies in the era of rapidly developing artificial intelligence focus more than earlier on stepping out of one’s comfort zone, asking the right questions, assessing the reliability of information sources, and developing cross-cultural teamwork readiness. The purpose of this research is to prepare students for collaborative learning situations where generative artificial intelligence tools have become widely available. The main research question is: What are the challenges in different types of entrepreneurial team-based action learning projects in the context of artificial intelligence? Learning concepts based on collaborative learning and improving the international networking readiness of students are presented. Practical implications of time zone differences, trust building and conflict management in international teams are analysed based on the experience of X-Culture projects. We compare the opportunities and challenges of applying generative artificial intelligence such as ChatGPT-4, Microsoft Co-pilot and Google Gemini Advanced when developing learners’ own international business ideas to learning from international teamwork to assist foreign start-ups in entering new markets. Gaps in skills to apply human collaboration for critically assessing artificial intelligence summary answers and identifying possible hallucinations when comparing new potential foreign markets were revealed. Artificial intelligence applications can be used to form global student teams based on the similarity of entrepreneurial self-development visions of team members. Still, experimenting with prompts in these applications can also enhance readiness to compare the pros and cons of different international entrepreneurship concepts and intelligent questioning skills for coaching and consulting. Students can chat with AI in a role-playing manner that helps them understand how to manage cultural differences in international entrepreneurship teams. Online action learning driven by tasks of start-ups is an essential tool in the entrepreneurial learning approach that prepares students to understand the value and limitations of generative artificial intelligence tools for international entrepreneurship. This paper contributes to understanding the challenges in action learning of students for international entrepreneurship and ways to overcome these challenges in the era when generative artificial intelligence tools are changing learning and international entrepreneurship practices.

Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory

Abstract Process automation is essential to establish an economically viable circular factory in high-wage locations. This involves using autonomous production technologies, such as robots, to disassemble, reprocess, and reassemble used products with unknown conditions into the original or a new generation of products. This is a complex and highly dynamic issue that involves a high degree of uncertainty. To adapt robots to these conditions, learning from humans is necessary. Humans are the most flexible resource in the circular factory and they can adapt their knowledge and skills to new tasks and changing conditions. This paper presents an interdisciplinary research framework for learning human action knowledge from complex manipulation tasks through human observation and demonstration. The acquired knowledge will be described in a machine-executable form and will be transferred to industrial automation execution by robots in a circular factory. There are two primary research objectives. First, we investigate the multi-modal capture of human behavior and the description of human action knowledge. Second, the reproduction and generalization of learned actions, such as disassembly and assembly actions on robots is studied.

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

Twenty years of action learning in the journal Action Learning: Research and Practice

ABSTRACT This paper provides a thematic analysis of research papers published in Action Learning: Research and Practice over the past 20 years. The analysis concentrates on the different contexts in which action learning has taken place and on the variations of action learning that have been presented in different papers. Contexts discussed in this paper are different international settings for action learning, and the use of action learning in healthcare, for social action, for small and medium-sized enterprises, and in higher education. Variations and developments of action learning are critical action learning, virtual action learning, and action learning research.

Action learning in higher education: reflections on facilitating AL in leadership development programmes

ABSTRACT This account of practice offers reflections and insights on facilitating Action Learning (AL) in Leadership Programmes within the Higher Education context. The account shares our reflections and key observations as practitioner academics, facilitating AL Sets within three higher education leadership programmes. We draw on our knowledge and expertise as facilitators of learning and development in the AL and leadership space. Our reflections have highlighted two key themes in our facilitation experience. First, the autonomy of learning and the significance of AL participants’ voice. Second, creating a shift from task-focus to people-focus through the use of metaphor and visualisation as a means of enquiry. This account will be of relevance to practitioner and academics engaged in leadership development and those involved in the facilitation of AL who may consider adopting AL as a part of a managerial leadership programme.

Quaker clearness and action learning

Quaker clearness and action learning

Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies

AbstractA hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart manufacturing context. Efficiently solving the dynamic hybrid flow shop scheduling problem (HFSP), characterised by dynamic release times, uncertain job processing times, and flexible machine maintenance has become a significant research focus. A NeuroEvolution of Augmenting Topologies (NEAT) algorithm is proposed to minimise the maximum completion time. To improve the NEAT algorithm's efficiency and effectiveness, several features were integrated: a multi‐agent system with autonomous interaction and centralised training to develop the parallel machine scheduling policy, a maintenance‐related scheduling action for optimal maintenance decision learning, and a proactive scheduling action to avoid waiting for jobs at decision moments, thereby exploring a broader solution space. The performance of the trained NEAT model was experimentally compared with the Deep Q‐Network (DQN) and five classical priority dispatching rules (PDRs) across various problem scales. The results show that the NEAT algorithm achieves better solutions and responds more quickly to dynamic changes than DQN and PDRs. Furthermore, generalisation test results demonstrate NEAT's rapid problem‐solving ability on test instances different from the training set.

Twenty years of Action Learning: Research and Practice

Twenty years of Action Learning: Research and Practice

Becoming an action learning facilitator – two stories

ABSTRACT This article traces the development of two action learning practitioners from differing backgrounds and starting points.

A shared temporal window of integration across cognitive control and reinforcement learning paradigms: A correlational study.

Cognitive control refers to the ability to override prepotent response tendencies to achieve goal-directed behavior. On the other hand, reinforcement learning refers to the learning of actions through feedback and reward. Although cognitive control and reinforcement learning are often viewed as opposing forces in driving behavior, recent theories have emphasized possible similarities in their underling processes. With this study, we aimed to investigate whether a similar time window of integration could be observed during the learning of control on the one hand, and the learning rate in reinforcement learning paradigms on the other. To this end, we performed a correlational analysis on a large public dataset (n = 522) including data from two reinforcement learning tasks, i.e., a probabilistic selection task and a probabilistic Wisconsin Card Sorting Task (WCST), and data from a classic conflict task (i.e., the Stroop task). Results showed expected correlations between the time scale of control indices and learning rate in the probabilistic WCST. Moreover, the learning-rate parameters of the two reinforcement learning tasks did not correlate with each other. Together, these findings suggest a reliance on a shared learning mechanism between these two traditionally distinct domains, while at the same time emphasizing that value updating processes can still be very task-specific. We speculate that updating processes in the Stroop and WCST may be more related because both tasks require task-specific updating of stimulus features (e.g., color, word meaning, pattern, shape), as opposed to stimulus identity.

Pelatihan Penelitian Tindakan Kelas bagi Guru MTs di Kota Palangkaraya

This service activity aimed to provide training to Madrasah Tsanawiyah teachers in compiling scientific papers in the form of Classroom Action Research. Apart from that, it trains teachers to carry out research activities in the classroom to develop their profession. The participants in this activity were 25 teachers at MTs Negeri 2 Palangka Raya. This activity was held in the Meeting Room of MTs Negeri 2 Palangka Raya on December 15 2023. Material was provided by Sabarun, M.Pd. which relates to guidelines for preparing scientific papers which consist of procedures in classroom action research and learning models that can be used in the teaching and learning process. This activity produces a draft proposal for Classroom Action Research using appropriate cycles. During the activity, participants were enthusiastic and produced PTK proposals with various titles. It could be showed that this service activity was running well and obtaining maximum results.

Learning Action Cell Program and Teachers’ Functional Competency in Cagayan De Oro City

Learning Action Cell (LAC) program influences teachers’ functional competency. It is an integral part of professional development initiatives, which impacts teachers’ instructional skills, classroom management, assessment strategies, and professional growth. This study focused on the implementation of LAC program and teachers’ functional competency in the Division of Cagayan de Oro City. It sought to determine the relationship between the implementation of LAC and teachers’ functional competency and find the level of implementation of LAC and teachers’ functional competency. A descriptive-correlational method was used in this study. The respondents were one hundred forty-seven (147) public elementary school teachers. The survey questionnaire was the main instrument used to gather data. The statistical tools used were descriptive statistics such as mean and standard deviation. Pearson Product Moment Correlation (r) was employed to determine the relationship between the implementation of LAC and teachers’ functional competency. Findings revealed that the LAC program was well implemented, and the level of teachers’ functional competency was outstanding. Hence, there was a significant relationship between the implementation of LAC and teachers’ functional competency. It is recommended that LAC coordinators align LAC activities with the school’s broader professional development plans. Teachers should also engage in collaborative projects with other educators to share experiences and best practices. School heads should also provide training and support strategies for teachers.