Action Learning Research Articles

Experiential Learning in Action: Analyzing Outcomes and Educational Implications

Background and Aims: Experiential Learning in Action is important because it connects theoretical knowledge to practical application, increasing student engagement and real-world problem-solving abilities. It transforms learning from abstract concepts to actionable experiences, better-preparing students for future challenges. This paper aims to explore how experiential learning translates into practice and its educational implications. Methodology: This article's methodology involved conducting a thorough analysis of secondary data from various sources to investigate the outcomes and implications of experiential learning. The study successfully identified key patterns and trends through systematic document analysis as well as thematic and content analyses, providing valuable insights and recommendations for improving educational practices and policy. Results: the finding found that Experiential learning improves educational outcomes by encouraging critical thinking, personal development, and practical skills that are necessary for future success. Its incorporation into curricula and teaching practices necessitates careful planning and institutional support. Taking on challenges and leveraging ongoing research and innovation will ensure that experiential learning continues to transform education, making it more dynamic, relevant, and inclusive. Conclusion: Experiential learning improves educational outcomes by encouraging critical thinking, personal development, and the practical skills required for future success. Its successful integration into curricula and teaching necessitates careful planning and institutional support, as well as ongoing research and innovation to ensure its continued educational transformation.

Storytelling for personal connection: A pedagogical strategy to localise fashion education

The 2015 student protests, #RhodesMustFall and #FeesMustFall, gave rise to the call to decolonise curricula and end the dominance of Western ideologies in South African higher education (HE). The argument put forward in this paper is the need to shift from a traditional approach to a humanised pedagogical approach, wherein students frame knowledge around individual experiences to construct personal and shared understanding. Although limited scholarship around decolonising South African fashion HE exists, such scholarship does not focus on storytelling and circle learning as pedagogical strategies. To address this research gap, narrative humanism, referred to in this research as storytelling, and circle learning are put forward as pedagogical strategies to integrate student identities for personal connection in South African fashion HE. This paper aims to explore the affordances of storytelling and circle learning to decolonise South African fashion HE. Through qualitative action research, two teaching and learning interventions, termed the pilot and main studies, were designed and applied with fashion students at a South African HE institution. Data collection entailed semi-structured student questionnaires, artefacts, and a reflective research journal. To analyse the data, content analysis was employed. The findings reveal that, irrespective of cultural lived experiences or diverse backgrounds, storytelling afforded a decolonised approach in terms of inclusivity, collaboration, and a safe environment for socially engaged dialogue and peer feedback. Similarly, circle learning seemed to reduce teacher-student power relations and contrasted traditional modes of delivery. Circle learning appeared to encourage meaningful, engaged participation, affording a progressive pedagogical strategy to accommodate student and teacher voices in open dialogue. This paper contributes to the scholarship of teaching and learning in that storytelling and circle learning are suitable pedagogies to decolonise fashion education in the Global South.

Action learning for change management in digital transformation

Action learning for change management in digital transformation

How is Reflective Practice in Learning? A Science Teachers Perspective

Reflective is a thinking process that can be carried out by teachers, leading to improvements in effective classroom management and quality and improving student performance, so that reflective practices are indispensable in teaching and learning practices. This study aims to describe the reflective practice of science teachers in junior high schools. Primary data were collected based on the teacher’s experience in teaching science learning, highlighting three aspects including reflective action, the form of reflective action, and follow-up to reflective activities. This qualitative study was conducted through the distribution of online questionnaires. The results indicate that reflective action in learning by science teachers is always carried out, but the form of reflective action has not yet involved many peers, principals, and supervisors. Most often done by teachers themselves, to accept other people’s thoughts, learn, or share good practices have not fully occurred. The follow-up of reflective activities has not been followed by changes to the design of further learning in the lesson plan, but teachers are more focused on changing the teaching process that they do in class independently. Keywords: reflective practice, science teachers, perspective

Learning in action: embedding the SDGs through the Reach Alliance

There has been increasing practical and scholarly interest in the engagement of universities with the Sustainable Development Goals (SDGs). However, there has been limited examination of international university collaborations focusing on the SDGs and how they become embedded within universities. Addressing this need, this article explores the experiences of three members of the Reach Alliance a consortium of eight higher education institutions from around the globe. Reach supports students and faculty mentors to study how critical interventions can be made accessible to those who are the hardest to reach. This work aligns with SDG 4 (Quality Education), as well as SDG 17 (Partnership for the Goals) and the Goal’s second universal value of leave no one behind. This commitment to connecting education and societal engagement resonates with Goddard et al.’s work on the civic university as both “globally competitive and locally engaged” (2012: 43). This article focuses on University College London (UK), Ashesi University (Ghana), and Tecnológico de Monterrey (Mexico), selected for their diverse structures and geographies. For each case, we examine how the Reach Alliance initiative has been institutionally embedded, as well as the role of local and global partnerships in making the case for supporting Reach. We find that Reach’s organisation as an international network has encouraged its adoption by host institutions. The initiative’s emphasis on both local concerns as well as the global goal and networks has also resonated with host institutions. This article will be of interest to those working in sustainability and higher education when considering strategies for introducing or increasing SDG-focussed research and teaching.

Salience Interest Option: Temporal abstraction with salience interest functions

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.

The School Learning Action Cell (Slac): Its Influence on Teachers’ Proficiency and School Performance From the Perspectives of Teachers and School Heads

The School Learning Action Cell (Slac): Its Influence on Teachers’ Proficiency and School Performance From the Perspectives of Teachers and School Heads

Enhancing the well-being of early childhood education practitioners working in resource-constrained contexts

Background: The recent migration of early childhood care and education function brought with it many changes that affect the workplace well-being of practitioners and centre managers, yet little research has reported on the voices and experiences of those working on the ground.Aim: To find out the current state of well-being of practitioners working in resource-constrained contexts to help us theorise how might they take action to improve it.Setting: Early childhood care and education centres in rural and township areas in six different provinces.Methods: The first author conducted 10 semi-structured focus group interviews with 80 practitioners recruited by collaborating researchers at various universities. All ethical protocols were adhered to. The focus groups were audio-taped, transcribed and thematically analysed independently by the two authors before reaching consensus.Results: Two themes were identified: (1) participants experienced negative emotions arising from both internal and systemic aspects that were affecting their well-being. (2) Several factors promoted the well-being of practitioners despite their difficult circumstances.Conclusion: Based on the findings, it appears that close collaboration among practitioners within centres and, with other external stakeholders, was an important factor for enhanced well-being. Drawing from action learning theory, we suggest how practitioners can collaborate to sustain their well-being while addressing the challenges they face.Contribution: This collaborative action learning approach can be applied not only by ECCE centres, but to any organisation wishing to improve the well-being and practice of their members.

Leveraging Frame- and Feature-Level Progressive Augmentation for Semi-supervised Action Recognition

Semi-supervised action recognition is a challenging yet prospective task due to its low reliance on costly labeled videos. One high-profile solution is to explore frame-level weak/strong augmentations for learning abundant representations, inspired by the FixMatch framework dominating the semi-supervised image classification task. However, such a solution mainly brings perturbations in terms of texture and scale, leading to the limitation in learning action representations in videos with spatiotemporal redundancy and complexity. Therefore, we revisit the creative trick of weak/strong augmentations in FixMatch, and then propose a novel Frame- and Feature-level augmentation FixMatch (dubbed as F 2 -FixMatch) framework to learn more abundant action representations for being robust to complex and dynamic video scenarios. Specifically, we design a new Progressive Augmentation (P-Aug) mechanism that implements the weak/strong augmentations first at the frame level, and further implements the perturbation at the feature level, to obtain abundant four types of augmented features in broader perturbation spaces. Moreover, we present an evolved Multihead Pseudo-Labeling (MPL) scheme to promote the consistency of features across different augmented versions based on the pseudo labels. We conduct extensive experiments on several public datasets to demonstrate that our F 2 -FixMatch achieves the performance gain compared with current state-of-the-art methods. The source codes of F 2 -FixMatch are publicly available at https://github.com/zwtu/F2FixMatch.

Learning task-relevant representations via rewards and real actions for reinforcement learning

The input of visual reinforcement learning often contains redundant information, which will reduce the decision efficiency and decrease the performance of the agent. To address this issue, task-relevant representations of the input are usually learned, where only task-related information is preserved for the decision making. While in the literature, auxiliary tasks that are constructed by using reward signals, optimal policy or by extracting controllable elements in the input are commonly adopted to learn the task-relevant representations, the methods based on reward signals do not work well in sparse reward environments, the effectiveness of methods using optimal policy relies heavily on the optimality degree of the given policy, and the methods by extracting the controllable elements will ignore the uncontrollable task-relevant information in the input. To alleviate these problems and to learn better task-relevant representations, in this paper, we first encourage the encoder to encode controllable parts in the input by maximizing the conditional mutual information between the representations and agent’s real actions. And then as reward signals are directly related to the underlying tasks , they are used to make more task-related information encoded irrespective of whether such information is controllable or not. Finally, a temporal coherence constraint is incorporated into the whole framework to reduce the task-irrelevant information in the representations. Experiments on Distracting DeepMind Control Suite and autonomous driving simulator CARLA show that our proposed approach can achieve better performances than some state-of-the-art (SOTA) baselines, which demonstrates the method’s effectiveness in enhancing the agent’s decision efficiency and overall performances. Code is available at https://github.com/DMU-XMU/Learning-Task-relevant-Representations-via-Rewards-and-Real-Actions-for-Reinforcement-Learning.git.

Social demonstration of colour preference improves the learning of associated demonstrated actions.

We studied how different types of social demonstration improve house sparrows' (Passer domesticus) success in solving a foraging task that requires both operant learning (opening covers) and discrimination learning (preferring covers of the rewarding colour). We provided learners with either paired demonstration (of both cover opening and colour preference), action-only demonstration (of opening white covers only), or no demonstration (a companion bird eating without covers). We found that sparrows failed to learn the two tasks with no demonstration, and learned them best with a paired demonstration. Interestingly, the action of cover opening was learned faster with paired rather than action-only demonstration despite being equally demonstrated in both. We also found that only with paired demonstration, the speed of operant (action) learning was related to the demonstrator's level of activity. Colour preference (i.e. discrimination learning) was eventually acquired by all sparrows that learned to open covers, even without social demonstration of colour preference. Thus, adding a demonstration of colour preference was actually more important for operant learning, possibly as a result of increasing the similarity between the demonstrated and the learned tasks, thereby increasing the learner's attention to the actions of the demonstrator. Giving more attention to individuals in similar settings may be an adaptive strategy directing social learners to focus on ecologically relevant behaviours and on tasks that are likely to be learned successfully.

Me or we? Action-outcome learning in synchronous joint action

Goal-directed behaviour requires mental representations that encode instrumental relationships between actions and their outcomes. The present study investigated how people acquire representations of joint actions where co-actors perform synchronized action contributions to produce joint outcomes in the environment. Adapting an experimental procedure to assess individual action-outcome learning, we tested whether co-acting individuals link jointly produced action outcomes to individual-level features of their own action contributions or to group-level features of their joint action instead. In a learning phase, pairs of participants produced musical chords by synchronizing individual key press responses. In a subsequent test phase, the previously produced chords were presented as imperative stimuli requiring forced-choice responses by both pair members. Stimulus-response mappings were systematically manipulated to be either compatible or incompatible with the individual and joint action-outcome mappings of the preceding learning phase. Only joint but not individual compatibility was found to modulate participants' performance in the test phase. Yet, opposite to predictions of associative accounts of action-outcome learning, jointly incompatible mappings between learning and test phase resulted in better performance. We discuss a possible explanation of this finding, proposing that pairs' group-level learning experience modulated how participants encoded ambiguous task instructions in the test phase. Our findings inform current debates about mechanistic explanations of action-outcome learning effects and provide novel evidence that joint action is supported by dedicated mental representations encoding own and others' actions on a group level.

Navigating the future workplace

The integration of artificial intelligence produces a significant transformation in the workplace, which presents obstacles to the performance of intrapreneurs and the productivity of the organisation, in response to disruptive technological developments. The objective of this paper is to examine the correlation between intrapreneurship development and training initiatives and the end result for organisational performance and productivity. By employing a simplified random sampling method and a quantitative research design, this study examines the intricacies of interdepartmental relationships and emphasises the importance of development and training programmes in equipping intrapreneurs to meet the challenges of the fourth industrial revolution. The collected primary data, which was structured using a 5-point Likert scale, were analysed using Statistical Package for the Social Sciences version 26. An intelligent strategy for increasing performance and productivity in order to gain a competitive edge, the study demonstrates that fostering and improving intradepartmental relationships is effective. This finding also indicates that training and development programmes focused on the implementation of advanced technologies significantly enhance the performance of intrapreneurs and the productivity of organisations. In order to adapt to the changing employment environment of the twenty-first century, the results underscore the necessity for organisations to give precedence to technical proficiency while cultivating an environment that promotes inventive thinking, action learning, and progress.

(129) - The Outcomes of Temporary Mechanical Circulatory Support Using Paracorporeal Continuous Flow Pump in Children; A Multicenter Study Using ACTION Learning Network

(129) - The Outcomes of Temporary Mechanical Circulatory Support Using Paracorporeal Continuous Flow Pump in Children; A Multicenter Study Using ACTION Learning Network

Best Social Entrepreneurship Teaching and Learning Strategies for Promoting Students' Social Entrepreneurial Minds: A Scoping Review

This paper aims to explore the best teaching and learning strategies for promoting students' social entrepreneurial minds. At the initial stage, the scoping review draws from 198 secondary data from various works in the social entrepreneurship education context, including studies on education programs, curricular development, knowledge-acquiring mechanisms, and social entrepreneurial exploration processes. It also examines instructional methods and strategies that foster the development of potential social entrepreneur mindsets. Finally, 26 included papers present a Top-Down and Bottom-Up Approach, Experiential Learning, Action Learning, Community-Based Project, and Problem-Based Learning (PBL) as effective social entrepreneurship teaching and learning approaches to promote social entrepreneurial mindsets. The review concludes that a combination of all of the aforementioned teaching and learning strategies is among the top teaching approaches for effective social entrepreneurship education. The paper proposes that dynamic social entrepreneurship curricula and learning models that emphasize hands-on experience and experience-based learning can be effective strategies for promoting students' social entrepreneurial minds.

경증 장애인과 가족 돌봄자의 효과적인 미술관 융합교육을 위한 뮤지엄 액티브 러닝 방법론 연구

This study presents the following objectives for the necessity of art museum activities necessary for the disabled and family caregivers. First, active art museum factors for people with mild disabilities and families are discovered. Second, we propose an active learning range centered on effectiveness and self-efficacy. Third, we find the Gap elements of art museum activity and present effective art museum service design. Fourth, we will build a museum active learning model that embraces both disabilities and non-disabilities. As a result of studying the special program of art museums centered on the last three years, Case-based learning is advantageous for the disabled, and family caregivers predict that Goal-based learning, Team-based learning, and Problem-based learning methods are effective. Disabled and family caregivers can try Flipped learning, Action learning, and Project-based learning together. The results of this study: First, there should be many cultural and artistic service programs by expanding public care for the developmentally disabled and the elderly with dementia. Second, m service design should help disabled people and their families with a sense of accomplishment, satisfaction, and social resilience. Third, art museum experience education services for the mildly disabled and their families should prioritize various activities, familiar living materials, and space accessibility

School Learning Action Cell (SLAC) in the Context of Bangsamoro Autonomous Region in Muslim Mindanao (BARMM): A Policy Assessment

School Learning Action Cell (SLAC) in the Context of Bangsamoro Autonomous Region in Muslim Mindanao (BARMM): A Policy Assessment

Using of Action Learning Approach on Self-Empowerment of the Elderly

Purpose: This research aims to investigate the effects of using the Action Learning Approach on elderly self-empowerment and elderly satisfaction evaluation of the action learning approach on self-empowerment. Theoretical Framework:This study employs the concepts and theories of self-empowerment and action learning to create the conceptual framework. Methodology: The research was conducted through the One-Shot Case Study Design in the experimental method. Eight samples were a group of elderly from Udon Thani province, Thailand. Findings: The findings indicated that after implementing the Action Learning approach, participants’ capacity for self-empowerment displayed notable transformations in four distinct dimensions, finding the real problem, critical thinking reflection, deciding on the right way selection, and maintaining effective practice. Research Practical and Social Implication: The research also sheds light on satisfaction-related key points, including Creating new experiences, Promoting community learning, Leader activities that can create learning stimulation, Flow of activities step and Approach enjoyable. Originality/Value: Creating new experiences, Promoting community learning, Leader activities that can create learning stimulation, Flow of activities step and Approach enjoyable.

DVANet: Disentangling View and Action Features for Multi-View Action Recognition

In this work, we present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video. When trying to classify action instances captured from multiple viewpoints, there is a higher degree of difficulty due to the difference in background, occlusion, and visibility of the captured action from different camera angles. To tackle the various problems introduced in multi-view action recognition, we propose a novel configuration of learnable transformer decoder queries, in conjunction with two supervised contrastive losses, to enforce the learning of action features that are robust to shifts in viewpoints. Our disentangled feature learning occurs in two stages: the transformer decoder uses separate queries to separately learn action and view information, which are then further disentangled using our two contrastive losses. We show that our model and method of training significantly outperforms all other uni-modal models on four multi-view action recognition datasets: NTU RGB+D, NTU RGB+D 120, PKU-MMD, and N-UCLA. Compared to previous RGB works, we see maximal improvements of 1.5%, 4.8%, 2.2%, and 4.8% on each dataset, respectively. Our code can be found here: https://github.com/NyleSiddiqui/MultiView_Actions

Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning

A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or underestimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based cost constraint and instead imitates "good" trajectories and avoids "bad" trajectories generated from incrementally improving policies. We employ an oracle that utilizes a reward threshold (which is varied with learning) and the overall cost constraint to label trajectories as "good" or "bad". A key advantage of our approach is that we are able to work from any starting policy or set of trajectories and improve on it. In an exhaustive set of experiments, we demonstrate that our approach is able to outperform top benchmark approaches for solving Constrained RL problems, with respect to expected cost, CVaR cost, or even unknown cost constraints.