Action Learning Research Articles

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.

Live and Learn: Continual Action Clustering with Incremental Views

Multi-view action clustering leverages the complementary information from different camera views to enhance the clustering performance. Although existing approaches have achieved significant progress, they assume all camera views are available in advance, which is impractical when the camera view is incremental over time. Besides, learning the invariant information among multiple camera views is still a challenging issue, especially in continual learning scenario. Aiming at these problems, we propose a novel continual action clustering (CAC) method, which is capable of learning action categories in a continual learning manner. To be specific, we first devise a category memory library, which captures and stores the learned categories from historical views. Then, as a new camera view arrives, we only need to maintain a consensus partition matrix, which can be updated by leveraging the incoming new camera view rather than keeping all of them. Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are optimized. The empirical experimental results on 6 realistic multi-view action collections demonstrate the excellent clustering performance and time/space efficiency of the CAC compared with 15 state-of-the-art baselines.

NaRuto: Automatically Acquiring Planning Models from Narrative Texts

Domain model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Learning action models from narrative texts in an automated way is essential to overcome this barrier, but challenging because of the inherent complexities of such texts. We present an evaluation of planning domain models derived from narrative texts using our fully automated, unsupervised system, NaRuto. Our system combines structured event extraction, predictions of commonsense event relations, and textual contradictions and similarities. Evaluation results show that NaRuto generates domain models of significantly better quality than existing fully automated methods, and even sometimes on par with those created by semi-automated methods, with human assistance.

Revelation as Scientific in its Method

This paper is an exploration of Shoghi Effendi’s statement that Bahá’u’lláh’s Revelation is “scientifi c in its method,” starting from the various ways that scientific methods are implemented and including perspectives from the philosophy of science. We examine the role of diversity in achieving scientific objectivity and escape from bias, and consider how the Bahá’í process of action, reflection, consultation, and study is consistent with modern understandings of the scientific method. Because this form of learning in action can be used by everybody, and because ethical, moral, and spiritual practices are part of its way of doing things, it provides a powerful extension of science and its methods that is available to everyone and allows ready integration of spiritual values into the process.

Becoming Attuned to Reality

What is entailed in accurately reading reality? The main thesis of this paper is that the learning mode of action, reflection, consultation, and study encourages investigators to attend to at least six “interplays” which, together, work to facilitate collaborative readings that are progressively attuned to reality. Owing to these interplays, this mode of learning in action helps to weed out disabling or harmful presuppositions and corresponding ideas, making it possible to discover and retain enabling or beneficial ones. By doing so, it resolves the longstanding problem of how to conceptualize objectivity and cultivate it in the search for truth.

Responding to the UN sustainability goals in transdisciplinary partnership through network action learning

AbstractThe global water crisis, an enormous concern according to the World Economic Forum, poses a significant challenge to long-term sustainability, exacerbated by the high energy demand associated with water supply and treatment. As the renewable energy sector grows, the need for green technologies to support the water-energy nexus becomes evident. However, mere technological advancements are insufficient to address complex water-related challenges. This paper presents a transdisciplinary collaborative effort involving engineers, geographers, management researchers, and environmentalists working with practitioners in a cross-border network. The study explores through action learning research how, in a transdisciplinary partnership, network action learning influences the exploration and implementation of novel green technology and the development of innovation capabilities. The research is structured around three themes: green technology platforms, policy support and guidance, and dissemination and collaboration. It identifies the factors impacting technology exploration and application and how concurrently green innovation capabilities are developed. The study emphasizes the significance of transdisciplinary collaboration and offers valuable insights into addressing UN Sustainability Goals related to clean water, sustainable industry, and partnerships. It contributes to innovation capability theory and provides practical guidance to researchers, practitioners, and policymakers, emphasizing the need for holistic approaches to address the water-energy crisis and achieve sustainable development.

Word-object and action-object learning in a unimodal context during early childhood

Abstract Word-object and action-object learning in children aged 30 to 48 months appears to develop at a similar time scale and adheres to similar attentional constraints. However, children below 36 months show different patterns of learning word-object and action-object associations when this information is presented in a bimodal context (Eiteljoerge et al., 2019b). Here, we investigated 12- and 24-month-olds’ word-object and action-object learning when this information is presented in a unimodal context. Forty 12- and 24-month-olds were presented with two novel objects that were either first associated with a novel label (word learning task) and then later with a novel action (action learning task) or vice versa. In subsequent yoked test phases, children either heard one of the novel labels or saw a hand performing one of the actions presented with the two objects on screen while we measured their target looking. Generalized linear mixed models indicate that 12-month-olds learned action-object associations but not word-object associations and 24-month-olds learned neither word- nor action-object associations. These results extend previous findings (Eiteljoerge et al., 2019b) and, together, suggest that children appear to learn action-object associations early in development while struggling with learning word-object associations in certain contexts until 2 years of age.

Implementation of Problem-Based Learning Model to Improve Science Learning Outcomes in Grade V Students of Elementary Schools GMIM Pinaras

Starting froml the problemls found at GMLIML Pinaras Elemlentary School regarding low student learning outcomles caused by the domlinance of conventional learning processes, researchers conducted research at the school by imlplemlenting an innovative learning mlodel. The aiml of this research is to find out how to apply the Probleml Based Learning MLodel to imlprove science learning outcomles for fifth grade students at GMLIMLPinaras Elemlentary School. Researchers used a classrooml action research (PTK) design including four stages, namlely: (1) planning, (2) action, (3) observation, and (4) reflection which was carried out in two cycles. The subjects of this research were fifth grade students at GMLIML Pinaras Elemlentary School using the Probleml Based Learning learning mlodel. The data collection technique was carried out through observation, namlely by directly observing the imlplemlentation of learning actions and written tests to determline the extent to which students were able to understand the mlaterial froml the two cycles. The results obtained in cycle I reached 69%, while in cycle II student learning outcomles reached 88%. Students understood and were able to achieve the expected learning objectives. Based on the results of the research and discussion, it can be concluded that GMLIML Pinaras Elemlentary School teachers are expected to apply the Probleml Based Learning learning mlodel to imlprove the science learning outcomles of fifth grade students at GMLIML Pinaras Elemlentary School.