Relevant bibliographies by topics / Action Model Learning

Academic literature on the topic 'Action Model Learning'

Author: Grafiati
Published: 22 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Action Model Learning":

1

Rao, Dongning, and Zhihua Jiang. "Cost-Sensitive Action Model Learning." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (April 2016): 167–93. http://dx.doi.org/10.1142/s0218488516500094.

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Action model learning can relieve people from writing planning domain descriptions from scratch. Real-world learners need to be sensitive to all kinds of expenses which it will spend in the learning. However, most of previous studies in this research line only considered the running time as the learning cost. In real-world applications, we will spend extra expense when we carry out actions or get observations, particularly for online learning. The learning algorithm should apply more techniques for saving the total cost when keeping a high rate of accuracy. The cost of carrying out actions and getting observations is the dominated expense in online learning. Therefore, we design a cost-sensitive algorithm to learn action models under partial observability. It combines three techniques to lessen the total cost: constraints, filtering and active learning. These techniques are used in observation reduction in action model learning. First, the algorithm uses constraints to confine the observation space. Second, it removes unnecessary observations by belief state filtering. Third, it actively picks up observations based on the results of the previous two techniques. This paper also designs strategies to reduce the amount of plan steps used in the learning. We performed experiments on some benchmark domains. It shows two results. For one thing, the learning accuracy is high in most cases. For the other, the algorithm dramatically reduces the total cost according to the definition of cost in this paper. Therefore, it is significant for real-world learners, especially, when long plans are unavailable or observations are expensive.
2

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.
3

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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Wang, Ziyi, Xinran Li, Luoyang Sun, Haifeng Zhang, Hualin Liu, and Jun Wang. "Learning State-Specific Action Masks for Reinforcement Learning." Algorithms 17, no. 2 (January 30, 2024): 60. http://dx.doi.org/10.3390/a17020060.

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Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space into a latent space or employing environmental action masks to reduce the action possibilities. Nevertheless, these methods often lack interpretability or rely on expert knowledge. In this study, we introduce a novel method for automatically reducing the action space in environments with discrete action spaces while preserving interpretability. The proposed approach learns state-specific masks with a dual purpose: (1) eliminating actions with minimal influence on the MDP and (2) aggregating actions with identical behavioral consequences within the MDP. Specifically, we introduce a novel concept called Bisimulation Metrics on Actions by States (BMAS) to quantify the behavioral consequences of actions within the MDP and design a dedicated mask model to ensure their binary nature. Crucially, we present a practical learning procedure for training the mask model, leveraging transition data collected by any RL policy. Our method is designed to be plug-and-play and adaptable to all RL policies, and to validate its effectiveness, an integration into two prominent RL algorithms, DQN and PPO, is performed. Experimental results obtained from Maze, Atari, and μRTS2 reveal a substantial acceleration in the RL learning process and noteworthy performance improvements facilitated by the introduced approach.
5

Funai, Naoki. "An Adaptive Learning Model with Foregone Payoff Information." B.E. Journal of Theoretical Economics 14, no. 1 (January 1, 2014): 149–76. http://dx.doi.org/10.1515/bejte-2013-0043.

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AbstractIn this paper, we provide theoretical predictions on the long-run behavior of an adaptive decision maker with foregone payoff information. In the model, the decision maker assigns a subjective payoff assessment to each action based on his past experience and chooses the action that has the highest assessment. After receiving a payoff, the decision maker updates his assessments of actions in an adaptive manner, using not only the objective payoff information but also the foregone payoff information, which may be distorted. The distortion may arise from “the grass is always greener on the other side” effect, pessimism/optimism or envy/gloating; it depends on how the decision maker views the source of the information. We first provide conditions in which the assessment of each action converges, in that the limit assessment is expressed as an average of the expected objective payoff and the expected distorted payoff of the action. Then, we show that the decision maker chooses the optimal action most frequently in the long run if the expected distorted payoff of the action is greater than the ones of the other actions. We also provide conditions, under which this model coincides with the experience-weighted attraction learning, stochastic fictitious play and quantal response equilibrium models, and thus this model provides theoretical predictions for the models in decision problems.
6

Mordoch, Argaman, Brendan Juba, and Roni Stern. "Learning Safe Numeric Action Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (June 26, 2023): 12079–86. http://dx.doi.org/10.1609/aaai.v37i10.26424.

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Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Yet obtaining such an action model is a notoriously hard task. This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. Learning safe action models for planning has been recently explored for domains in which states are sufficiently described with Boolean variables. In this work, we go beyond this limitation and propose the NSAM algorithm. NSAM runs in time that is polynomial in the number of observations and, under certain conditions, is guaranteed to return safe action models. We analyze its worst-case sample complexity, which may be intractable for some domains. Empirically, however, NSAM can quickly learn a safe action model that can solve most problems in the domain.
7

Bong, Hyeon-Cheol, Yonjoo Cho, and Hyung-Sook Kim. "Developing an action learning design model." Action Learning: Research and Practice 11, no. 3 (August 11, 2014): 278–95. http://dx.doi.org/10.1080/14767333.2014.944087.

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Chalard. "Developing Learner Centered Action Learning Model." Journal of Social Sciences 7, no. 4 (April 1, 2011): 635–42. http://dx.doi.org/10.3844/jssp.2011.635.642.

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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..
10

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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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Action Model Learning":

1

Foster, Allison A. "Educational Design and Implementation of a Blended Active Learning Instructional Model for Undergraduate Gross Anatomy Education: A Multi-Modal Action Research Study." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156594215554831.

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Webb, Nicholas. "Imitation learning : does children's imitation model preference vary across different action types? /." [St. Lucia, Qld.], 2006. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19752.pdf.

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Whitbeck, Barbara Ann. "Strengths in Action: Implementing a Learning Organization Model in a Human Service Setting." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/2095.

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Although learning organization theory evolved in corporate settings, literature suggests that the theory has much to offer human service organizations. This dissertation examines the implementation of a modified learning organization model in three small field offices of a publicly-funded vocational rehabilitation organization in the Pacific Northwest, at a time when the organization was negotiating financial cutbacks and organizational changes. The model - known as Strengths in Action - was based on Senge's five learning organization disciplines, and informed by organizational culture theory. In each participating office, all staff worked together to set a goal, make a plan, and achieve the goal. This dissertation covers the implementation of the modified learning organization model; the factors that facilitated and impeded the model's implementation; the model's impact on participating offices' climate and culture; and the similarities and differences among participating offices. This primarily qualitative study utilized mixed methods: observations, interviews, and an online survey. Implementation of the model resulted in individual and team learning, better staff communication, more productive teamwork, stronger staff relationships, stronger office/community partner relationships, and improved office morale. This study shows that such a model can be effective in a human service setting, moving workgroups away from a mode of individual workers reactively handling individual cases, and toward a mode of proactive collective problem-solving. It also provides strong evidence that a learning organization model, implemented during a period of resource retrenchment, can produce substantial benefits for small workgroups within human service organizations, even when the model is not disseminated organization-wide.
4

Stoner, Alexis Marino. "A Conceptual Model Incorporating Mindfulness to Enhance Reflection in a Situated Learning Environment." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/70885.

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Key to designing instruction for situated learning is ensuring the ability of learners to transfer acquired knowledge to a variety of situations. Common to models of instruction and frameworks for situated learning is the importance of including activities for promoting reflection within the design of the learning environment. However, these models currently do not include detailed support for reflective practice that will help instructional designers prepare learners to meet the demands of situated learning. One method to meet the demand of the ill-structured nature of situated learning and provide adaptability for instructional design is through reflection-in-action and mindfulness. The purpose of this study was to apply design and development research methodologies to develop a conceptual model of reflection that incorporates mindfulness to enhance reflection-in-action within a situated learning environment. This model illustrates the relationship of incorporating mindfulness to help learners increase and direct attention to the present moment in order to improve performance through reflection-in-action. Based on the results of the study, mindfulness and reflection strategies are incorporated before, during, and after the learning experience to enhance reflection-in-action.
Ph. D.
5

Van, der Voort Geoffrey Hermanus. "An action learning model to assist circuit teams to support school management teams towards whole-school development." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1016065.

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This research study took as its point of departure the general state of underperformance of the majority of schools in South Africa. A review of the literature uncovered evidence that suggested that the state of school management in the majority of schools was in disarray. The problem was compounded by the poor quality and haphazard nature of support that District and Circuit Officials of the Education Department rendered to schools. I therefore saw the need to conduct a scientific investigation into how Circuit Teams could be assisted to support School Management Teams towards whole-school development. Against this background, I formulated the following primary research question to guide the study: “How can Circuit Teams effectively support School Management Teams of underperforming schools towards whole-school development?” The following secondary research questions arose out of this to provide further direction to the study: How can Circuit Teams assist School Management Teams to develop and implement their respective School Improvement Plans? How can Circuit Teams be assisted to develop, implement and monitor their Circuit Improvement Plans? What recommendations can be made to improve service delivery to the schools?. The primary aim of the research was therefore to design an action learning model that would enable Circuit Teams to support School Management Teams of underperforming high schools towards whole-school development. A qualitative research approach was adopted for this study, as it best suited the purpose of the research, and the philosophical assumptions of the researcher. In addition, I drew on a constructivist-interpretative and a critical theory paradigm to guide the design. I chose action research as the specific methodology for the study as, in line with critical theory it aims to empower people to facilitate social change and improvement at a local level. Purposive sampling was used to select four underperforming high schools in the same township within the Cape Town Metro, belonging to the same education Circuit and District Office to participate in the research. In addition, the members of the Circuit Team that serviced these schools were also brought on board as participants. In total, 40 people participated in the research: 4 members from the Circuit Team, 4 Principals, 8 Deputy Principals and 24 Heads of Department. Data were generated in the period January to June 2012 using structured, semi-structured and unstructured interviews, participant observation, and document analysis. Data were analysed by following the eight steps for analysing qualitative data identified by Tesch. An Action Research cycle consisting of the following five steps was followed with participants during the fieldwork, viz.: Identification of the problem; Designing the action plan; Implementing the action plan; Evaluating the action, and Reflection and lessons learnt. Two Action Research cycles emerged from the fieldwork. The first cycle dealt with assisting the schools and Circuit Team to construct their improvement plans. The main findings from this cycle were (1) that the Circuit Team did not function as a team, due to the autocratic management style of the Circuit Team Manager, and the plan of action to address the underperforming schools was not developed in a participative manner. In addition, the Circuit Team had no Circuit Improvement Plan in place with which to support the schools. (2) The schools did not receive the required support to prepare their School Improvement Plans, and although they were able to articulate their areas of support needed, none of them undertook the process of School Self-Evaluation and therefore did not have School Improvement Plans in place. The second action r esearch cycle dealt with the support that schools needed from the other pillars of the District Office to implement their intervention plans. Three themes emerged from this action research cycle: (1) The School Management Teams required capacitybuilding to manage their schools effectively, (2) teachers needed support to implement the curriculum, and (3) learners required assistance to achieve better results. As the outcome of the research, a spiral model consisting of three distinctive phases, each having several loops that describe the particular action that Circuit Teams and School Management Teams have to undertake was developed as the ultimate outcome of the research. The structure of the model was explained, and explicit guidelines for operationalizing it in practice were provided. Based on the findings and the construction of the model, a number of recommendations were put forward to guide future research and practice in the area of Circuit Team support to nderperforming schools. In conclusion, this research study contributed to the body of knowledge by exploring, investigating and describing the working relationship between Circuit Teams and School Management Teams, which until now has not been adequately covered in the existing literature and research. The study culminated in a theoretical model which can be used to improve this relationship permitting Circuit Teams to better support School Management Teams towards whole-school development. The action research design also allowed a more participative and democratic relationship to develop between the Circuit Team and the School Management Teams of the four schools, which is also an innovative idea considering the traditional hierarchic and autocratic approach which has been the norm in the past. Hopefully the findings of this study will encourage the emergence of democratic partnerships between Departmental officials and school management, leading to the empowerment and transformation of school management.
6

Mahembe, Bright. "The development and empirical evaluation of an extended learning potential structural model." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86456.

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Thesis (PhD)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: In South Africa, selection from a diverse population poses a formidable challenge. The challenge lies in subgroup difference in the performance criterion. Protected group members perform systematically lower on the criterion due to systematic, group-related differences in learning and job competency potential latent variables required to succeed in learning and on the job. These subgroup differences are attributable to the unequal development and distribution of intellectual capital across racial-ethnic subgroups due to systemic historical disadvantagement. This scenario has made it difficult for organisations in South Africa to meet equity targets when selecting applicants from a diverse group representative of the South African population, while at the same time maintaining production and efficiency targets. Therefore there is an urgent need for affirmative development. Ensuring that those admitted to affirmative development interventions successfully develop the job competency potential and job competencies required to succeed on the job requires that the appropriate people are selected into these interventions. Selection into affirmative development opportunities represents an attempt to improve the level of Learning performance during evaluation of learners admitted to affirmative development opportunities. A valid understanding of the identity of the determinants of learning performance in conjunction with a valid understanding of how they combine to determine the level of learning performance achieved should allow the valid prediction of Learning performance during evaluation. The primary objective of the present study was to integrate and elaborate the De Goede (2007) and the Burger (2012) learning potential models in a manner that circumvents the problems and shortcomings of these models by developing an extended explanatory learning performance structural model that explicates additional cognitive and non-cognitive learning competency potential latent variables that affect learning performance and that describes the manner in which these latent variables combine to affect learning performance. A total of 213 participants took part in the study. The sample was predominantly made up of students from previously disadvantaged groups on the extended degree programme of a university in the Western Cape Province of South Africa. The proposed De Goede – Burger – Mahembe Learning Potential Structural Model was tested via structural equation modeling after performing item and dimensional analyses. Item and dimensional analyses were performed to identify poor items and ensure uni-dimensionality. Uni-dimensionality is a requirement for item parcel creation. Item parcels were used due to sample size restrictions. The fit of the measurement and structural models can generally be regarded as reasonable and both models showed close fit. Significant relationships were found between: Information processing capacity and Learning Performance during evaluation; Self-leadership and Motivation to learn; Motivation to learn and Time-engaged-on-task; Self efficacy and Self-leadership; Knowledge about cognition and Regulation of cognition; Regulation of cognition and Time-cognitively-engaged; Learning goal orientation and Motivation to learn; Openness to experience and Learning goal orientation. Support was not found for the relationships between Conscientiousness and Time-cognitively-engaged, as well as between Time-cognitively-engaged and Learning performance. The hypothesised moderating effect of Prior learning on the relationship between Abstract reasoning capacity and Learning performance during evaluation was not supported. The statistical power of the test of close fit for the comprehensive LISREL model was examined. The discriminant validity of the item parcels were ascertained. The limitations of the research and suggestions for future studies have been highlighted. The results of the present study provide some important insights for educators and training and development specialists on how to identify potential students and talent for affirmative development in organisations in South Africa.
7

Byadarhaly, Kiran. "A Neuro-dynamical model of Synergistic Motor Control." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384426521.

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Nyame-Asiamah, Frank. "The deferred model of reality for designing and evaluating organisational learning processes : a critical ethnographic case study of Komfo Anokye teaching hospital, Ghana." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7582.

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The study proposed an evidence-based framework for designing and evaluating organisational learning and knowledge management processes to support continuously improving intentions of organisations such as hospitals. It demarcates the extant approaches to organisational learning including supporting technology into ‘rationalist’ and ‘emergent’ schools which utilise the dichotomy between the traditional healthcare managers’ roles and clinicians’ roles, and maintains that they are exclusively inadequate to accomplish transformative growth intentions, such as continuously improving patient care. The possibility of balancing the two schools for effective organisational learning design is not straightforward, and fails; because the balanced-view school is theoretically orientated and lack practical design to resolve power tensions entrenched in organisational structures. Prior attempts to address the organisational learning and knowledge management design and evaluation problematics in actuality have situated in the interpretivist traditions, only focusing on explanations of meanings. Critically, this is uncritical of power relations and orthodox practices. The theory of deferred action is applied in the context of critical research methods and methodology to expose the motivations behind the established organisational learning and knowledge management practices of Komfo Anokye Teaching Hospital (KATH) which assumed rationality design conceptions. Ethnographic data was obtained and interpreted with combined critical hermeneutics and narrative analyses to question the extent of healthcare learning and knowledge management systems failures and unveil the unheard voices as force for change. The study makes many contributions to knowledge but the key ones are: (i) Practically, the participants accepted the study as a catalyst for (re)-designing healthcare learning and knowledge management systems to typify the acceptance of the theory of deferred action in practice; (ii) theoretically, the cohered emergent transformation (CET) model was developed from the theory of deferred action and validated with empirical data to explain how to plan strategically to achieve transformative growth objectives; and (iii) methodologically, the sense-making of the ethnographic data was explored with the combined critical hermeneutics and critical narrative analyses, the data interpretation lens from the critical theory and qualitative pluralism positions, to elucidate how the unheard emergent voices could bring change to the existing KATH learning and knowledge management processes for improved patient care.
9

Vellala, Abhinay. "Genre-based Video Clustering using Deep Learning : By Extraction feature using Object Detection and Action Recognition." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176942.

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Social media has become an integral part of the Internet. There have been users across the world sharing content like images, texts, videos, and so on. There is a huge amount of data being generated and it has become a challenge to the social media platforms to group the content for further usage like recommending a video. Especially, grouping videos based on similarity requires extracting features. This thesis investigates potential approaches to extract features that can help in determining the similarity between videos. Features of given videos are extracted using Object Detection and Action Recognition. Bag-of-features representation is used to build the vocabulary of all the features and transform data that can be useful in clustering videos. Probabilistic model-based clustering, Multinomial Mixture model is used to determine the underlying clusters within the data by maximizing the expected log-likelihood and estimating the parameters of data as well as probabilities of clusters. Analysis of clusters is done to understand the genre based on dominant actions and objects. Bayesian Information Criterion(BIC) and Akaike Information Criterion(AIC) are used to determine the optimal number of clusters within the given videos. AIC/BIC scores achieved minimum scores at 32 clusters which are chosen to be the optimal number of clusters. The data is labeled with the genres and Logistic regression is performed to check the cluster performance on test data and has achieved 96% accuracy
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Nivens, Ryan Andrew. "Moving from Student Teaching to a Residency Model: Tennessee's Ready 2 Teach Initiative in Action." Digital Commons @ East Tennessee State University, 2013. https://dc.etsu.edu/etsu-works/232.

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Tennessee’s Ready2Teach initiative, a statewide teacher education reform, will be discussed with a description of its enactment at one regional university. Discussion will focus on how to utilize 300 hours of co-teaching in addition to student teaching.
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Books on the topic "Action Model Learning":

1

C, Wade Rahima, ed. Community action rooted in history: The CiviConnections model of service-learning. Silver Spring, Md: National Council for the Social Studies, 2007.

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University, Sheffield Hallam, ed. Credit through learning action planning: A working model : session to be held at the Records of achievement conference, University of North London, 14 March 1994 : information pack. [Sheffield]: Sheffield Hallam University, 1994.

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C, Alkin Marvin, Christie Christina A, and American Evaluation Association, eds. Theorists' models in action. San Francisco, Calif: Jossey-Bass, 2005.

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Action-Reflection Seminar (2nd 2001 Naivasha, Kenya). Strategic and responsive evaluation of peacebuilding: Towards a learning model : report of the Second Action-Reflection Seminar convened by NPI-Africa and the NCCK-CPBD Project : Naivasha, Kenya, March 2001. Nairobi: NPI-Africa, 2002.

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Delogu, Cristina, ed. Tecnologia per il web learning. Florence: Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-571-9.

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This book maps out a course through the methodological and technological innovations of internet-based training, setting the emphasis on the collaborative character of experiences of learning and on the interactivity of the virtual workshops. On the one hand, this underscores the possibilities offered by the net to make available educational modes centred on the social process that enables learning in an active manner, rather than on the centrality of contents to be passively transferred to the students. On the other hand, it also shows how in the virtual workshops it is possible to develop one's understanding of the phenomena that are the subject of learning as a result of the interaction with the phenomena themselves, reproduced in the computer, acting upon them and observing the consequences of one's own actions. The effect is to underline how this type of model of learning can help to overcome the technology gap between different countries and social groups (the digital divide) and also to make learning more accessible even to disabled students.
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Secundo, Giustina. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.

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German ICM Conference (3th 2014 Marburg). The inverted classroom model: The 3rd German ICM-Conference - proceedings. Berlin: De Gruyter Oldenbourg, 2014.

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Johnson, Bob. Models of APEL and quality assurance. London: Southern England Consortium for Credit Accumulation and Transfer, 2002.

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Keengwe, Jared, Grace Onchwari, and James N. Oigara. Promoting active learning through the flipped classroom model. Hershey, PA: Information Science Reference, 2014.

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Pak, Sang-jun. Kŏkkuro kyosil ŭl nŏmŏ kŏkkuro haksŭp ŭro: Uri nara kyosil e mannŭn kŏkkuro kyosil model ŭl ch'ajasŏ = Flipped classroom flipped learning. 8th ed. Kyŏnggi-do P'aju-si: Kyoyuk Kwahaksa, 2016.

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Book chapters on the topic "Action Model Learning":

1

Edmonstone, John. "The Energy Investment Model." In The Handbook of Action Learning, 78–82. New York: Productivity Press, 2024. http://dx.doi.org/10.4324/9781003464440-8.

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Bellmann, Matthias. "Siemens Management Learning: A Highly Integrated Model to Align Learning Processes with Business Needs." In Business Driven Action Learning, 140–51. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230285866_12.

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Rodrigues, Christophe, Henry Soldano, Gauvain Bourgne, and Céline Rouveirol. "Collaborative Online Learning of an Action Model." In Solving Large Scale Learning Tasks. Challenges and Algorithms, 300–319. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41706-6_16.

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Poole, David, and Ian D. Thomas. "The Action Learning Partnership (ALPS®) Model." In Educational Innovation in Economics and Business III, 65–75. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-1388-7_5.

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Edmonstone, John. "The energy investment model and action learning." In Action Learning in Health, Social and Community Care, 85–90. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315266701-8.

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Edmonstone, John. "The energy investment model and action learning*." In Action Learning in Health, Social and Community Care, 85–90. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]: CRC Press, 2017. http://dx.doi.org/10.4324/9781315266701-9.

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Pinto, Javier A. "Using histories to model observations in theories of action." In Learning and Reasoning with Complex Representations, 221–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64413-x_38.

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Haazebroek, Pascal, and Bernhard Hommel. "Anticipative Control of Voluntary Action: Towards a Computational Model." In Anticipatory Behavior in Adaptive Learning Systems, 31–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02565-5_3.

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McVee, Mary B., Lynn E. Shanahan, H. Emily Hayden, Fenice B. Boyd, P. David Pearson, and Jennifer Reichenberg. "Learning through a Pedagogy of Video Reflection and the Gradual Release of Responsibility Model." In Video Pedagogy in Action, 23–40. New York, NY : Routledge, 2018.: Routledge, 2017. http://dx.doi.org/10.4324/9781315175638-2.

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Hunt, Darwin P., and Michelle R. Sams. "Human Self-Assessment Process Theory: An Eight-Factor Model of Human Performance and Learning; and Everyman’s Causation." In Psychophysics in Action, 41–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74382-5_4.

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Conference papers on the topic "Action Model Learning":

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Chen, Lei, Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. "Uncertainty-Aware Representation Learning for Action Segmentation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/115.

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In this paper, we propose an uncertainty-aware representation Learning (UARL) method for action segmentation. Most existing action segmentation methods exploit continuity information of the action period to predict frame-level labels, which ignores the temporal ambiguity of the transition region between two actions. Moreover, similar periods of different actions, e.g., the beginning of some actions, will confuse the network if they are annotated with different labels, which causes spatial ambiguity. To address this, we design the UARL to exploit the transitional expression between two action periods by uncertainty learning. Specially, we model every frame of actions with an active distribution that represents the probabilities of different actions, which captures the uncertainty of the action and exploits the tendency during the action. We evaluate our method on three popular action prediction datasets: Breakfast, Georgia Tech Egocentric Activities (GTEA), and 50Salads. The experimental results demonstrate that our method achieves the performance with state-of-the-art.
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Lintilä, Taina, and Mark Zarb. "COMPUTING STUDENTS LEARNING OUTCOMES IN LEARNING BY DEVELOPING ACTION MODEL." In 13th annual International Conference of Education, Research and Innovation. IATED, 2020. http://dx.doi.org/10.21125/iceri.2020.0477.

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Nematollahi, Iman, Daniel Kuhner, Tim Welschehold, and Wolfram Burgard. "Augmenting Action Model Learning by Non-Geometric Features." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794153.

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Davoodi, Laleh, and József Mezei. "A Comparative Study of Machine Learning Models for Sentiment Analysis: Customer Reviews of E-Commerce Platforms." In Digital Restructuring and Human (Re)action. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.fov.4.2022.13.

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Understanding customers' preferences can be vital for companies to improve customer satisfaction. Reviews of products and services written by customers and published on various online platforms offer tremendous potential to gain important insights about customers' opinions. Sentiment classification with various machine learning models has been of great interest to academia and practice for a while, however, the emergence of language transformer models brings forth new avenues of research. In this article, we compare the performance of traditional machine learning models and recently introduced transformer-based techniques on a dataset of customer reviews published on the Trustpilot platform. We found that transformer-based models outperform traditional models, and one can achieve over 98% accuracy. The best performing model shows the same excellent performance independently of the store considered. We also illustrate why it can be sometimes more reliable to use the sentiment polarity assigned by the machine learning model, rather than a numeric rating that is provided by the customer.
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El-Ghaish, Hany, Mohamed Hussein, and Amin Shoukry. "Human Action Recognition Using A Multi-Modal Hybrid Deep Learning Model." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.84.

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Wang, Zi, Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomas Lozano-Perez. "Active Model Learning and Diverse Action Sampling for Task and Motion Planning." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594027.

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Boutilier, Craig, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, and Dale Schuurmans. "Planning and Learning with Stochastic Action Sets." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/650.

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In many practical uses of reinforcement learning (RL) the set of actions available at a given state is a random variable, with realizations governed by an exogenous stochastic process. Somewhat surprisingly, the foundations for such sequential decision processes have been unaddressed. In this work, we formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to provide these foundations. We show that optimal policies and value functions in this model have a structure that admits a compact representation. From an RL perspective, we show that Q-learning with sampled action sets is sound. In model-based settings, we consider two important special cases: when individual actions are available with independent probabilities, and a sampling-based model for unknown distributions. We develop polynomial-time value and policy iteration methods for both cases, and provide a polynomial-time linear programming solution for the first case.
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Lange, Robert Tjarko, and Aldo Faisal. "Action Grammars: A Cognitive Model for Learning Temporal Abstractions." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1258-0.

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Degris, T., P. M. Pilarski, and R. S. Sutton. "Model-Free reinforcement learning with continuous action in practice." In 2012 American Control Conference - ACC 2012. IEEE, 2012. http://dx.doi.org/10.1109/acc.2012.6315022.

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Berseth, Glen, Alex Kyriazis, Ivan Zinin, William Choi, and Michiel van de Panne. "Model-Based Action Exploration for Learning Dynamic Motion Skills." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593588.

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Reports on the topic "Action Model Learning":

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Whitbeck, Barbara. Strengths in Action: Implementing a Learning Organization Model in a Human Service Setting. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2093.

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Bhattacharjea, Suman, Sehar Saeed, Rajib Timalsina, and Syeed Ahamed. Citizen-led Assessments: A Model for Evidence-based Advocacy and Action to Improve Learning. Australian Council for Educational Research, June 2021. http://dx.doi.org/10.37517/978-1-74286-636-9.

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Designed as household-based assessments, citizen-led assessments (CLAs) are implemented by local organizations who assess children in their homes, thus reaching the most marginalized children, families, and communities, often in remote areas. CLAs add an essential piece of information for truly monitoring progress and help realistically represent the learning levels of all children – at national, regional, and global levels. By using simple tools and easy-to-understand reports, CLAs engage parents and community members in discussions about learning and help foster understanding of the importance of ensuring quality education through civil action. In this publication, members from organizations conducting CLAs in India, Pakistan, Nepal and Bangladesh provide an overview of the CLA model and illustrate a range of ways in which the model has been implemented in the four South Asian countries to monitor and improve learning. In all four countries, the initiative is known as the Annual Status of Education Report, or ASER – a word that means ‘impact’ in three of these four countries. By design, ASER assesses foundational reading and numeracy skills.
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Prieto Martín, Pedro, Marina Apgar, Jiniya Afroze, Amit Arulanantham, Jacqueline Hicks, Shanta Karki, Sophie Mareschal, et al. Bridging Learning and Action: How Did CLARISSA’s Participatory Adaptive Management Approach Foster Innovation, Effectiveness, and Stakeholder Empowerment? Institute of Development Studies, May 2024. http://dx.doi.org/10.19088/clarissa.2024.007.

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Child Labour: Action-Research-Innovation in South and South-Eastern Asia (CLARISSA) is an evidence and innovation-generation programme funded by the United Kingdom’s Foreign, Commonwealth & Development Office (FCDO), responding to the challenge of the worst forms of child labour (WFCL) in Bangladesh and Nepal. It is a challenge characterised by a poor understanding of its drivers and a lack of evidence on what works to combat it. To handle such fundamental uncertainty, the programme adopts a child-centric and participatory action research approach, which is supported by an adaptive management model to respond better to challenges and opportunities. From its inception, the programme needed to navigate shocks and challenges, such as Covid-19 lockdowns, political upheaval, and sustained budget cuts, which put its capacity to learn and evolve to the test. This paper shares insights emerging from evaluating CLARISSA’s participatory adaptive management (PAM) practices, connecting them with current discussions on adaptive management. It provides an in-depth evaluation of CLARISSA’s PAM approach, exploring how adaptive strategies were implemented and evolved throughout the programme’s life cycle.
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Wollentz, Gustav. Increasing future awareness in the cultural heritage sector using the SoPHIA model. Department of Cultural Sciences, Linnaeus University, 2023. http://dx.doi.org/10.15626/fkh.kv.2023.01.

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This report presents results from a project that aims at increasing future awareness in the cultural heritage sector, using the SoPHIA model. The project was run by the Centre for Applied Heritage at Linnaeus University, with funding from the university. Work on the report was carried out in2021 and 2022 by NCK (The Nordic Centre of Heritage Learning and Creativity AB) under the direction of Gustav Wollentz, in co-operation with Kalmar County Museum, Jamtli Museum, andDaniel Laven from the Department of Economics, Geography, Law and Tourism at Mid Sweden University. Results from the project show that the model succeeded in exploring possible future effects of a heritage intervention, defined as any action that results in a physical change to an element of a historic place, and related these effects to prioritized issues for societal development, such as participation, inclusion, and wellbeing. It managed to expand the range of potential action in the present. Furthermore, it also provided a useful tool for identifying significant areas where there is the potential to think more innovatively and creatively regarding future change and effects. The model helped in identifying the necessary steps and actions needed for realizing the interventionin accordance with a desirable scenario. The model failed in anticipating long-term futures or futures radically different from the present. It mostly provided insights into how the intervention could have an impact upon future change, but not on how future change would have an impact upon the intervention. Ways of adapting the model for increased future awareness are suggested.These include ways to make the model more suitable for anticipating long-term futures as well as futures of radical change.
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Fullan, Michael, and Joanne Quinn. How Do Disruptive Innovators Prepare Today's Students to Be Tomorrow's Workforce?: Deep Learning: Transforming Systems to Prepare Tomorrow’s Citizens. Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002959.

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Disruptive innovators take advantage of unique opportunities. Prior to COVID-19 progress in Latin America and the Caribbean for integrating technology, learning, and system change has been exceedingly slow. In this paper we first offer a general framework for transforming education. The framework focuses on the provision of technology, innovative ideas in learning and well-being, and what we call systemness which are favorable change factors at the local, middle/regional, and policy levels. We then take up the matter of system reform in Latin America and the Caribbean noting problems and potential. Then, we turn to a specific model in system change that we have developed called New Pedagogies for Deep Learning, a model developed in partnerships with groups of schools in ten countries since 2014. The model consists of three main components: 6 Global Competences (character, citizenship, collaboration, communication, creativity, and critical thinking), 4 learning elements (pedagogy, learning partnerships, learning environments, leveraging digital), and three system conditions (school culture, district/regional culture, and system policy). We offer a case study of relative success based on Uruguay with whom we have been working since 2014. Finally, we identify steps and recommendations for next steps in Latin America for taking action on system reform in the next perioda time that we consider critical for taking advantage of the current pandemic disruption. The next few years will be crucial for either attaining positive breakthroughs or slipping backwards into a reinforced status quo.
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Cohn, David A., Zoubin Ghahramani, and Michael I. Jordan. Active Learning with Statistical Models. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada295617.

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Hickman McMahon, Lauren, Stefani Pautz Stephenson, and Seth Corrigan. The Promise of Digital Math Tools for Universally Accessible Mathematics Instruction. Digital Promise, May 2024. http://dx.doi.org/10.51388/20.500.12265/211.

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In this report, we share insights from a Research-Practice-Industry Partnership (RPIP) that explored mathematics instructional practices with support of digital mathematics tools. RPIPs bring together researchers, practitioners, and product developers, with each party having an equal voice, in a rapid-cycle model for edtech research and development. We framed this RPIP using the principles of Universal Design for Learning (UDL): provide multiple means of Engagement, Representation, and Action & Expression. These principles support learner variability, improve accessibility, and are closely aligned with mathematics education research, which highlights the multimodal nature of mathematics and the need to use and connect multiple representations to help students develop understanding of mathematics concepts.
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Pautz Stephenson, Stefani, Rebecca Banks, and Merijke Coenraad. Outcomes of Increased Practitioner Engagement in Edtech Development: How Strong, Sustainable Research-Practice-Industry Partnerships will Build a Better Edtech Future. Digital Promise, June 2022. http://dx.doi.org/10.51388/20.500.12265/158.

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A new participatory research model, Research-Practice-Industry Partnerships (RPIP), presents a unique value proposition. Design processes have typically placed professional designers, not the end users, at the center of the work. RPIPs create an intentional feedback loop that transforms the knowledge, action, or goals of all involved parties (Baker et al., 2022). RPIP aims to create better designs for scalable technologies that both meet the needs of educators and incorporate research from the learning sciences. This yields a product more likely to be used, used appropriately, and have the desired impact for learners. Digital Promise partnered with edtech startup Merlyn Mind and the University of California, Irvine (UCI) in an RPIP. This white paper describes our engagement and suggests that this model can yield positive impacts and new learning for all participants. Surveys and interviews with participants showed that engagement was mutually valued among all parties, practitioners learned more about AI and edtech in general, and Merlyn Mind staff learned more about working with schools and educators. Practitioners also benefited from the networking and collaboration that participation in the RPIP brought and felt it helped them grow professionally.
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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced.
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Bynum, Nora, Georgina Cullman, Margret Domroese, Carol Fialkowski, and Eleanor J. Sterling. Student-Active Teaching Techniques. American Museum of Natural History, 2009. http://dx.doi.org/10.5531/cbc.ncep.0027.

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This module introduces a variety of methods for active teaching and learning, guided by research on learning models and styles. Studies show that active teaching and learning techniques engage students and help them to understand and apply what they learn. This module covers learning models to guide teachers in engaging their students and introduces a variety of methods for active teaching: interactive lectures, discussions, case studies, collaborative learning (group work and peer teaching), and classroom and field exercises.

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