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Relevant bibliographies by topics / Action Model Learning

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Journal articles
Dissertations / Theses
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Academic literature on the topic 'Action Model Learning'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "Action Model Learning"

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Rao, Dongning, and Zhihua Jiang. "Cost-Sensitive Action Model Learning." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (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

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Wang, Zhenyi, Ping Yu, Yang Zhao, et al. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (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

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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 (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. Th

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

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Funai, Naoki. "An Adaptive Learning Model with Foregone Payoff Information." B.E. Journal of Theoretical Economics 14, no. 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 th

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Mordoch, Argaman, Brendan Juba, and Roni Stern. "Learning Safe Numeric Action Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (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

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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 pa

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Bong, Hyeon-Cheol, Yonjoo Cho, and Hyung-Sook Kim. "Developing an action learning design model." Action Learning: Research and Practice 11, no. 3 (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 (2011): 635–42. http://dx.doi.org/10.3844/jssp.2011.635.642.

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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

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Dissertations / Theses on the topic "Action Model Learning"

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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.

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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

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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 de

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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.<br>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 systemi

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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. T

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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 Actio

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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"

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C, Wade Rahima, ed. Community action rooted in history: The CiviConnections model of service-learning. 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 Hallam University, 1994.

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C, Alkin Marvin, Christie Christina A, and American Evaluation Association, eds. Theorists' models in action. 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. NPI-Africa, 2002.

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Delogu, Cristina, ed. Tecnologia per il web learning. 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 on

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Aldo, Romano, and SpringerLink (Online service), eds. Dynamic Learning Networks: Models and Cases in Action. Springer-Verlag US, 2009.

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

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American Association for Artificial Intelligence. Workshop. Learning action models: Papers from the 1993 AAAI workshop, July 12, Washington, D.C.. AAAI Press, 1994.

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Keengwe, Jared, and Grace Onchwari. Handbook of research on active learning and the flipped classroom model in the digital age. Information Science Reference, 2016.

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Ann, Mabrouk Patricia, American Chemical Society. Division of Analytical Chemistry, and American Chemical Society. Division of Chemical Education, eds. Active learning: Models from the analytical sciences. American Chemical Society, 2007.

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Book chapters on the topic "Action Model Learning"

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Edmonstone, John. "The Energy Investment Model." In The Handbook of Action Learning. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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Aineto, Diego, and Enrico Scala. "Action Model Learning with Guarantees." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/75.

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This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypotheses that are consistent with the learning samples. Our theoretical findings are instantiated in an online algorithm that maintains a compact representation of all solutions of the problem. Among this range of solutions, we bring attention to action models approximating the actual transition system from below (sound models) and from above (complet

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Balyo, Tomáš, Martin Suda, Lukáš Chrpa, et al. "Planning Domain Model Acquisition from State Traces without Action Parameters." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/76.

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Existing planning action domain model acquisition approaches consider different types of state traces from which they learn. The differences in state traces refer to the level of observability of state changes (from full to none) and whether the observations have some noise (the state changes might be inaccurately logged). However, to the best of our knowledge, all the existing approaches consider state traces in which each state change corresponds to an action specified by its name and all its parameters (all objects that are relevant to the action). Furthermore, the names and types of all th

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Snehalatha, N., S. Mohana Kumar, H. Kavitha, S. Nisha, Puttabore Gowda, and Sj Krishna Prasad. "Sign Language Detection Using Action Recognition LSTM Deep Learning Model." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10699301.

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R, Divya Rani, and Prabhakar C. J. "Human Action Recognition in Videos Using Inception-v4 Deep Learning Model." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690470.

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Wang, Jiangxing, Jiachen Li, Xiao Han, Deheng Ye, and Zongqing Lu. "Language Model Adaption for Reinforcement Learning with Natural Language Action Space." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.89.

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Barg, Feline, Eric Veith, and Lasse Hammer. "Transfer Learning in Deep Reinforcement Learning: Actor-Critic Model Reuse for Changed State-Action Space." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013304900003890.

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Senbo, Jin. "Taekwondo action recognition model based on deep learning and key point matching technology." In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). IEEE, 2024. https://doi.org/10.1109/ispcem64498.2024.00077.

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Chaturved, E. Samatha Sree, and L. Mary Gladence. "A Novel Reinforcement Learning-Based Behavior Pattern Analytics Model for Action and Learning-Plan Recommendation in Children." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691127.

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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}. 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 p

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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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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, 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, 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 importan

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Prieto Martín, Pedro, Marina Apgar, Jiniya Afroze, et al. Bridging Learning and Action: How Did CLARISSA’s Participatory Adaptive Management Approach Foster Innovation, Effectiveness, and Stakeholder Empowerment? Institute of Development Studies, 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 managem

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Cheng, DingXin. Development of the Roadway Pothole Management Program. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2306.

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Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pot

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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 sh

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Valdivia-Vizarreta, Paloma, and Johana Evelyn Montalvan Castilla. Flexibility and Playful Learning as Strategies for a Transformative Pedagogy in Higher Education. University of Stavanger, 2024. http://dx.doi.org/10.31265/usps.295.

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This essay explores how flexibility and playful learning address the current challenges in higher education, aligned with the principles of transformative pedagogy. This pedagogical approach views the university as an inclusive and adaptable space, where teaching aligns with students’ realities and needs, promoting accessibility, engagement, and holistic development. Flexibility enables students to manage their learning in a personalised manner, adapting to their individual paces, schedules, and environments (whether in-person or virtual), fostering autonomy and a better balance between person

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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, 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 poten

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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, 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

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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, 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 des

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Cohn, David A., Zoubin Ghahramani, and Michael I. Jordan. Active Learning with Statistical Models. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada295617.

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