Letteratura scientifica selezionata sul tema "Action Model Learning"
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Articoli di riviste sul tema "Action Model Learning":
Rao, Dongning, e Zhihua Jiang. "Cost-Sensitive Action Model Learning". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, n. 02 (aprile 2016): 167–93. http://dx.doi.org/10.1142/s0218488516500094.
Wang, Zhenyi, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan e Changyou Chen. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 07 (3 aprile 2020): 12281–88. http://dx.doi.org/10.1609/aaai.v34i07.6911.
Chang, Kyungwon. ""A Model of Action Learning Program Design in Higher Education"". Journal of Educational Technology 27, n. 3 (30 settembre 2011): 475–505. http://dx.doi.org/10.17232/kset.27.3.475.
Wang, Ziyi, Xinran Li, Luoyang Sun, Haifeng Zhang, Hualin Liu e Jun Wang. "Learning State-Specific Action Masks for Reinforcement Learning". Algorithms 17, n. 2 (30 gennaio 2024): 60. http://dx.doi.org/10.3390/a17020060.
Funai, Naoki. "An Adaptive Learning Model with Foregone Payoff Information". B.E. Journal of Theoretical Economics 14, n. 1 (1 gennaio 2014): 149–76. http://dx.doi.org/10.1515/bejte-2013-0043.
Mordoch, Argaman, Brendan Juba e Roni Stern. "Learning Safe Numeric Action Models". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 10 (26 giugno 2023): 12079–86. http://dx.doi.org/10.1609/aaai.v37i10.26424.
Bong, Hyeon-Cheol, Yonjoo Cho e Hyung-Sook Kim. "Developing an action learning design model". Action Learning: Research and Practice 11, n. 3 (11 agosto 2014): 278–95. http://dx.doi.org/10.1080/14767333.2014.944087.
Chalard. "Developing Learner Centered Action Learning Model". Journal of Social Sciences 7, n. 4 (1 aprile 2011): 635–42. http://dx.doi.org/10.3844/jssp.2011.635.642.
Pandey, Ritik, Yadnesh Chikhale, Ritik Verma e Deepali Patil. "Deep Learning based Human Action Recognition". ITM Web of Conferences 40 (2021): 03014. http://dx.doi.org/10.1051/itmconf/20214003014.
Amir, E., e A. Chang. "Learning Partially Observable Deterministic Action Models". Journal of Artificial Intelligence Research 33 (20 novembre 2008): 349–402. http://dx.doi.org/10.1613/jair.2575.
Tesi sul tema "Action Model Learning":
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.
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.
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.
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.
Ph. D.
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.
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.
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.
Byadarhaly, Kiran. "A Neuro-dynamical model of Synergistic Motor Control". University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384426521.
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.
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.
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.
Libri sul tema "Action Model Learning":
C, Wade Rahima, a cura di. Community action rooted in history: The CiviConnections model of service-learning. Silver Spring, Md: National Council for the Social Studies, 2007.
University, Sheffield Hallam, a cura di. 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.
C, Alkin Marvin, Christie Christina A e American Evaluation Association, a cura di. Theorists' models in action. San Francisco, Calif: Jossey-Bass, 2005.
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.
Delogu, Cristina, a cura di. Tecnologia per il web learning. Florence: Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-571-9.
Secundo, Giustina. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.
German ICM Conference (3th 2014 Marburg). The inverted classroom model: The 3rd German ICM-Conference - proceedings. Berlin: De Gruyter Oldenbourg, 2014.
Johnson, Bob. Models of APEL and quality assurance. London: Southern England Consortium for Credit Accumulation and Transfer, 2002.
Keengwe, Jared, Grace Onchwari e James N. Oigara. Promoting active learning through the flipped classroom model. Hershey, PA: Information Science Reference, 2014.
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. 8a ed. Kyŏnggi-do P'aju-si: Kyoyuk Kwahaksa, 2016.
Capitoli di libri sul tema "Action Model Learning":
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.
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.
Rodrigues, Christophe, Henry Soldano, Gauvain Bourgne e 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.
Poole, David, e 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.
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.
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.
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.
Haazebroek, Pascal, e 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.
McVee, Mary B., Lynn E. Shanahan, H. Emily Hayden, Fenice B. Boyd, P. David Pearson e 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.
Hunt, Darwin P., e 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.
Atti di convegni sul tema "Action Model Learning":
Chen, Lei, Muheng Li, Yueqi Duan, Jie Zhou e 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.
Lintilä, Taina, e 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.
Nematollahi, Iman, Daniel Kuhner, Tim Welschehold e 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.
Davoodi, Laleh, e 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.
El-Ghaish, Hany, Mohamed Hussein e 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.
Wang, Zi, Caelan Reed Garrett, Leslie Pack Kaelbling e 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.
Boutilier, Craig, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov e 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.
Lange, Robert Tjarko, e 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.
Degris, T., P. M. Pilarski e 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.
Berseth, Glen, Alex Kyriazis, Ivan Zinin, William Choi e 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.
Rapporti di organizzazioni sul tema "Action Model Learning":
Whitbeck, Barbara. Strengths in Action: Implementing a Learning Organization Model in a Human Service Setting. Portland State University Library, gennaio 2000. http://dx.doi.org/10.15760/etd.2093.
Bhattacharjea, Suman, Sehar Saeed, Rajib Timalsina e Syeed Ahamed. Citizen-led Assessments: A Model for Evidence-based Advocacy and Action to Improve Learning. Australian Council for Educational Research, giugno 2021. http://dx.doi.org/10.37517/978-1-74286-636-9.
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, maggio 2024. http://dx.doi.org/10.19088/clarissa.2024.007.
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.
Fullan, Michael, e 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, dicembre 2020. http://dx.doi.org/10.18235/0002959.
Cohn, David A., Zoubin Ghahramani e Michael I. Jordan. Active Learning with Statistical Models. Fort Belvoir, VA: Defense Technical Information Center, gennaio 1995. http://dx.doi.org/10.21236/ada295617.
Hickman McMahon, Lauren, Stefani Pautz Stephenson e Seth Corrigan. The Promise of Digital Math Tools for Universally Accessible Mathematics Instruction. Digital Promise, maggio 2024. http://dx.doi.org/10.51388/20.500.12265/211.
Pautz Stephenson, Stefani, Rebecca Banks e 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, giugno 2022. http://dx.doi.org/10.51388/20.500.12265/158.
de Luis, Mercedes, Emilio Rodríguez e Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, settembre 2023. http://dx.doi.org/10.53479/33560.
Bynum, Nora, Georgina Cullman, Margret Domroese, Carol Fialkowski e Eleanor J. Sterling. Student-Active Teaching Techniques. American Museum of Natural History, 2009. http://dx.doi.org/10.5531/cbc.ncep.0027.