Relevant bibliographies by topics / Continuous Learning

Contents

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

Academic literature on the topic 'Continuous Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Continuous Learning"

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Geeganage, Dakshi T. K., and Asoka S. Karunananda. "Ontology Driven Continuous Learning Approach." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 37–42. http://dx.doi.org/10.7763/ijke.2015.v1.6.

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Williams, Ruth. "Continuous learning." Nursing Management 24, no. 6 (2017): 11. http://dx.doi.org/10.7748/nm.24.6.11.s11.

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Martin, Alec. "Continuous learning partnership." Education + Training 27, no. 5 (1985): 132–34. http://dx.doi.org/10.1108/eb017135.

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The consultative paper Towards an Adult Training Strategy, published by the Manpower Services Commission in 1983, explicitly recognised and sought to foster wider appreciation of the need for systematic continuous learning throughout adult life. Adults will come from four decades; from various backgrounds of culture and language; from work, leisure and unemployment. If the opportunities of this situation are seized, a massive commitment will have been made to the development of the learning society. Inevitably this will mean commitment to the Open Society which, as Karl Popper long ago pointed
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Mahida, Ankur. "A Review on Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 1967–70. http://dx.doi.org/10.21275/sr24314131827.

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van Breda-Verduijn, Hester, and Marjoleine Heijboer. "Learning culture, continuous learning, organizational learning anthropologist." Industrial and Commercial Training 48, no. 3 (2016): 123–28. http://dx.doi.org/10.1108/ict-11-2015-0074.

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Purpose – The purpose of this paper is to clarify the way an organizational culture forms the right breeding ground for continuous learning. More and more organizations feel the urgency for innovation and continuous improvement. Learning is a key issue in this. A powerful culture of learning forms an effective breeding ground for continuous learning. That is the reason why in this paper the concept of “learning culture” will be analyzed: how will it contribute to continuous improvement and innovation? The authors will answer this question by taking the perspective of an organizational learning
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Stratford, Elaine. "Collaboration and continuous learning." Geographical Research 60, no. 2 (2022): 216–17. http://dx.doi.org/10.1111/1745-5871.12539.

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Hylton, Preetee. "'Continuous learning is contagious'." BDJ Team 8, no. 10 (2021): 34–36. http://dx.doi.org/10.1038/s41407-021-0769-7.

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Day, George S. "Continuous Learning about Markets." California Management Review 36, no. 4 (1994): 9–31. http://dx.doi.org/10.2307/41165764.

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Rowold, Jens, and Jan Schilling. "Career‐related continuous learning." Career Development International 11, no. 6 (2006): 489–503. http://dx.doi.org/10.1108/13620430610692917.

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Day, George S. "Continuous learning about markets." Planning Review 20, no. 5 (1992): 47–49. http://dx.doi.org/10.1108/eb054381.

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Dissertations / Theses on the topic "Continuous Learning"

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Effraimidis, Dimitros. "Computation approaches for continuous reinforcement learning problems." Thesis, University of Westminster, 2016. https://westminsterresearch.westminster.ac.uk/item/q0y82/computation-approaches-for-continuous-reinforcement-learning-problems.

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Optimisation theory is at the heart of any control process, where we seek to control the behaviour of a system through a set of actions. Linear control problems have been extensively studied, and optimal control laws have been identified. But the world around us is highly non-linear and unpredictable. For these dynamic systems, which don’t possess the nice mathematical properties of the linear counterpart, the classic control theory breaks and other methods have to be employed. But nature thrives by optimising non-linear and over-complicated systems. Evolutionary Computing (EC) methods exploit
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Welby-Solomon, Vanessa. "The continuous learning cycle. Investigating possibilities for experiential learning." Thesis, University of the Western Cape, 2015. http://hdl.handle.net/11394/5357.

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Magister Educationis (Adult Learning and Global Change) - MEd(AL)<br>Scholars focusing on experiential learning argue that experience should be considered as critical for adult learning. This research paper frames experiential learning within a Constructivist framework. This paper focuses on an investigation into the ways that facilitators use the Continuous Learning Cycle, a model for learning based on Kolb's Learning Cycle, to facilitate learning through experience during the triad skills observation role-play in a workshop, which is part of an induction programme, for a retail bank. Indicat
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Johannemann, Jonathan. "COAL : a continuous active learning system." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111453.

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Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 59-60).<br>In this thesis, our objective is to enable businesses looking to enhance their product by varying its attributes, where effectiveness of the new product is assessed by humans. To achieve this, we mapped the task to a machine learning problem. The solution is two fold: learn a non linear model that can map the attribute space to the human response, which can then be used to make pred
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Boyer, Eric. "Continuous auditory feedback for sensorimotor learning." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066165/document.

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Notre système sensorimoteur a développé une relation particulière entre nos actions et le retour sonore qui en découle. Les systèmes de captation gestuelle et les technologies audio permettent de manipuler ce retour sonore par la sonification interactive du mouvement. Nous explorons dans divers cadres expérimentaux la contribution de la sonification à l'apprentissage moteur dans les systèmes interactifs. Tout d'abord, nous montrons que le système auditif intègre des indices acoustiques issus du mouvement pour le contrôle moteur. Des représentations de l'espace émergent de ces indices et sont t
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Nichols, B. "Reinforcement learning in continuous state- and action-space." Thesis, University of Westminster, 2014. https://westminsterresearch.westminster.ac.uk/item/967w8/reinforcement-learning-in-continuous-state-and-action-space.

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Reinforcement learning in the continuous state-space poses the problem of the inability to store the values of all state-action pairs in a lookup table, due to both storage limitations and the inability to visit all states sufficiently often to learn the correct values. This can be overcome with the use of function approximation techniques with generalisation capability, such as artificial neural networks, to store the value function. When this is applied we can select the optimal action by comparing the values of each possible action; however, when the action-space is continuous this is not p
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Stefano, Alexandra di. "Beyond the rhetoric : a grounded perspective on learning company and learning community relationships." Thesis, Open University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343744.

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Dahlberg, Leslie. "Evolutionary Computation in Continuous Optimization and Machine Learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35674.

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Evolutionary computation is a field which uses natural computational processes to optimize mathematical and industrial problems. Differential Evolution, Particle Swarm Optimization and Estimation of Distribution Algorithm are some of the newer emerging varieties which have attracted great interest among researchers. This work has compared these three algorithms on a set of mathematical and machine learning benchmarks and also synthesized a new algorithm from the three other ones and compared it to them. The results from the benchmark show which algorithm is best suited to handle various machin
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Tappen, Marshall Friend 1976. "Learning continuous models for estimating intrinsic component images." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37878.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Also issued in pages.<br>MIT Rotch Library copy: issued in pages.<br>Includes bibliographical references (leaves 137-144).<br>The goal of computer vision is to use an image to recover the characteristics of a scene, such as its shape or illumination. This is difficult because an image is the mixture of multiple characteristics. For example, an edge in an image could be caused by either an edge on a surface or a change in the surface's color. Distinguishing the effects of diffe
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Tummala, Akhil. "Self-learning algorithms applied in Continuous Integration system." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16675.

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Context: Continuous Integration (CI) is a software development practice where a developer integrates a code into the shared repository. And, then an automated system verifies the code and runs automated test cases to find integration error. For this research, Ericsson’s CI system is used. The tests that are performed in CI are regression tests. Based on the time scopes, the regression test suites are categorized into hourly and daily test suits. The hourly test is performed on all the commits made in a day, whereas the daily test is performed at night on the latest build that passed the hourly
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Pinto, Rafael Coimbra. "Continuous reinforcement learning with incremental Gaussian mixture models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157591.

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A contribução original desta tese é um novo algoritmo que integra um aproximador de funções com alta eficiência amostral com aprendizagem por reforço em espaços de estados contínuos. A pesquisa completa inclui o desenvolvimento de um algoritmo online e incremental capaz de aprender por meio de uma única passada sobre os dados. Este algoritmo, chamado de Fast Incremental Gaussian Mixture Network (FIGMN) foi empregado como um aproximador de funções eficiente para o espaço de estados de tarefas contínuas de aprendizagem por reforço, que, combinado com Q-learning linear, resulta em performance com
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Books on the topic "Continuous Learning"

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United States. Dept. of Labor. Office of the Assistant Secretary for Administration and Management, ed. Continuous learning catalog: Continuous learning : everybody's business. U.S. Dept. of Labor, Office of the Assistant Secretary for Administration and Management, 2005.

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Canada. Dept. of National Defence. Directorate of Continuous Learning Strategies. Manager's guide to continuous learning. National Defence, 1996.

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Canadian Centre for Management Development., ed. Continuous learning: A CCMD report : summary. Canadian Centre for Management Development, 1994.

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Rao, K. Sudha. Influence of continuous evaluation on learning. National Council of Educational Research and Training, 1986.

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Yasin, Mahmuddin. Organisasi, manajemen, leadership: Studi transformasi BUMN : pentingnya continuous learning dan continuous improvement. Exposé, 2014.

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Saenz, Maria Jesus, Eduardo Ubaghs, and Alejandra Isabel Cuevas. Enabling Horizontal Collaboration Through Continuous Relational Learning. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08093-2.

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Oosthuizen, Izak. Self-directed learning research: An imperative for transforming the educational landscape. AOSIS, 2016.

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Robert, Howard. The Learning imperative: Managing people for continuous innovation. Harvard Business School Press], 1993.

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Manuel, London, ed. Continuous learning in organizations: Individual, group, and organizational perspectives. Lawrence Erlbaum, 2005.

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Wardrop, Alex. The Para-Academic Handbook: A Toolkit for Making-Learning-Creating-Acting. Intellect, 2014.

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Book chapters on the topic "Continuous Learning"

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Akers, L. T. "Continuous Learning." In ACS Symposium Series. American Chemical Society, 2010. http://dx.doi.org/10.1021/bk-2010-1055.ch007.

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Hijfte, Stijn Van. "Continuous Learning." In Make Your Organization a Center of Innovation. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6507-9_6.

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John, C. Frederic. "Continuous Learning." In Storytelling and Market Research. Routledge, 2021. http://dx.doi.org/10.4324/9781003202516-10.

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Carter, Eric, and Matthew Hurst. "Continuous Delivery." In Agile Machine Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5107-2_3.

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Gruber, Susan, and Mark J. van der Laan. "Bounded Continuous Outcomes." In Targeted Learning. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_7.

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Bishop, Christopher M., and Hugh Bishop. "Continuous Latent Variables." In Deep Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-45468-4_16.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, et al. "Continuous Attribute." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_172.

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Moeller, John, Vivek Srikumar, Sarathkrishna Swaminathan, Suresh Venkatasubramanian, and Dustin Webb. "Continuous Kernel Learning." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46227-1_41.

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Bang, Henning, and Thomas Nesset Midelfart. "Continuous Team Learning." In Effective Management Teams and Organizational Behavior. Routledge, 2021. http://dx.doi.org/10.4324/9781003053552-18.

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Ducoulombier, Antoine, and Michèle Sebag. "Continuous mimetic evolution." In Machine Learning: ECML-98. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026704.

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Conference papers on the topic "Continuous Learning"

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Vignau, Benjamin, Patrice Clémente, Pascal Berthomé, and Joseph Kawalec. "Continuous learning: a feasible solution for continuous authentication using PPG?" In 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2024. http://dx.doi.org/10.1109/ijcb62174.2024.10744508.

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Adorni, Giovanni, and Daniele Grosso. "CONTINUOUS EDUCATION FOR TEACHER EMPOWERMENT." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1530.

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Runkel, Christina, Ander Biguri, and Carola-Bibiane Schönlieb. "Continuous Learned Primal Dual." In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2024. http://dx.doi.org/10.1109/mlsp58920.2024.10734760.

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Spears, Tyler, and P. Thomas Fletcher. "Learning Spatially-Continuous Fiber Orientation Functions." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635838.

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Drăgan, Theodora-Augustina, Alexander Künzner, Robert Wille, and Jeanette Lorenz. "Continuous Quantum Reinforcement Learning for Robot Navigation." In Workshop on Quantum Artificial Intelligence and Optimization 2025. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013371800003890.

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Zlobin, O. N., V. L. Litvinov, and F. V. Filippov. "Domain-Specific Language Models for Continuous Learning." In 2025 VI International Conference on Neural Networks and Neurotechnologies (NeuroNT). IEEE, 2025. https://doi.org/10.1109/neuront66873.2025.11049984.

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Nakasone, Gentoku, Yoshinari Motokawa, Yuki Miyashita, and Toshiharu Sugawara. "Efficient Retraining for Continuous Operability Through Strategic Directives." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00215.

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Dhakan, Paresh, Kathryn Elizabeth Merrick, Inaki Rano, and Nazmul Haque Siddique. "Modular Continuous Learning Framework." In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2018. http://dx.doi.org/10.1109/devlrn.2018.8761008.

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Cerman, Otto, and Petr Husek. "Self-learning continuous controllers." In Vision (ICARCV 2010). IEEE, 2010. http://dx.doi.org/10.1109/icarcv.2010.5707210.

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Vasilateanu, Andrei, and A. G. Turcus. "CHATBOT FOR CONTINUOUS MOBILE LEARNING." In 11th International Conference on Education and New Learning Technologies. IATED, 2019. http://dx.doi.org/10.21125/edulearn.2019.0525.

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Reports on the topic "Continuous Learning"

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Baird, III, Klopf Leemon C., and A. H. Reinforcement Learning With High-Dimensional, Continuous Actions. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada280844.

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Griffith, David, Susan Heller-Zeisler, Joy Herman, et al. Providing NIST Supervisors with a Continuous Learning Program. National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7776.

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Chernozhukov, Victor, Greg Lewis, Vasilis Syrgkanis, and Mert Demirer. Semi-Parametric Efficient Policy Learning with Continuous Actions. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.3419.

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Meadors, Grant, Shira Goldhaber-Gordon, and Lexington Smith. Deep learning to help find continuous gravitational waves. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1830555.

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Lee, Ying-Ying, and Kyle Colangelo. Double debiased machine learning nonparametric inference with continuous treatments. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.5419.

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Lee, Ying-Ying, and Kyle Colangelo. Double debiased machine learning nonparametric inference with continuous treatments. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.7219.

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Buckell, Chris, and Mairi Macintyre. Sustaining Continuous Improvement in Public Sector Services Through Double Loop Learning. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317335.

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Ghavamzadeh, Mohammad, Sridhar Mahadevan, and Rajbala Makar. Extending Hierarchical Reinforcement Learning to Continuous-Time, Average-Reward, and Multi-Agent Models. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada445107.

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Stone, Peter, and Manuela Veloso. Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function,. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada303088.

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Ariza H., Angela Andrea. Integrating Student Teachers’ Learning through an E-portfolio. Institución Universitaria Colombo Americana, 2025. https://doi.org/10.26817/paper.30.

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This working paper reports on an action research study aimed to explore and analyze the process of creating and using an E-portfolio of evidence to promote integrative learning of Student Teachers (STs) to further their self-discovery as EFL Teachers. The Process of creating the portfolio was framed within one Practicum semester and viewed as a capstone experience addressed from the perspective of an integrative learning approach. The construction of portfolios was carried out in three phases that had a progressive and transformative intention. The analysis and the interpretation of the data r
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