Academic literature on the topic 'Learning framework'
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Journal articles on the topic "Learning framework"
Koggalahewa, Darshika N., and Asoka S. Karunananda. "Ontology Guided Semantic Self Learning Framework." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 30–36. http://dx.doi.org/10.7763/ijke.2015.v1.5.
Full textNordin, Norazah, Mohamed Amin Embi, and Melor Md Yunus. "Mobile Learning Framework for Lifelong Learning." Procedia - Social and Behavioral Sciences 7 (2010): 130–38. http://dx.doi.org/10.1016/j.sbspro.2010.10.019.
Full textNaw, Naw. "Work-based learning: A learning strategy in support of the Myanmar education framework." Universal Academic Research Journal 4, no. 1 (January 1, 2022): 22–31. http://dx.doi.org/10.17220/tuara.2022.01.03.
Full textBooth, Marion. "Learning disability award framework." Paediatric Nursing 15, no. 1 (February 1, 2003): 6. http://dx.doi.org/10.7748/paed.15.1.6.s16.
Full textFraihat, Salam, and Qusai Shambour. "A Framework of Semantic Recommender System for e-Learning." Journal of Software 10, no. 3 (March 2015): 317–30. http://dx.doi.org/10.17706/jsw.10.3.317-330.
Full textHoai Nam, Nguyen, Vu Thai Giang, and Vu Dang Luat. "B-LEARNING ISSUES: A SUGGESTION FOR DEVELOPING THE FRAMEWORK." Journal of Science, Educational Science 61, no. 11 (2016): 57–65. http://dx.doi.org/10.18173/2354-1075.2016-0216.
Full textPirani, Zainab, Vasiqullah Molvizadah, Mohammad Abbas Sayyed, and Sasikumar M. "E-Learning Framework for Learning Disabled Children." International Journal of Computer Applications 63, no. 19 (February 15, 2013): 38–42. http://dx.doi.org/10.5120/10577-5703.
Full textFayek, Haytham M., Lawrence Cavedon, and Hong Ren Wu. "Progressive learning: A deep learning framework for continual learning." Neural Networks 128 (August 2020): 345–57. http://dx.doi.org/10.1016/j.neunet.2020.05.011.
Full textTran, Hien Minh Thi, and Farshid Anvari. "A Five-Dimensional Requirements Elicitation Framework for e-Learning Systems." International Journal of Information and Electronics Engineering 6, no. 3 (2016): 185–91. http://dx.doi.org/10.18178/ijiee.2016.6.3.621.
Full textR. Dhaya, R. Dhaya, R. Kanthavel R. Dhaya, and Kanagaraj Venusamy R. Kanthavel. "AI Based Learning Model Management Framework for Private Cloud Computing." 網際網路技術學刊 23, no. 7 (December 2022): 1633–42. http://dx.doi.org/10.53106/160792642022122307017.
Full textDissertations / Theses on the topic "Learning framework"
Ghali, Fawaz. "Social personalized e-learning framework." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/35247/.
Full textDesimone, Roberto V. "Learning control knowledge within an explanation-based learning framework." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/18827.
Full textUgur, Emre. "A Developmental Framework For Learning Affordances." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612754/index.pdf.
Full texts discovery of action possibilities is realized in two sequential phases. In the first phase, the robot that initially possesses a limited number of basic actions and reflexes discovers new behavior primitives by exercising these actions and by monitoring the changes created in its initially crude perception system. In the second phase, the robot explores a more complicated environment by executing the discovered behavior primitives and using more advanced perception to learn further action possibilities. For this purpose, first, the robot discovers commonalities in action-effect experiences by finding effect categories, and then builds predictors for each behavior to map object features and behavior parameters into effect categories. After learning affordances through self-interaction and self-observation, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. Mobile and manipulator robots were used to realize the proposed framework. Similar to infants, these robots were able to form behavior repertoires, learn affordances, and gain prediction capabilities. The learned affordances were shown to be relative to the robots, provide perceptual economy and encode general relations. Additionally, the affordance-based planning ability was verified in various tasks such as table cleaning and object transportation.
Nimmer, Natalie E. "Documenting A Marshallese Indigenous Learning Framework." Thesis, University of Hawai'i at Manoa, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10757762.
Full textWhile many Marshallese learners thrive in school environments, far more have struggled to find academic success, both at home and abroad. While this has been documented by educational researchers for decades, there is a dearth of research about how Marshallese students learn most effectively. Examining culturally-sustaining educational models that have resulted in successful student outcomes in other indigenous groups can inform strategies to improve educational experiences for Marshallese students. Understanding how recognized Marshallese experts in a range of fields have successfully learned and passed on knowledge and skills is important to understanding how formal school environments can be shaped to most effectively support Marshallese student learning.
This study examines the learning and teaching experiences of recognized Marshallese holders of traditional and contemporary knowledge and skills, in order to document a Marshallese indigenous learning framework. This research used bwebwenato (talk story) as a research method, to learn from the experiences of ten Marshallese experts in knowledge and skills ranging from sewing to linguistics and from canoe-making to business.
Key findings include the four key components of a Marshallese indigenous learning framework: • Relationships • Motivation for Learning • Teaching Strategies • Extending Networks Teaching strategies are comprised of the commonalities among the way Marshallese have learned and mastered both traditional and contemporary skills. Chief among these are: introducing the topic at a young age, scaffolding, demonstrating and observing, learning through relevant practice, and correcting learners constructively. To a lesser extent, and in a context in which the learner and teacher are not related in a familial way, learning and teaching occurs through visual aids and asking instructor for assistance.
Holte, R. C. "An analytical framework for learning systems." Thesis, Brunel University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379412.
Full textWood, Mark A. "An agent-independent task learning framework." Thesis, University of Bath, 2008. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492246.
Full textTenenbaum, Joshua B. (Joshua Brett) 1972. "A Bayesian framework for concept learning." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16714.
Full textIncludes bibliographical references (p. 297-314).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples can provide a complete picture of how people generalize concepts in even this simple setting. This thesis proposes a new computational framework for understanding how people learn concepts from examples, based on the principles of Bayesian inference. By imposing the constraints of a probabilistic model of the learning situation, the Bayesian learner can draw out much more information about a concept's extension from a given set of observed examples than either rule-based or similarity-based approaches do, and can use this information in a rational way to infer the probability that any new object is also an instance of the concept. There are three components of the Bayesian framework: a prior probability distribution over a hypothesis space of possible concepts; a likelihood function, which scores each hypothesis according to its probability of generating the observed examples; and the principle of hypothesis averaging, under which the learner computes the probability of generalizing a concept to new objects by averaging the predictions of all hypotheses weighted by their posterior probability (proportional to the product of their priors and likelihoods). The likelihood, under the assumption of randomly sampled positive examples, embodies the size principle for scoring hypotheses: smaller consistent hypotheses are more likely than larger hypotheses, and they become exponentially more likely as the number of observed examples increases. The principle of hypothesis averaging allows the Bayesian framework to accommodate both rule-like and similarity-like generalization behavior, depending on how peaked the posterior probability is. Together, the size principle plus hypothesis averaging predict a convergence from similarity-like generalization (due to a broad posterior distribution) after very few examples are observed to rule-like generalization (due to a sharply peaked posterior distribution) after sufficiently many examples have been observed. The main contributions of this thesis are as follows. First and foremost, I show how it is possible for people to learn and generalize concepts from just one or a few positive examples (Chapter 2). Building on that understanding, I then present a series of case studies of simple concept learning situations where the Bayesian framework yields both qualitative and quantitative insights into the real behavior of human learners (Chapters 3-5). These cases each focus on a different learning domain. Chapter 3 looks at generalization in continuous feature spaces, a typical representation of objects in psychology and machine learning with the virtues of being analytically tractable and empirically accessible, but the downside of being highly abstract and artificial. Chapter 4 moves to the more natural domain of learning words for categories of objects and shows the relevance of the same phenomena and explanatory principles introduced in the more abstract setting of Chapters 1-3 for real-world learning tasks like this one. In each of these domains, both similarity-like and rule-like generalization emerge as special cases of the Bayesian framework in the limits of very few or very many examples, respectively. However, the transition from similarity to rules occurs much faster in the word learning domain than in the continuous feature space domain. I propose a Bayesian explanation of this difference in learning curves that places crucial importance on the density or sparsity of overlapping hypotheses in the learner's hypothesis space. To test this proposal, a third case study (Chapter 5) returns to the domain of number concepts, in which human learners possess a more complex body of prior knowledge that leads to a hypothesis space with both sparse and densely overlapping components. Here, the Bayesian theory predicts and human learners produce either rule-based or similarity-based generalization from a few examples, depending on the precise examples observed. I also discusses how several classic reasoning heuristics may be used to approximate the much more elaborate computations of Bayesian inference that this domain requires. In each of these case studies, I confront some of the classic questions of concept learning and induction: Is the acquisition of concepts driven mainly by pre-existing knowledge or the statistical force of our observations? Is generalization based primarily on abstract rules or similarity to exemplars? I argue that in almost all instances, the only reasonable answer to such questions is, Both. More importantly, I show how the Bayesian framework allows us to answer much more penetrating versions of these questions: How does prior knowledge interact with the observed examples to guide generalization? Why does generalization appear rule-based in some cases and similarity-based in others? Finally, Chapter 6 summarizes the major contributions in more detailed form and discusses how this work ts into the larger picture of contemporary research on human learning, thinking, and reasoning.
by Joshua B. Tenenbaum.
Ph.D.
Thorne, Elizabeth Ann. "A framework for effective management learning." Thesis, Liverpool John Moores University, 2002. http://researchonline.ljmu.ac.uk/4926/.
Full textChakravarty, Saurabh. "A Large Collection Learning Optimizer Framework." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78302.
Full textMaster of Science
LUCZAJ, JEROME ERIC. "A FRAMEWORK FOR E-LEARNING TECHNOLOGY." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1054225415.
Full textBooks on the topic "Learning framework"
David, Campbell. Learning consultation: A systemic framework. London: Karnac Books, 1995.
Find full textBritish Columbia. Ministry of Education. Year 2000 : a framework for learning. [Victoria, BC]: Province of British Columbia, Ministry of Education, 1990.
Find full textH, Hoppe Michael, and Sayles Leonard R, eds. Managing across cultures: A learning framework. Greensboro, N.C: Center for Creative Leadership, 1996.
Find full textDiagnosing learning disorders: A neuropsychological framework. 2nd ed. New York: Guilford Press, 2009.
Find full textBritish Columbia. Ministry of Education. Year 2000: A framework for learning. [Victoria, B.C.]: Province of British Columbia, Ministry of Education, 1990.
Find full textErneling, Christina E. Understandinglanguage acquisition: The framework of learning. Albany: State University of New York Press, 1993.
Find full textMelzer, Philipp. A Conceptual Framework for Personalised Learning. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-23095-1.
Full textHolte, Robert Craig. An analytical framework for learning systems. Uxbridge: Brunel University, 1988.
Find full textDiagnosing learning disorders: A neuropsychological framework. New York: Guilford Press, 1991.
Find full textBook chapters on the topic "Learning framework"
Vasudevan, Shriram K., Sini Raj Pulari, and Subashri Vasudevan. "The Deep Learning Framework." In Deep Learning, 65–79. New York: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003185635-4.
Full textLall, Sanjaya. "The Analytical Framework." In Learning to Industrialize, 1–22. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1007/978-1-349-18798-0_1.
Full textEtaati, Leila. "Bot Framework." In Machine Learning with Microsoft Technologies, 335–53. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3658-1_19.
Full textShane, Jon. "Theoretical Framework." In Learning from Error in Policing, 7–15. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00041-1_2.
Full textKim, Sangkyun, Kibong Song, Barbara Lockee, and John Burton. "Gamification Framework." In Gamification in Learning and Education, 59–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47283-6_7.
Full textArgote, Linda. "Organization Learning: A Theoretical Framework." In Organizational Learning, 31–56. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5251-5_2.
Full textKim, Steven H. "Introduction and Framework 1." In Learning and Coordination, 1–19. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1016-7_1.
Full textCentea, Dan, Mo Elbestawi, Ishwar Singh, and Tom Wanyama. "SEPT Learning Factory Framework." In Smart Industry & Smart Education, 354–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95678-7_40.
Full textKelly, Wendy. "The Relational Learning Framework." In Understanding Children in Foster Care, 119–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65376-1_6.
Full textLeke, Collins Achepsah, and Tshilidzi Marwala. "Deep Learning Framework Analysis." In Studies in Big Data, 147–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01180-2_10.
Full textConference papers on the topic "Learning framework"
Ibrahim, Mubaraka Sani, and Mohamed Hamada. "Adaptive learning framework." In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, 2016. http://dx.doi.org/10.1109/ithet.2016.7760738.
Full textDhakan, 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.
Full textHarrington, Kyler. "Distributed autonomous learning framework." In Disruptive Technologies in Information Sciences III, edited by Misty Blowers, Russell D. Hall, and Venkateswara R. Dasari. SPIE, 2019. http://dx.doi.org/10.1117/12.2519963.
Full textMaulitz, Russell, and Debra McGrath. "The active learning framework." In the 7th international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/502716.502777.
Full textAravind, C. V., Siva Kumar Sivanesan, and S. Ramesh. "Reinforced learning experience framework." In 8TH BRUNEI INTERNATIONAL CONFERENCE ON ENGINEERING AND TECHNOLOGY 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0114287.
Full textShao, Jingyu, Qing Wang, and Fangbing Liu. "Learning to Sample: An Active Learning Framework." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00064.
Full textImran, Ali Shariq, and Faouzi Alaya Cheikh. "Multimedia learning objects framework for e-learning." In 2012 International Conference on e-Learning and e-Technologies in Education (ICEEE). IEEE, 2012. http://dx.doi.org/10.1109/icelete.2012.6333417.
Full textTsai, Yi-Shan, Pedro Manuel Moreno-Marcos, Kairit Tammets, Kaire Kollom, and Dragan Gašević. "SHEILA policy framework." In LAK '18: International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3170358.3170367.
Full textSun, Sally, and Martin Smith. "PERUSALL INTEGRATION FRAMEWORK." In 11th International Conference on Education and New Learning Technologies. IATED, 2019. http://dx.doi.org/10.21125/edulearn.2019.0928.
Full textNikou, Stavros. "MOBILE LEARNING TEACHER COMPETENCIES FRAMEWORK." In 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.0827.
Full textReports on the topic "Learning framework"
Day, James. Virtual Faculty Learning Community Implementation Framework. ERAU, February 2020. http://dx.doi.org/10.15394/2020.2473.
Full textHart, Stephen, Shichao Ou, John Sweeney, and Rod Grupen. A Framework for Learning Declarative Structure. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459921.
Full textStout, Andrew, George D. Konidaris, and Andrew G. Barto. Intrinsically Motivated Reinforcement Learning: A Promising Framework for Developmental Robot Learning. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440079.
Full textSquire, Kevin M., Stephen E. Levinson, and Patrick Gordon Xavier. A robotic framework for semantic concept learning. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/919146.
Full textDavis, Cathlyn. Summative Evaluation: UFERN Framework Professional Learning Community. Oregon State University, March 2022. http://dx.doi.org/10.5399/osu/1153.
Full textRay, Jaideep, Fulton Wang, and Christopher Young. A Multi-Instance learning Framework for Seismic Detectors. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1673169.
Full textBonk, Curtis J., and Robert A. Wisher. Applying Collaborative and e-Learning Tools to Military Distance Learning: A Research Framework. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada393677.
Full textPopovi, Zoran. Engage: A Game Based Learning and Problem Solving Framework. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada562150.
Full textPopovic, Zoran. ENGAGE: A Game Based Learning and Problem Solving Framework. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada564820.
Full textPopovic, Zoran. ENGAGE: A Game Based Learning and Problem Solving Framework. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada564831.
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