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Relevant bibliographies by topics / Learning from

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Academic literature on the topic 'Learning from'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Learning from"

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Kim, Hye-Jin. "Educational Paradigm Shift from E-Learning to Mobile Learning Toward Ubiquitous Learning." Journal of the Korea Academia-Industrial cooperation Society 12, no. 11 (2011): 4788–95. http://dx.doi.org/10.5762/kais.2011.12.11.4788.

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Hasırcı, Deniz, Zeynep Tuna Ultav, Hande Atmaca, and Seren Borvalı. "Learning from Turkish modern furniture design." New Trends and Issues Proceedings on Humanities and Social Sciences 2, no. 1 (2016): 126–34. http://dx.doi.org/10.18844/gjhss.v2i1.289.

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Ozier, Marcia. "Learning From Learning." Contemporary Psychology: A Journal of Reviews 30, no. 11 (1985): 883–84. http://dx.doi.org/10.1037/023329.

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R., Poornima, and Ashok L. "Problem Based Learning a Shift from Teaching Paradigm to the Learning Paradigm." Indian Journal of Dental Education 10, no. 1 (2017): 47–51. http://dx.doi.org/10.21088/ijde.0974.6099.10117.6.

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Arato, Andrew, and Ertuğ Tombuş. "Learning from success, learning from failure." Philosophy & Social Criticism 39, no. 4-5 (2013): 427–41. http://dx.doi.org/10.1177/0191453713477601.

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The article has several theses. First we propose that there is a new method of constitution-making today, the two-stage, post-sovereign one perfected in South Africa. Second, we admit the path-dependent nature, and difficult pre-conditions, of this method. Third, we maintain that even when the full method is unlikely in a given context, its legitimating principles nevertheless can play a role through international dissemination. We explore that possibility in the context of the projected comprehensive reform of Turkey, and the constitutional revolution in Egypt. It is our belief that in these contexts one can learn both from successes of the new method and also from its failures typified by the Hungarian case that we briefly present. We are unfortunately not optimistic about the success of the new method especially where actors maintain their strong belief in the constituent power of the popular sovereign. This is likely to be the case in revolutions, but can happen in reform or even during the last state of the post-sovereign method itself.

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Subrahmanyam, Kaveri, Minas Michikyan, Christine Clemmons, Rogelio Carrillo, Yalda T. Uhls, and Patricia M. Greenfield. "Learning from Paper, Learning from Screens." International Journal of Cyber Behavior, Psychology and Learning 3, no. 4 (2013): 1–27. http://dx.doi.org/10.4018/ijcbpl.2013100101.

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Electronic screens on laptop and tablet computers are being used for reading text, often while multitasking. Two experimental studies with college students explored the effect of medium and opportunities to multitask on reading (Study 1) and report writing (Study 2). In Study 1, participants (N = 120) read an easy and difficult passage on paper, a laptop, or tablet, while either multitasking or not multitasking. Neither multitasking nor medium impacted reading comprehension, but those who multitasked took longer to read both passages, indicating loss of efficiency with multitasking. In Study 2, participants (N = 67) were asked to synthesize source material in multiple texts to write a one-page evidence-based report. Participants read the source texts either on (1) paper, (2) computer screen without Internet or printer access, or (3) computer screen with Internet and printer access (called the “real-world” condition). There were no differences in report quality or efficiency between those whose source materials were paper or computer. However, global report quality was significantly better when participants read source texts on a computer screen without Internet or printer access, compared with when they had Internet and printer access. Active use of paper for note-taking greatly reduced the negative impact of Internet and printer access in the real-world condition. Although participants expressed a preference for accessing information on paper, reading the texts on paper did not make a significant difference in report quality, compared with either of the two computer conditions. Implications for formal and informal learning are discussed.

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Malik, Rahul. "Learning from Learning Curves." Joule 2, no. 9 (2018): 1637–38. http://dx.doi.org/10.1016/j.joule.2018.09.006.

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Chandler, David, and Hokyu Hwang. "Learning From Learning Theory." Journal of Management 41, no. 5 (2015): 1446–76. http://dx.doi.org/10.1177/0149206315572698.

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McDougall, Marilyn, and Rona S. Beattie. "Learning from learning groups." Journal of Management Development 14, no. 8 (1995): 35–41. http://dx.doi.org/10.1108/02621719510097406.

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Neal, Lisa. "Learning from e-learning." eLearn 2001, no. 10 (2001): 3. http://dx.doi.org/10.1145/566900.566904.

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Dissertations / Theses on the topic "Learning from"

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Goodwin, Elizabeth Eve. "Learning from Mackintosh." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/23743.

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White, David Keith. "Learning From Disasters." Thesis, University of Canterbury. Engineering Management, 2013. http://hdl.handle.net/10092/7440.

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Two projects are documented within this MEM Report: I. The first project examined what was learnt involving the critical infrastructure in the aftermath of natural disasters in the Canterbury region of New Zealand – the most prominent being the series of earthquakes between 2010 and 2011. The project identified several learning gaps, leading to recommendations for further investigations that could add significant value for the lifeline infrastructure community. II. Following the Lifeline Lesson Learnt Project, the Disaster Mitigation Guideline series was initiated with two booklets, one on Emergency Potable Water and a second on Emergency Sanitation. The key message from both projects is that we can and must learn from disasters. The projects described are part of the emergency management, and critical infrastructure learning cycles – presenting knowledge captured by others in a digestible format, enabling the lessons to be reapplied. Without these kinds of projects, there will be fewer opportunities to learn from other’s successes and failures when it comes to preparing for natural disasters.

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Cecchini, Alessandro. "Learning from Aare." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4278/.

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Supratak, Akara. "Learning from biosignals." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/60649.

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A long-standing goal of the biosignal analysis is to develop tools that can continuously collect quality biosignals, and algorithms to extract meaningful information from them. This can lead to a better understanding of our health that allows us to adjust our daily activity to be suitable for our well-being, and provide treatments promptly. However, the conventional approaches to analyze biosignals rely on the development of algorithms to extract features from the signals (i.e., hand-engineering features). It can be labor-intensive and time-consuming to develop algorithms to extract such features for particular applications repeatedly. Also, the existing machine learning based systems assume that there are annotations associated with the particular patterns of biosignals; such annotations are very expensive to obtain. In this thesis, our objective is to develop models that can automatically learn features from biosignals without utilizing any hand-engineering features and can extract meaningful information from biosignals even when the labels of biosignals are not available. Our first contribution is that we propose a method that can remotely and accurately estimate walking speeds for people with walking impairments. This work also motivates us to use deep learning to automate the expensive feature engineering process in the remaining contributions. Our second contribution is that we propose a model that can automatically extract meaningful features from raw scalp EEG signals for epileptic seizure detection. We demonstrate that it can detect seizures without utilizing any seizure annotations. Our third contribution is that we propose a model that can automatically learn features that are useful for sleep stage scoring from raw single-channel EEG data, and achieved similar performance compared to the state-of-the-art hand-engineering ones. We also demonstrated that our model can generalize to two sleep datasets that have different properties without any modifications to the model architecture. Our final contribution is that we propose a novel data-driven approach that employs a signal transcription model to capture the relationships between signals from multiple domains to detect desynchronized biosignals as anomalies without utilizing any annotations.

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Maron, Oded 1971. "Learning from Ambiguity." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47515.

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Bills, Christopher John. "Learning from experience : manifestations of young children's learning from pedagogic representations." Thesis, University of Warwick, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391936.

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Kaiser, Alexander. "Learning from the future meets Bateson's levels of learning." Emerald, 2018. http://dx.doi.org/10.1108/TLO-06-2017-0065.

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Purpose: Previous studies showed that combining learning based on experiences in the past with learning from an envisioned future scenario results in more innovative and radical ideas, as well as in a higher number of covered content domains. However, currently there is no holistic learning theory that integrates both sources of learning. The main purpose of this paper is to investigate whether it is possible to extend Bateson's theory of learning, to link these two learning sources in one coherent framework. Design/methodology/Approach: To answer this research question, the author draws on learning from an envisioned future, and tries to link it with the most important levels of learning in Bateson's framework. Findings: This paper contributes to the literature by attempting to link the important but still underexplored aspect of "learning from the future" to the complex and multifaceted work of Bateson. Given the fact that both sources of learning and experience yield a great potential to create new knowledge, this study outlines a possibility to include both sources into one learning theory. Research limitations/implications: This work provides the basis for further research in building a general holistic theory of learning to learn. Practical implications: On the individual level, the proposed approach can be easily applied with systemic coaching processes in general and coaching processes in the fields of developing an individual vision in particular. In the field of organizational learning, the awareness of different learning sources and different learning modes on the one hand and knowledge about the implementation of enabling spaces (PE-ba, FE-ba) to support these various learning modes on the other hand help organizations to generate new knowledge and create innovative and sustainable solutions, products and services. Originality/value: To the best of the author's knowledge, it is the first theoretical work that describes the integration of learning from past experiences and learning from future experiences in a methodological way.

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Suay, Halit Bener. "Reinforcement Learning from Demonstration." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/173.

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Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent's own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a humanoid robot. Interactive RL was evaluated in a simulated domain which motivated us for evaluating its practicality on a robot. Our evaluation shows that guidance reduces learning time, and that its positive effects increase with state space size. A natural follow up question after our first evaluation was, how do some other previous works compare to interactive RL. Our second contribution is an analysis of a user study, where na"ive human teachers demonstrated a real-world object catching with a humanoid robot. We present the first comparison of several previous works in a common real-world domain with a user study. One conclusion of the user study was the high potential of RL despite poor usability due to slow learning rate. As an effort to improve the learning efficiency of RL learners, our third contribution is a novel human-agent knowledge transfer algorithm. Using demonstrations from three teachers with varying expertise in a simulated domain, we show that regardless of the skill level, human demonstrations can improve the asymptotic performance of an RL agent. As an alternative approach for encoding human knowledge in RL, we investigated the use of reward shaping. Our final contributions are Static Inverse Reinforcement Learning Shaping and Dynamic Inverse Reinforcement Learning Shaping algorithms that use human demonstrations for recovering a shaping reward function. Our experiments in simulated domains show that our approach outperforms the state-of-the-art in cumulative reward, learning rate and asymptotic performance. Overall we show that human demonstrators with varying skills can help RL agents to learn tasks more efficiently.

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Kitchens, Jonathan Ashley. "Learning from the landfill." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/kitchens/KitchensJ1208.pdf.

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We are a culture of material consumption. We have created a cycle of extraction, production, distribution, consumption, and disposal that perpetuates our consumptive behavior. Our culture, way of life and economy is also hinged upon this process. We have created and enacted myths that provide reasoning, support and even a need for the continuation of this lifestyle. The byproduct, as well as the foundation for this culture, is the landfill. This thesis will explore the space created by the centralized concentration of garbage and the associated myths. This thesis will also show the necessity for a new myth and outline a new vision for the landfill encompassing the man-made landscape of this culture's discarded belongings. This project will shed light upon both the beautiful potential and decay that constitutes our landfill.

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Watkins, Christopher John Cornish Hellaby. "Learning from delayed rewards." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.330022.

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Books on the topic "Learning from"

1

Council, Higher Education Quality. Learning from audit. HEQC, 1996.

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Greig, Deborah. Learning from industry. Langham, 1988.

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Sinha, Dharni P. Learning from life. Excel Books, 2007.

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Kletz, Trevor A. Learning from accidents. 2nd ed. Butterworth-Heinemann, 1994.

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Sturgess, Jack. Learning from JTS. Further Education Unit, 1988.

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Learning from fossils. Raintree, 2007.

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Learning from conflict. Orient Longman, 1996.

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Learning from Iraq. Special Inspector General for Iraq Reconstruction, 2013.

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Fisher, Doug, and Hans-J. Lenz, eds. Learning from Data. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4.

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Shorrocks-Taylor, Diane, and Edgar W. Jenkins, eds. Learning from Others. Kluwer Academic Publishers, 2002. http://dx.doi.org/10.1007/0-306-47219-8.

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Book chapters on the topic "Learning from"

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Shishmarev, Maxim, Christopher Mears, Guido Tack, and Maria Garcia de la Banda. "Learning from Learning Solvers." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44953-1_29.

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Bleakley, Alan, John Bligh, and Julie Browne. "Learning from Learning Theory." In Medical Education for the Future. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9692-0_3.

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Warren, Scott J., and Greg Jones. "Lessons from Anytown." In Learning Games. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46829-7_11.

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Davis, Adam L. "Coming from Java." In Learning Groovy. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2117-4_5.

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Burnard, Philip. "Learning from life: experiential learning." In Acquiring Interpersonal Skills. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-7015-2_1.

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Mayes, Terry, Mike Kibby, and Tony Anderson. "Learning About Learning From Hypertext." In Designing Hypermedia for Learning. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75945-1_13.

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Nilson, Pernilla. "Learning from a Learning Study." In Understanding Science Teachers’ Professional Knowledge Growth. SensePublishers, 2015. http://dx.doi.org/10.1007/978-94-6300-313-1_9.

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Gertsen, Martine Cardel. "Learning from Difference." In Global Collaboration: Intercultural Experiences and Learning. Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137026064_13.

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Wroe, Andrew. "Learning from History." In The Republican Party and Immigration Politics. Palgrave Macmillan US, 2008. http://dx.doi.org/10.1057/9780230611085_2.

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Giordana, Attilio, and Alessandro Serra. "Learning from Mistakes." In Human and Machine Perception 3. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1361-2_7.

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Conference papers on the topic "Learning from"

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McIntosh, A. R. "Offshore Safety." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.14.

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Gatfield, D. I. "Are Current Risk Management Strategies Within The Commercial Shipping Industry Adequate?" In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.02.

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Ferguson, S. J., A. C. Landsburg, and G. C. Kraatz. "imiSs: An International Maritime Information Safety System - The Next Safety Frontier." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.07.

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Lang, J. "Learning From Marine Accidents: The Work of The Marine Accident Investigation Branch." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.11.

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Tucker, C. E., and A. J. Dean. "Risk Assessment In The Fishing Industry." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.15.

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Riding, J., and C. Whittington. "Learning From The Port Experience." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.12.

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Ayeko, M. "Integrated Safety Investigation Methodology (Isim) - A Systematic Approach To Learning From Accidents." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.08.

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Kristiansen, S., E. Koster, W. F. Schmidt, M. Olofsson, C. Guedes Soares, and P. Caridis. "A New Methodology For Marine Casualty Analysis Accounting For Human and Organisational Factors." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.01.

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Saeveraas, N., E. A. Dahle, and A. Aleksandersen. "Learning From Fishing Vessel Accidents In Norway." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.10.

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Nielsen, D. "Occupational Accidents Among Seafarers." In Learning From Marine Incidents. RINA, 1999. http://dx.doi.org/10.3940/rina.mi.1999.13.

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Reports on the topic "Learning from"

1

Seifert, Colleen M., and Edwin L. Hutchins. Learning from Error. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199117.

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Jarosch, Gregor, Ezra Oberfield, and Esteban Rossi-Hansberg. Learning from Coworkers. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25418.

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Natarajan, B. K. On Learning from Exercises. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada210593.

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Ghahramani, Zoubin, and Michael I. Jordan. Learning from Incomplete Data. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada295618.

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Anderson, John R. Learning Robustly From Instructions. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada468924.

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Goetzmann, William. Bubble Investing: Learning from History. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21693.

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Guiso, Luigi, Luigi Pistaferri, and Fabiano Schivardi. Learning Entrepreneurship From Other Entrepreneurs? National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21775.

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Porat, Sara, and Jerome A. Feldman. Learning Automata from Ordered Examples. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada206851.

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Friedman, Benjamin. Learning From the Reagan Deficits. National Bureau of Economic Research, 1992. http://dx.doi.org/10.3386/w4022.

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Kaffenberger, Michelle, and Lant Pritchett. More Schooling or More Learning? Evidence from Learning Profiles from the Financial Inclusion Insights Data. Research on Improving Systems of Education (RISE), 2017. http://dx.doi.org/10.35489/bsg-rise-wp_2017/012.

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