Relevant bibliographies by topics / Learning approach

Academic literature on the topic 'Learning approach'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Learning approach"

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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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Prata, David N., Patrick Letouze, Stefano Cerri, and Evandro Costa. "A Game Approach to Assessing Learning Outcomes." International Journal of Information and Education Technology 6, no. 2 (2016): 137–42. http://dx.doi.org/10.7763/ijiet.2016.v6.673.

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Kamath, Dr R. S., Dr S. S. Jamsandekar, and Dr P. G. Naik. "Machine Learning Approach for Employee Attrition Analysis." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-FIIIIPM2019 (March 20, 2019): 62–67. http://dx.doi.org/10.31142/ijtsrd23065.

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Kurkin, Andrey Vladimirovich, and Anna Aleksandrovna Myshkina. "Distance learning approach." Nedelya nauki Sankt-Peterburgskogo gosudarstvennogo morskogo tekhnicheskogo universiteta 2, no. 4 (2020): 43. http://dx.doi.org/10.52899/9785883036063_598.

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Aboulnoor, A., and L. Aboulnoor. "Learning System Approach." Egyptian Journal for Engineering Sciences and Technology 9, no. 1 (June 1, 2005): 9–10. http://dx.doi.org/10.21608/eijest.2005.96628.

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Gopal, Jagadeesh. "An Approach for Facial Recognition Using Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 137–43. http://dx.doi.org/10.5373/jardcs/v12sp3/20201247.

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A, Tanko. "A Machine Learning Approach to Modeling Pore Pressure." Petroleum & Petrochemical Engineering Journal 4, no. 1 (2020): 1–6. http://dx.doi.org/10.23880/ppej-16000213.

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Machine Learning techniques and applications have lately gained a lot of interest in many areas, including spheres of arithmetic, finances, engineering, dialectology, and a lot more. This is owing to the upwelling of ground-breaking and sophisticated machine learning procedures to exceedingly multifaceted complications along with the prevailing advances in high speed computing. Numerous usages of Machine learning in daily life include pattern recognition, automation, data processing and analysis, and so on. The Petroleum industry is not lagging behind also. On the contrary, machine learning approaches have lately been applied to enhance production, forecast recoverable hydrocarbons, augment well placement by means of pattern recognition, optimize hydraulic fracture design, and to help in reservoir characterization. In this paper, three different machine learning models were trained and utilized to explore the feasibility of forecasting pore pressure of a well. The machine learning algorithms include, Simple Linear Regression, Decision Stump and Multilayer Perceptron (ANN). The predictive accuracies of the algorithm was analyzed using statistical measures. Five (5) parameters were utilized as input variables in the models: hydrostatic pressure, overburden pressure, observed and normal sonic velocities and pore pressure. 80% of the data was used in training while the remaining 20% was used for testing of the models. A sensitivity analysis of the five variable was conducted so as to identify correlations of the variables. Results of the sensitivity analysis revealed that both hydrostatic and overburden pressures appear to have the strongest correlation with pore pressure (0.766) and closely followed by normal compacted sonic velocity (0.753). Meanwhile, observed sonic velocity has the least correlation (0.046). The models were appraised by determining their Relative Absolute Errors. Results indicate that Multilayer Perceptron has the best prediction and least Relative Absolute Error of 5.77%. While the Decision Stump model had a Relative absolute error of 54.41%. The Simple Linear Regression had a relative absolute error of 67.93%. By and large, all three models appear to be suitable for modeling pore pressure but the Multilayer Perceptron is the most accurate.
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Dingli, Alexiei, and Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

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Tabassum, Kanij, Afsana A. Memi, Nasrin Sultana, Ahmed W. Reza, and Surajit D. Barman. "Food and Formalin Detector Using Machine Learning Approach." International Journal of Machine Learning and Computing 9, no. 5 (October 2019): 609–14. http://dx.doi.org/10.18178/ijmlc.2019.9.5.847.

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Chouiten, Mehdi, and Romain Ekert. "A Machine Learning Approach to Spinoff Investment Optimization." International Journal of Trade, Economics and Finance 9, no. 4 (August 2018): 182–85. http://dx.doi.org/10.18178/ijtef.2018.9.4.612.

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Dissertations / Theses on the topic "Learning approach"

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Ouyang, Li. "Motivation, cultural values, learning processes, and learning in Chinese students." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1340.

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Bertsimas, Dimitris J., and Georgia Perakis. "Dynamic Pricing: A learning Approach." Massachusetts Institute of Technology, Operations Research Center, 2001. http://hdl.handle.net/1721.1/5314.

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We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, but rather assume parametric families of demand functions that are learned over time. We first consider the noncompetitive case and present dynamic programming algorithms of increasing computational intensity with incomplete state information for jointly estimating the demand and setting prices as time evolves. Our computational results suggest that dynamic programming based methods outperform myopic policies often significantly. We then extend our analysis in a competitive environment with two firms. We introduce a more sophisticated model of demand learning, in which the price elasticities are slowly varying functions of time, and allows for increased flexibility in the modeling of the demand. We propose methods based on optimization for jointly estimating the Firm's own demand, its competitor's demand, and setting prices. In preliminary computational work, we found that optimization based pricing methods offer increased expected revenue for a firm independently of the policy the competitor firm is following.
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Liston, Karina. "A mature approach to learning /." Title page, table of contents and abstract only, 1994. http://web4.library.adelaide.edu.au/theses/09ARPS/09arpsl773.pdf.

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Ribeiro, de Mello Carlos Eduardo. "Active Learning : an unbiased approach." Phd thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://tel.archives-ouvertes.fr/tel-01000266.

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Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.
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Lai, Ling-yan Edith. "Effects of cooperative learning on student learning outcomes and approaches to learning in sixth form geography." Click to view the E-thesis via HKUTO, 1991. http://sunzi.lib.hku.hk/HKUTO/record/B38627292.

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Morri, Francesco. "A thermodynamic approach to deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Neural Networks are an incredibly powerful tool used to solve complex problems. The actual functioning of this tool and its behaviour when applied to different kind of problems is not completely explain though. In this work we study the behaviour of a neural network, used to classify images, through a physical model, based on statistical thermodynamics. We found interesting results regarding the temperature of the different components of the network, that may be exploited in a more efficient training algorithm.
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Kong, Dan. "Learning-based approach for vision problems /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.

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CASTRO, THAIS HELENA CHAVES DE. "SYSTEMATIC APPROACH FOR GROUP PROGRAMMING LEARNING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18366@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A investigação aqui relatada trata da concepção de elementos estruturantes para ampliar as oportunidades de intervenção pelo professor em um contexto de aprendizagem de programação em grupo. A partir de uma série de estudos de caso com turmas de calouros em cursos de computação, foi desenvolvida a sistematização de práticas, metodologias e tecnologias em uma abordagem para apoiar a aprendizagem de programação em grupo, baseada em três frentes de investigação: pressupostos pedagógicos, ferramentas LMS e métodos de colaboração. O eixo teórico referente à aprendizagem é a teoria de desenvolvimento cognitivo de Piaget, aliada a técnicas conhecidas de programação em grupo utilizadas no ensino de graduação em disciplinas introdutórias de programação. As ferramentas computacionais são utilizadas para monitorar e intervir durante o processo de aprendizagem. Nesse contexto, ambientes CSCL incentivam a colaboração e regulam as práticas desejadas. Nesta tese, outras tecnologias, como linguagens para representação de agentes e identificação de padrões são agregadas a eles para melhorar o acompanhamento e facilitar a intervenção. Por fim, como método de colaboração, é proposto um esquema progressivo de aprendizagem de programação em grupo, que auxilia os alunos a gradativamente adotarem práticas colaborativas na resolução de exercícios e que pode ser formalizado para incorporação a plataformas automatizadas.
The research reported here deals with devising structuring elements that may broaden intervention opportunities from the teacher in a context of group programming learning. Based on a set of case studies with freshmen in computing courses a systematization for practices, methods and technologies was developed producing an approach for supporting group programming based in three investigation paths: pedagogical assumptions, CSCL environments and collaboration methods. The main learning rationale is Jean Piaget’s Cognitive Development Theory, used alongside group programming techniques commonly applied in undergraduate introductory programming courses. Computational tools are used to monitor and intervene during learning process and in such context, CSCL environments encourage collaboration and regulate expected practices. In this thesis other technologies like languages for agent representation and patterning identification are also exploited for improving control and facilitate interventions. Finally, as collaboration method, it is proposed a Programming Progressive Learning Scheme that helps students to adopt collaborative practices when solving exercises and that can be formalized to be used with automated platforms.
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Chung, Ryan Kyong-doc. "Deep learning approach to metagenomic binning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119755.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-41).
Understanding the diversity and abundance of microbial populations is paramount to the health of humans and the environment. Estimating the diversity of these populations from whole metagenome shotgun (WMS) sequencing reads is difficult because the size of these datasets and overlapping reads limit what kinds of analysis we can do. Current methods require matching reads to a database of known microbes. These methods are either too slow or lack the sensitivity needed to identify novel species. We propose a convolutional neural network (CNN) based approach to metagenomic binning that embeds reads into a low-dimensional vector space based on taxonomic classification. We show that our method can get the speed and sensitivity necessary taxonomic classification. Our method was able to achieve 13% accuracy on identifying novel genus of bacteria as compared to 7% accuracy of k-mer embedding. At the same time, the speed of our method is within an order of magnitude of that of k-mer embedding, making it viable as a metagenomic analysis tool.
by Ryan Kyong-doc Chung.
M. Eng.
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Klivans, Adam R. "A complexity theoretic approach to learning." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8395.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.
Includes bibliographical references (leaves 127-138).
This thesis details a new vantage point for attacking longstanding problems in machine learning. We use tools from computational complexity theory to make progress on problems from computational learning theory. Our methods yield the fastest and most expressive algorithms to date for learning several fundamental concept classes: * We show that any s-term DNF over n variables can be computed by a polynomial threshold function of order O(n1/3 log s). As an immediate consequence we obtain the fastest known DNF learning algorithm which runs in time 2O(n1/3). * We give the first polynomial time algorithm to learn an intersection of a constant number of halfspaces under the uniform distribution to within any constant error parameter. We also give the first quasipolynomial time algorithm for learning any function of a constant number of halfspaces with polynomial bounded weights under any distribution. * We give an algorithm to learn constant-depth polynomial-size circuits augmented with majority gates under the uniform distribution using random examples only. For circuits which contain a polylogarithmic number of majority gates the algorithm runs in quasipolynomial time. Under a suitable cryptographic assumption we show that these are the most expressive circuits which will admit a non-trivial learning algorithm. Our approach relies heavily on giving novel representations of well known concept classes via complexity theoretic reductions. We exploit the fact that many results in computational learning theory have a complexity theoretic analogue or implication. As such,
(cont.) we also obtain new results in computational complexity including (1) a proof that the 30 year old lower bound due to Minsky and Papert [88] on the degree of a perceptron computing a DNF formula is tight and (2) improved constructions of pseudo-random generators, mathematical objects which play a fundamental role in cryptography and derandomization.
by Adam Richard Klivans.
Ph.D.
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Books on the topic "Learning approach"

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Kellogg, Karen. Learning communities. Washington, DC: ERIC Clearinghouse on Higher Education, Institute for Education Policy Studies, Graduate School of Education and Human Development, the George Washington University, 1999.

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Ellerman, David P. The indirect approach. Washington, DC: World Bank, 2000.

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Søren, Brunak, ed. Bioinformatics: The machine learning approach. Cambridge, Mass: MIT Press, 1998.

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Clarke, Andrew. Torts: A practical learning approach. 2nd ed. Chatswood, N.S.W: LexisNexis Butterworths, 2011.

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Heathcote, P. M. Computing: An active-learning approach. London: DP Publications, 1991.

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Lewis, Jackson, and Caffarella Rosemary S. 1946-, eds. Experiential learning: A new approach. San Francisco: Jossey-Bass, 1994.

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Søren, Brunak, ed. Bioinformatics: The machine learning approach. 2nd ed. Cambridge, Mass: MIT Press, 2001.

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Ken, Griffiths. A learning approach to change. Aldershot, Hampshire, England: Gower, 1998.

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Heathcote, P. M. Computing: An active-learning approach. 2nd ed. London: DP Publications, 1996.

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Boyne, Roy. Adopting a learning outcomes approach. Newcastle upon Tyne: MARCET, 1993.

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Book chapters on the topic "Learning approach"

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Rechtman, Janet E. "From Active Learning to Activist Learning." In The Foxfire Approach, 115–20. Rotterdam: SensePublishers, 2016. http://dx.doi.org/10.1007/978-94-6300-564-7_14.

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Gormezano, Isidore. "Learning: Conditioning approach." In Encyclopedia of psychology, Vol. 5., 5–8. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10520-003.

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Ganguly, Santanu. "Machine Learning." In Quantum Machine Learning: An Applied Approach, 41–97. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_2.

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Isaacs, Barbara. "Learning in Montessori settings." In Understanding the Montessori Approach, 67–81. Edition two. | New York : Routledge, 2018. | “First edition published by Routledge 2012”—T.p. verso.: Routledge, 2018. http://dx.doi.org/10.4324/9781315536880-6.

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Menzel, Wolfam. "Mathematics Approach to Learning." In Adaptivity and Learning, 111–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05594-6_11.

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Frantiska, Joseph J. "A Structured Approach." In Creating Reusable Learning Objects, 31–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32889-8_10.

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Zhang, Xian-Da. "Machine Learning." In A Matrix Algebra Approach to Artificial Intelligence, 223–440. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2770-8_6.

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Turner, Amanda. "Methodological Approach." In Professional Identity Development through Incidental Learning, 59–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86092-9_4.

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Albu, Mihai, Scott Helm, Thanh-Dat Cao, George Miltenburg, and Angelo Cosco. "LeARning—An AR Approach." In Mobile Technologies and Applications for the Internet of Things, 143–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11434-3_18.

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Towill, D. R., and J. E. Cherrington. "Learning Curve Models." In A Systems Approach to AMT Deployment, 57–75. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3406-0_4.

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Conference papers on the topic "Learning approach"

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Shi-Zhong Liao, Xiao-Jun Wang, and Jin-Liang Lu. "An incremental Bayesian approch to sketch recognition [approach read approach]." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527740.

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Thaysen, Peter. "An Approach to Building Learning Objects." In Sixth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/head20.2020.11070.

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Designing for online education can be a complex endeavor and the way to approach this must be carefully considered. This article examines the case of Danish educational institution SmartLearning to map out and analyze the approach to online courses. The study find that SmartLearning approaches the online courses by setting up guidelines for educators on how to build learnings objects. The approach is to use three different elements, one focused on the content and learning goals for a course, one focused on the layout of the leaning management system, and one focusing on which didactic principles to apply. These three elements must work together in the learning object in order to assure learning and motivation of learners. The study also find some structure regarding this process and based on the analysis it is recommended to bind the development of learning objects and courses together through instructional design tools. This will aid the further development of combining the three elements into quality learning objects.
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Abashidze, Natia. "Challenges and Outcomes of Distance Learning." In 9th International Conference on Modern Approach in Humanities. Acavent, 2021. http://dx.doi.org/10.33422/9th.mahconf.2021.10.100.

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Bucolo, Maide, Arturo Buscarino, Luigi Fortuna, and Gabriele Puglisi. "Learning-on-learning approach for modeling." In IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022. http://dx.doi.org/10.1109/iecon49645.2022.9968904.

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Sen, Gabriel, Albert Adeboye, and Oluwole Alagbe. "STUDENT LEARNING APPROACHES OF ARCHITECTURE STUDENTS: DEEP OR SURFACE LEARNING APPROACH." In 14th International Technology, Education and Development Conference. IATED, 2020. http://dx.doi.org/10.21125/inted.2020.2588.

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Favario, Leonardo. "A Comprehensive MOOC Creation Approach." In 2018 Learning With MOOCS (LWMOOCS). IEEE, 2018. http://dx.doi.org/10.1109/lwmoocs.2018.8534603.

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Neo, Han-Foon, Chuan-Chin Teo, and Jackson Lew Han Boon. "Mobile Edutainment Learning Approach." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3284497.3284500.

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Karpenko, A. P. "Metacompetence approach to learning." In Психология творчества и одаренности. Москва: Ассоциация технических университетов, 2021. http://dx.doi.org/10.53677/9785919160458_49_59.

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Mandal, Aditya Chandra, Abhijeet Phatak, Jayaram Jothi balaji, and Vasudevan Lakshminarayanan. "A deep-learning approach to pupillometry." In Applications of Machine Learning 2021, edited by Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2021. http://dx.doi.org/10.1117/12.2594315.

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Rae, John, Carole Roberts, and Gary Taylor. "Collaborative Learning: A Connected Community Approach." In InSITE 2006: Informing Science + IT Education Conference. Informing Science Institute, 2006. http://dx.doi.org/10.28945/2946.

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Collaborative Learning in group settings currently occurs across a substantial portion of the UK Higher Education curriculum. This style of learning has many roots including: Enterprise in Higher Education, Action Learning and Action Research, Problem Based Learning, and Practice Based Learning. As such our focus on Collaborative Learning development can be viewed as an evolutionary. This collaborative and active group learning provides the foundation for what can be collectively called connectivist ‘Learning Communities’. In this setting a primary feature of a ‘Learning Community’ is one that carries a responsibility to promote one another’s learning. This paper will outline a developmental collaborative learning approach and describe a supporting software environment, known as the Salford Personal Development Environment (SPDE), that has been developed and implemented to assist in delivering collaborative learning for post graduate and other provision. This is done against a background of much research evidence that group based activity can enhance learning. These findings cover many approaches to group based learning and over a significant period of time. This paper reports on work-in-progress and the features of the environment that are designed to help promote individual and group or community learning that have been influenced by the broad base of research findings in this area.
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Reports on the topic "Learning approach"

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Ikudo, Akina, Julia Lane, Joseph Staudt, and Bruce Weinberg. Occupational Classifications: A Machine Learning Approach. Cambridge, MA: National Bureau of Economic Research, August 2018. http://dx.doi.org/10.3386/w24951.

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Wisher, Robert A., and Christina K. Curnow. An Approach to Evaluating Distance Learning Events. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada360984.

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Albanesi, Stefania, and Domonkos Vamossy. Predicting Consumer Default: A Deep Learning Approach. Cambridge, MA: National Bureau of Economic Research, August 2019. http://dx.doi.org/10.3386/w26165.

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Howard, Jo, Evert-jan Quak, and Jim Woodhill. A Practical Approach for Supporting Learning in Development Organisations. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.120.

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The Knowledge, Evidence and Learning for Development (K4D) Programme, which started in 2016, comes to an end in September 2022. K4D is a programme funded by and for the UK Government’s Foreign, Commonwealth and Development Office (initially with the Department for International Development, DFID, which was merged with the FCO in 2020). To reflect on the processes and approaches of learning that K4D enabled over the years, a special Working Paper series will be published. One important pillar of the programme was to facilitate learning processes through learning journeys. A total of 33 learning journeys took place during K4D. This summary looks back at the K4D concept, the learning journeys, the learning processes it supported, and the outcomes enabled. The paper finds that there is evidence that K4D learning journeys have helped enable sound, informed decision-making through collective understanding of issues and options, and through internal consensus on directions. Effective learning spaces were created and the methods used (including online tools for participation) were able to capture and share internal learning, foster internal connections, present external evidence and bring in other perspectives. However, success in enabling external alliances for decisions and change was more constrained, since most learning journeys engaged only in limited ways with external organisations. Further challenges were encountered when staff were redeployed to respond to political (Brexit) or international development (COVID-19, Ukraine) priorities.
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Howard, Jo, Evert-jan Quak, and Jim Woodhill. Lessons Learned From K4D Learning Journeys: A Practical Approach for Supporting Learning in Development Organisations. Institute of Development Studies, November 2022. http://dx.doi.org/10.19088/k4d.2022.166.

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The Knowledge, Evidence and Learning for Development (K4D) Programme, which started in 2016, came to an end in September 2022. This K4D working paper reflects on the learning processes and approaches facilitated by this programme, through ‘learning journeys’ conducted in collaboration with staff of the Foreign Commonwealth and Development Office (FCDO) in the United Kingdom. A total of 45 learning journeys took place, of which 33 have been assessed for this working paper. Through this assessment, we test our proposed Theory of Change for organisational learning (OL) in the context of international development agencies.
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Corcoran, Tom, Frederic Mosher, and Aaron Rogat. Learning Progressions in Science: An Evidence-based Approach to Reform. Consortium for Policy Research in Education, May 2009. http://dx.doi.org/10.12698/cpre.2009.rr63.

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7

Carpenter, Tamitha, Daniel Fu, Phillip Michalak, Laurie Spencer, and Luciano Iorizzo. A Constructivist Approach to Distance Learning for Counterterrorist Intelligence Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439022.

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8

Boyd, Zachary M., and Joanne Roth Wendelberger. An Integrated Approach to Parameter Learning in Infinite-Dimensional Space. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1392846.

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9

Shute, Valerie J., and Lisa A. Gawlick-Grendell. An Experimental Approach to Teaching and Learning Probability: Stat Lady. Fort Belvoir, VA: Defense Technical Information Center, April 1996. http://dx.doi.org/10.21236/ada316969.

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10

Musacchio, John, Greg Frazier, and Pat Kreidl. Out-Learning Attackers: A Game Theoretic Approach to Cyber Defense. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada583626.

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