Relevant bibliographies by topics / Learning programs

Contents

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

Academic literature on the topic 'Learning programs'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Learning programs"

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St. Clair, D. C. "Learning programs." IEEE Potentials 11, no. 3 (1992): 19–22. http://dx.doi.org/10.1109/45.207106.

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Abdullaeva, Rozanna, Gulbahor Mirrahimova, Guzal Aminova, Ilona Israilova, and Oybek Eshbaev. "Learning Foreign Vocabulary Using Computer Programs." International Journal of Psychosocial Rehabilitation 24, Special Issue 1 (2020): 567–72. http://dx.doi.org/10.37200/ijpr/v24sp1/pr201192.

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Sherman, Thomas M. "Learning Improvement Programs." Journal of Higher Education 56, no. 1 (1985): 85–100. http://dx.doi.org/10.1080/00221546.1985.11778706.

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Cropper, Andrew, and Rolf Morel. "Learning programs by learning from failures." Machine Learning 110, no. 4 (2021): 801–56. http://dx.doi.org/10.1007/s10994-020-05934-z.

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AbstractWe describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.
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Herlo, Dorin. "SELF-DIRECTED LEARNING ON TEACHER TRAINING STUDIES PROGRAMS." Journal Plus Education 18, no. 2/2017 (2017): 7–17. http://dx.doi.org/10.24250/jpe/2/2017/dh.

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Cushman, Ellen. "Sustainable Service Learning Programs." College Composition and Communication 54, no. 1 (2002): 40. http://dx.doi.org/10.2307/1512101.

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WOOD, CARL MARTIN ALL, and TORBJÖN WIKSTRÖM. "Learning complex computer programs." Behaviour & Information Technology 5, no. 3 (1986): 217–25. http://dx.doi.org/10.1080/01449298608914515.

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Hart, Stephen, and Roderic Grupen. "Learning Generalizable Control Programs." IEEE Transactions on Autonomous Mental Development 3, no. 3 (2011): 216–31. http://dx.doi.org/10.1109/tamd.2010.2103311.

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CASE, JOHN, SANJAY JAIN, and ARUN SHARMA. "ON LEARNING LIMITING PROGRAMS." International Journal of Foundations of Computer Science 03, no. 01 (1992): 93–115. http://dx.doi.org/10.1142/s0129054192000097.

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Machine learning of limit programs (i.e., programs allowed finitely many mind changes about their legitimate outputs) for computable functions is studied. Learning of iterated limit programs is also studied. To partially motivate these studies, it is shown that, in some cases, interesting global properties of computable functions can be proved from suitable (n+1)-iterated limit programs for them which can not be proved from any n-iterated limit programs for them. It is shown that learning power is increased when (n+1)-iterated limit programs rather than n-iterated limit programs are to be learned. Many trade-off results are obtained regarding learning power, number (possibly zero) of limits taken, program size constraints and information, and number of errors tolerated in final programs learned.
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Richards, Colin. "Programs to Encourage Learning." British Journal of Special Education 12, no. 2 (2007): 77. http://dx.doi.org/10.1111/j.1467-8578.1985.tb00611.x.

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Dissertations / Theses on the topic "Learning programs"

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Cropper, Andrew. "Efficiently learning efficient programs." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/58488.

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Discovering efficient algorithms is central to computer science. In this thesis, we aim to discover efficient programs (algorithms) using machine learning. Specifically, we claim we can efficiently learn programs (Claim 1), and learn efficient programs (Claim 2). In contrast to universal induction methods, which learn programs using only examples, we introduce program induction techniques which additionally use background knowledge to improve learning efficiency. We focus on inductive logic programming (ILP), a form of program induction which uses logic programming to represent examples, background knowledge, and learned programs. In the first part of this thesis, we support Claim 1 by using appropriate background knowledge to efficiently learn programs. Specifically, we use logical minimisation techniques to reduce the inductive bias of an ILP learner. In addition, we use higher-order background knowledge to extend ILP from learning first-order programs to learning higher-order programs, including the support for higher-order predicate invention. Both contributions reduce learning times and improve predictive accuracies. In the second part of this thesis, we support Claim 2 by introducing techniques to learn minimal cost logic programs. Specifically, we introduce Metaopt, an ILP system which, given sufficient training examples, is guaranteed to find minimal cost programs. We show that Metaopt can learn minimal cost robot strategies, such as quicksort, and minimal time complexity logic programs, including non-deterministic programs. Overall, the techniques introduced in this thesis open new avenues of research in computer science and raise the potential for algorithm designers to discover novel efficient algorithms, for software engineers to automate the building of efficient software, and for AI researchers to machine learn efficient robot strategies.
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Alsanie, Waleed. "Learning failure-free PRISM programs." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/3388/.

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First-order logic can be used to represent relations amongst objects. Probabilistic graphical models encode uncertainty over propositional data. Following the demand of combining the advantages of both representations, probabilistic logic programs provide the ability to encode uncertainty over relational data. PRISM is a probabilistic logic programming formalism based on the distribution semantics. PRISM allows learning the parameters when the programs are known. This thesis proposes algorithms to learn failure-free PRISM programs. It combines ideas from both areas of inductive logic programming and learning Bayesian networks. The learned PRISM programs generalise dynamic Bayesian networks by defining a halting distribution over the sampling process. Each dynamic Bayesian network models either an infinite sequential generative process or a sequential generative process of a fixed length. In both cases, only a fixed length of sequences can be sampled. On the other hand, the PRISM programs considered in this thesis represent self-terminating functions from which sequences of different lengths can be obtained. The effectiveness of the proposed algorithms on learning five programs is shown.
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Bone, Nicholas. "Models of programs and machine learning." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244565.

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Law, Mark. "Inductive learning of answer set programs." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/64824.

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The goal of Inductive Logic Programming (ILP) is to find a hypothesis that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning definite logic programs. This thesis constitutes the first comprehensive work on learning answer set programs, introducing new learning frameworks, theoretical results on the complexity and generality of these frameworks, algorithms for learning ASP programs, and an extensive evaluation of these algorithms. Although there is previous work on learning ASP programs, existing learning frameworks are either brave -- where examples should be explained by at least one answer set -- or cautious where examples should be explained by all answer sets. There are cases where brave induction is too weak and cautious induction is too strong. Our proposed frameworks combine brave and cautious learning and can learn ASP programs containing choice rules and constraints. Many applications of ASP use weak constraints to express a preference ordering over the answer sets of a program. Learning weak constraints corresponds to preference learning, which we achieve by introducing ordering examples. We then explore the generality of our frameworks, investigating what it means for a framework to be general enough to distinguish one hypothesis from another. We show that our frameworks are more general than both brave and cautious induction. We also present a new family of algorithms, called ILASP (Inductive Learning of Answer Set Programs), which we prove to be sound and complete. This work concerns learning from both non-noisy and noisy examples. In the latter case, ILASP returns a hypothesis that maximises the coverage of examples while minimising the length of the hypothesis. In our evaluation, we show that ILASP scales to tasks with large numbers of examples finding accurate hypotheses even in the presence of high proportions of noisy examples.
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Ellis, Kevin Ph D. (Kevin M. )Massachusetts Institute of Technology. "Algorithms for learning to induce programs." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130184.

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Thesis: Ph. D. in Cognitive Science, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, September, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 213-224).<br>The future of machine learning should have a knowledge representation that supports, at a minimum, several features: Expressivity, interpretability, the potential for reuse by both humans and machines, while also enabling sample-efficient generalization. Here we argue that programs-i.e., source code-are a knowledge representation which can contribute to the project of capturing these elements of intelligence. This research direction however requires new program synthesis algorithms which can induce programs solving a range of AI tasks. This program induction challenge confronts two primary obstacles: the space of all programs is infinite, so we need a strong inductive bias or prior to steer us toward the correct programs; and even if we have that prior, effectively searching through the vast combinatorial space of all programs is generally intractable. We introduce algorithms that learn to induce programs, with the goal of addressing these two primary obstacles. Focusing on case studies in vision, computational linguistics, and learning-to-learn, we develop an algorithmic toolkit for learning inductive biases over programs as well as learning to search for programs, drawing on probabilistic, neural, and symbolic methods. Together this toolkit suggests ways in which program induction can contribute to AI, and how we can use learning to improve program synthesis technologies.<br>by Kevin Ellis.<br>Ph. D. in Cognitive Science<br>Ph.D.inCognitiveScience Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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Lubbe, H. G., and B. J. Kotze. "Machine learning through self generating programs." Interim : Interdisciplinary Journal, Vol 6, Issue 2: Central University of Technology Free State Bloemfontein, 2007. http://hdl.handle.net/11462/407.

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Published Article<br>People have tried different ways to make machines intelligent. One option is to use a simulated neural net as a platform for Genetic Algorithms. Neural nets are a combination of neurons in a certain pattern. Neurons in a neural net system are a simulation of neurons in an organism's brain. Genetic Algorithms represent an emulation of evolution in nature. The question arose as to why write a program to simulate neurons if a program can execute the functions a combination of neurons would generate. For this reason a virtual robot indicated in Figure 1 was made "intelligent" by developing a process where the robot creates a program for itself. Although Genetic Algorithms might have been used in the past to generate a program, a new method called Single-Chromosome-Evolution-Algorithms (SCEA) was introduced and compared to Genetic Algorithms operation. Instructions in the program were changed by using either Genetic Algorithms or alternatively with SCEA where only one simulation was needed per generation to be tested by the fitness of the system.
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Balachandra, Lakshmi 1974. "Experimental learning programs : an analysis and review." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28687.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2004.<br>"June 2004 -- revised October 2004."<br>Includes bibliographical references (leaves 47-48).<br>Experiential Learning programs have increasingly been included in corporate training programs. Today there is a wide range of experiential learning programs using a variety of methodologies. However, there is a surprising dearth of research on the effectiveness of such programs for learning in business. This thesis reviews and analyzes one form of experiential learning--a program that utilizes outdoor activities for leadership and teamwork training--to understand the value proposition of such education for corporate clients. From this, a framework for implementing a successful experiential learning program was suggested and then analyzed by the design and delivery of a new, original experiential training program utilizing improvisational theater techniques. Finally, a method to evaluate experiential learning programs both before and after purchase is suggested.<br>by Lakshmi Balachandra.<br>M.B.A.
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Faria, Francisco Henrique Otte Vieira de. "Learning acyclic probabilistic logic programs from data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-27022018-090821/.

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To learn a probabilistic logic program is to find a set of probabilistic rules that best fits some data, in order to explain how attributes relate to one another and to predict the occurrence of new instantiations of these attributes. In this work, we focus on acyclic programs, because in this case the meaning of the program is quite transparent and easy to grasp. We propose that the learning process for a probabilistic acyclic logic program should be guided by a scoring function imported from the literature on Bayesian network learning. We suggest novel techniques that lead to orders of magnitude improvements in the current state-of-art represented by the ProbLog package. In addition, we present novel techniques for learning the structure of acyclic probabilistic logic programs.<br>O aprendizado de um programa lógico probabilístico consiste em encontrar um conjunto de regras lógico-probabilísticas que melhor se adequem aos dados, a fim de explicar de que forma estão relacionados os atributos observados e predizer a ocorrência de novas instanciações destes atributos. Neste trabalho focamos em programas acíclicos, cujo significado é bastante claro e fácil de interpretar. Propõe-se que o processo de aprendizado de programas lógicos probabilísticos acíclicos deve ser guiado por funções de avaliação importadas da literatura de aprendizado de redes Bayesianas. Neste trabalho s~ao sugeridas novas técnicas para aprendizado de parâmetros que contribuem para uma melhora significativa na eficiência computacional do estado da arte representado pelo pacote ProbLog. Além disto, apresentamos novas técnicas para aprendizado da estrutura de programas lógicos probabilísticos acíclicos.
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Hagerf, Alexander. "Complexity in Statistical Relational Learning : A Study on Learning Bayesian Logic Programs." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170160.

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Most work that is done within machine learning today uses statistical methods which assume that the data is identically and independently distributed. However, the problem domains that we face in the real world are often much more complicated and present both complex relational/logical parts as well as parts with uncertainty. Statistical relational learning (SRL) is a sub-field of machine learning and A.I. that tries to solve these limitations by combining both relational and statistical learning and has become a big research sector in recent years. This thesis will present SRL further and specifically introduce, test and review one of the implementations, namely Bayesian logic programs.<br>Idag används inom maskininlärning nästan alltid statistiska metoder som antar att datat för lärande är identiskt och oberoende distribuerat. Men de problemområden som vi står inför i den verkliga världen är ofta mycket mer komplicerade och har både komplexa relationella/logiska delar samt osäkerhet. Statistiskt relationslärande (SRL) är en del av maskininlärning och A.I. som försöker lösa dessa begränsningar genom att kombinera både relationer och statistiskt lärande och har på senare år blivit ett stort forskningsområde. Denna avhandling presenterar SRL mer i detalj och utreder och testar en specifik implementation, Bayesianska logikprogram.
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Chen, Mei 1962. "The characterization of learning environments and program structures of instructional programs produced using Logo /." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56930.

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A methodology was developed in this study for identifying the cognitive, pedagogical, and computational characteristics of computer-based learning environments. The characterization of the cognitive and pedagogical features was achieved by decomposing the learning environments into episodes which were composed of sequences of "views". Each "view" was described in terms of the different types of knowledge presented, the pedagogical strategies used to present the knowledge, and the forms and functions of user-computer interactions elicited. The computational characteristics were described in terms of modularity and other programming properties. The methodology was applied to characterizing the instructional programs produced by student teachers using Logo.<br>The results showed that this methodology can successfully identify the cognitive, pedagogical and computational characteristics of the learning environments. It can also clarify what can be learned in a microworld, especially the "powerful ideas" in Logo environments. In addition, the usability and constraints of learning environments in meeting the learners' cognitive needs during the learning process can be assessed.
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Books on the topic "Learning programs"

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Johansson, Jan-Olov. Alternative learning experience programs. State Superintendent of Public Instruction, 1999.

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Learning VBScript. O'Reilly, 1997.

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Washington (State). Legislature. Joint Legislative Audit and Review Committee. Alternative learning experience programs study. State of Washington, Joint Legislative Audit and Review Committee (JLARC), 2005.

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C4.5: Programs for machine learning. Morgan Kaufmann Publishers, 1993.

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McAteer, Erica. The designof multimedia learning programs. UCoSDA, 1995.

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Middle States Association of Colleges and Schools. Commission on Higher Education. Guidelines for distance learning programs. Commission on Higher Education, Middle States Association of Colleges and Schools, 1997.

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Organisation, Irish National Teachers'. Learning difficulties: Literacy. I.N.T.O., 1997.

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Brunsdon, Terri E. Learning Office accounting professional 2008. Prentice Hall, 2009.

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Bucki, Lisa A. Learning Microsoft Office 2010. Pearson Education, 2011.

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Seager, Andrew J. Learning from public library literacy programs. U.S. Dept. of Education, Office of Educational Research and Improvement, Library Programs, 1993.

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Book chapters on the topic "Learning programs"

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van Merriënboer, Jeroen J. G., and Paul A. Kirschner. "Programs of Assessment." In Ten Steps to Complex Learning. Routledge, 2017. http://dx.doi.org/10.4324/9781315113210-15.

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Tzuriel, David. "Cognitive Education Programs." In Mediated Learning and Cognitive Modifiability. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75692-5_15.

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Madritsch, C., T. Klinger, A. Pester, and W. Schwab. "Work in Progress: Using Pocket Labs in Master Degree Programs." In Interactive Collaborative Learning. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50340-0_5.

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Smith, David H., and Jeffrey E. Davis. "Formative Assessment for Student Progress and Program Improvement in Sign Language as L2 Programs." In Teaching and Learning Signed Languages. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137312495_12.

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Baxter, Nancy, Ed Dubinsky, and Gary Levin. "Numbers, Programs, and ISETL." In Learning Discrete Mathematics with ISETL. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3592-7_1.

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Dimopoulos, Yannis, and Antonis Kakas. "Learning non-monotonic logic programs: Learning exceptions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59286-5_53.

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Baguley, Margaret, Patrick Alan Danaher, Andy Davies, et al. "Individual Learning Needs and Designing Learning Programs." In Educational Learning and Development. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137392848_5.

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Härtel, Hermann. "Interactive learning programs for unix-machines." In Computer Assisted Learning. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/bfb0020891.

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Džeroski, Sašo, Stephen Muggleton, and Stuart Russell. "Learnability of constrained logic programs." In Machine Learning: ECML-93. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56602-3_148.

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Gunetti, D., and U. Trinchero. "Intensional learning of logic programs." In Machine Learning: ECML-94. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_73.

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Conference papers on the topic "Learning programs"

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Pinto, Jervis, Alan Fern, Tim Bauer, and Martin Erwig. "Robust Learning for Adaptive Programs by Leveraging Program Structure." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.150.

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Inoue, Tomoo, Ken-ichi Okada, and Yutaka Matsushita. "Learning from TV programs." In the 8th annual ACM symposium. ACM Press, 1995. http://dx.doi.org/10.1145/215585.215967.

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Case, John, Sanjay Jain, and Arun Sharma. "On learning limiting programs." In the fifth annual workshop. ACM Press, 1992. http://dx.doi.org/10.1145/130385.130407.

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Ellis, Kevin, and Sumit Gulwani. "Learning to Learn Programs from Examples: Going Beyond Program Structure." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/227.

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Programming-by-example technologies let end users construct and run new programs by providing examples of the intended program behavior. But, the few provided examples seldom uniquely determine the intended program. Previous approaches to picking a program used a bias toward shorter or more naturally structured programs. Our work here gives a machine learning approach for learning to learn programs that departs from previous work by relying upon features that are independent of the program structure, instead relying upon a learned bias over program behaviors, and more generally over program execution traces. Our approach leverages abundant unlabeled data for semisupervised learning, and incorporates simple kinds of world knowledge for common-sense reasoning during program induction. These techniques are evaluated in two programming-by-example domains, improving the accuracy of program learners.
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Cropper, Andrew. "Playgol: Learning Programs Through Play." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/841.

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Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of user-supplied build tasks and initial background knowledge (BK). Before solving the build tasks, the learner enters an unsupervised playing stage where it creates its own play tasks to solve, tries to solve them, and saves any solutions (programs) to the BK. After the playing stage is finished, the learner enters the supervised building stage where it tries to solve the build tasks and can reuse solutions learnt whilst playing. The idea is that playing allows the learner to discover reusable general programs on its own which can then help solve the build tasks. We claim that playing can improve learning performance. We show that playing can reduce the textual complexity of target concepts which in turn reduces the sample complexity of a learner. We implement our idea in Playgol, a new inductive logic programming system. We experimentally test our claim on two domains: robot planning and real-world string transformations. Our experimental results suggest that playing can substantially improve learning performance.
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Schede, Elias Arnold, Samuel Kolb, and Stefano Teso. "Learning Linear Programs from Data." In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00143.

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Norvig, Peter. "Applying machine learning to programs." In OpenSym '15: The 11th International Symposium on Open Collaboration. ACM, 2015. http://dx.doi.org/10.1145/2789853.2789869.

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Raychev, Veselin, Pavol Bielik, Martin Vechev, and Andreas Krause. "Learning programs from noisy data." In POPL '16: The 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. ACM, 2016. http://dx.doi.org/10.1145/2837614.2837671.

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Acosta-Flamma, Christian. "THE PROMISE OF ACTION LEARNING PROGRAMS." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.2404.

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Schoop, Eldon, Forrest Huang, and Bjoern Hartmann. "UMLAUT: Debugging Deep Learning Programs using Program Structure and Model Behavior." In CHI '21: CHI Conference on Human Factors in Computing Systems. ACM, 2021. http://dx.doi.org/10.1145/3411764.3445538.

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Reports on the topic "Learning programs"

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Cellini, Stephanie Riegg, and Hernando Grueso. Student Learning in Online College Programs. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28552.

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Weichert, Robert S. Leadership Theory Taught in Air Force Distant Learning Programs. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada590284.

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Heidbrink, Scott, Kathryn Rodhouse, and Daniel Dunlavy. Multimodal Deep Learning for Flaw Detection in Software Programs. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1660805.

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Schmid, Ute, and Fritz Wysotzki. Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada382307.

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Withers, Denissia. Engaging Community Food Systems through Learning Garden Programs: Oregon Food Bank's Seed to Supper Program. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.609.

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Boyd, Kaylee, Stacy Lee, and Jung Ha-Brookshire. Student and Instructor Attitudes toward Responsibility of Learning and Teaching Essential Programs. Iowa State University, Digital Repository, 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-58.

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Lopezrevoredo, Analucia. Learning From Culturally Specific Programs and Their Impact on Latino Parent Engagement. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6873.

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Huang, Luna Yue, Solomon Hsiang, and Marco Gonzalez-Navarro. Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29105.

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Wei, Max, Sarah J. Smith, and Michael D. Sohn. Non-Constant Learning Rates in Retrospective Experience Curve Analyses and their Correlation to Deployment Programs. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1237059.

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Banerji, Rukmini, James Berry, and Marc Shotland. The impact of mother literacy and participation programs on child learning: Evidence from a randomized evaluation in India. International Initiative for Impact Evaluation, 2015. http://dx.doi.org/10.23846/ow2153.

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