Relevant bibliographies by topics / Learning to program

Academic literature on the topic 'Learning to program'

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Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Learning to program"

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Singh, Gurjeet, and Raksha Singh. "DOMAINS OF LEARNING: ART OF LEARNING IN MEDICAL EDUCATION PROGRAM." Era's Journal of Medical Research 7, no. 1 (June 2020): 79–85. http://dx.doi.org/10.24041/ejmr2020.14.

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assahary, salman. "Integrative Learning Religious and Learning Enviroment at Adiwiyata Program School." mamangan 8, no. 2 (December 2019): 71–76. http://dx.doi.org/10.22202/mamangan.4145.

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Nord, Julia Ann, Padmanabhan Seshaiyer, Mary Nelson, Claudette Davis, Mary Ewell, Rebecca Jones, Kelly Knight, Kathy Pettigrew, and James Reid Schwebach. "Learning assistant program." Innovations in Teaching & Learning Conference Proceedings 8 (July 15, 2016): 2. http://dx.doi.org/10.13021/g8s89x.

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Following the model of the University of Colorado at Boulder Learning Assistant (LA) program, the College of Science (COS) at Mason began its own LA program in 2012. The program is a key segment of the STEM Accelerator program, and is a keystone for retaining students in COS and enabling them to graduate in a timely manner. COS STEM Accelerator has now supported over 300 undergraduate LA’s since it's inception through both internal and external funding. The LA program is very successful in increasing grades, engaging students in large lecture classes, enabling faculty to better utilize the ALT classroom, increasing retention in sciences and also increasing confidence and GPA’s for the undergraduate LA’s. The cornerstones of the LA program are student mentoring by the faculty sponsor, a required seminar in pedagogical techniques, and face-to-face time with students in the classroom. This interactive presentation will include information about the program in COS. Faculty and students will demonstrate examples of how LA’s are integrated into different learning environments and across disciplines. Examples will include LA’s in the ALT classroom, in large and small lectures, in lab, in orals, and on-line. Disciplines include Biology, Chemistry, Forensic Science, Geography, Geology, Math and Physics. Attendees will understand the program and it’s key concepts, discuss how an LA could work in their classroom and discipline, and understand how COS is working to enable your students to conquer their MATH and Mason Core Science classes.
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Anderson, Duncan P. "Program Learning Objectives." Canadian Journal of Ophthalmology 36, no. 2 (March 2001): 61. http://dx.doi.org/10.1016/s0008-4182(01)80103-4.

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Rafuse, Paul. "Program Learning Objectives." Canadian Journal of Ophthalmology 37, no. 2 (March 2002): 67. http://dx.doi.org/10.1016/s0008-4182(02)80075-8.

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Albury, David. "Learning to program." Data Processing 27, no. 7 (September 1985): 48. http://dx.doi.org/10.1016/0011-684x(85)90109-1.

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Patadia, Mayur. "Necessity of E-Learning in B.ed Program." International Journal of Scientific Research 3, no. 3 (June 1, 2012): 1–2. http://dx.doi.org/10.15373/22778179/march2014/148.

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Rego, Michelle. "The Global Learning Distinction: an Experiential Learning Research Project." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 3 (2018): 50–54. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.33.3005.

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The purpose of this paper is to demonstrate the importance of integrating global learning and culturally-responsive teaching as part of a University curriculum to prepare graduates to enter a global workforce. According to the Association of American Colleges and Universities (AAC&U), global learning programs are an important aspect of culturally responsive teaching which help students to “understand and engage the diversities and commonalities among the world’s peoples, cultures, nations, and regions” (AAC&U, 2018). The objective of this experiential learning project, implemented at the College of Business at Johnson & Wales University in Providence, RI, USA, was to research ways to engage students in active global learning both inside and outside of the classroom. Specifically, the problem being explored included needs to assess student attitudes toward global learning (1), understand perceived benefits of a Global Learning Distinction program (2), and identify an opportunity for students to get involved in the promotion of this program prior to the full launch in 2018 (3). Focus group research results are discussed and recommendations for future research in Global Learning.
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Burgess, Marion, and Matthew Stead. "Flexible learning program for acoustic consultants." Journal of the Acoustical Society of America 151, no. 3 (March 2022): 1672–75. http://dx.doi.org/10.1121/10.0009782.

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There is a demand around the world for professional staff to join acoustic consulting companies, and in Australasia this demand is increasing. The extent of acoustics covered in undergraduate programs varies greatly, and there is usually the need to provide additional learning opportunities for new employees. While larger companies may provide training in-house, there are times when they are not in a position to release the senior members to provide such training. By contrast, smaller companies generally do not have this option. Short courses or similar programs can provide the necessary upskilling of new staff, but such courses may not be offered at times or locations that suit the company recruitment program. To overcome the demonstrated need for supplementary education programs in acoustics, the Australasian Association for Acoustical Consultants worked with the University of New South Wales to develop a fully flexible distance learning program, loosely based on the United Kingdom Institute of Acoustics Diploma program. The development, implementation, and experiences from over a decade of offering this program are discussed in this paper.
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Muhamad, M., N. A. Maskor, and A. Ismail. "Learning Experience Contribution to Effective Cancer Education?" Journal of Global Oncology 4, Supplement 2 (October 1, 2018): 107s. http://dx.doi.org/10.1200/jgo.18.59800.

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Background: Cancer patient needs information to make informed decision. A registered cancer society in Malaysia, KanWork supports those affected by cancer by providing such information through regular structured education program. The programs were designed based on the learners needs addressing issues related to medical, clinical, psychological, spiritual, motivational, and healthy lifestyle. The programs were carried out in collaboration with cancer hospitals at their venues. Each program lasted 3 hours. The program was guided by adult learning principles. For accountability and further improvement, there is a need to evaluate the effectiveness of the program. This paper reports the programs and their effectiveness from the participants' perspective. Aim: To determine the relationship between program management (learning experience provided to participants, and facilities), and the cancer education program effectiveness. To determine predictors that contribute toward the program effectiveness. Methods: A customized instrument to measure the education program effectiveness related constructs was developed guided by literature. The questionnaire was administered to 868 participants of 8 cancer education programs carried out between 2015 and 2017. 481 (55%) returned the questionnaire. Descriptive statistics, Pearson product-moment correlations and multiple linear regression were used to analyze the data. Results: Majority (91%) who attended the education program were women and cancer patient/survivor (50%). There is positive relationship between learning experience (r=0.895, P = 0.00), program facilities (r=0.435, P = 0.00), and program effectiveness. Learning experience (β = .916; P < .000) showed a predictor and major contribution to cancer education program effectiveness. An appropriate learning experience to learner is critical to ensure the education program effectiveness. Adult learners joined the program to solve their problematic situation in their cancer journey. An appropriate learning experience is one that contributes to meeting their needs, they can follow (e.g., conducted in local language) (Bahasa Malaysia) and terms they can comprehend, and with two way communication channel that gives them chance to be engaged. The experienced facilitators, expert in their respective fields were able to provide meaningful learning experience to participants. Conclusion: Appropriate learning experiences and program facilities contributes toward effective cancer education program in a Malaysian context. The findings strengthen adult learning constructs that guide such program. Further research will give insight to education program in different contexts.
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Dissertations / Theses on the topic "Learning to program"

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Porter, Ronald, and ron porter@infoeng flinders edu au. "Design Patterns in Learning to Program." Flinders University. Informatics and Engineering, 2006. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20061127.153554.

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This thesis argues the case for the use of a pattern language based on the basic features of the programming language used in instruction for the teaching of programming. We believe that the difficulties that novices are known to have encountered with the task of learning to program ever since the inception of computers derive from a basic misfit between the language used to communicate with a computer, the programming language, and the way that humans think. The thrust of the pattern language idea is that patterns are the essential element in understanding how the mind words in that they are the source of that relationship that we call `meaning'. What an entity or event `means' to us derives from the effect that it has on us as living biological beings, a relationship that exists in the `real world', not from any linguistic relationship at the symbolic level. Meaning, as a real world relationship, derives from the patterns of interactions that constitute being. The meaning that an entity has for an individual is more than can be expressed in a formal definition, definitions are matters of agreement, convention, not the pattern of experience that the individual has acquired through living. What is missing for a novice in any skill acquisition process is meaning, the pattern of experience. All that we can give them using a formal linguistic system like a programming language is definitions, not meaning. Pattern language is the way that we think because it exists at that fundamental level of experience as living beings. The patterns of experience become the patterns of thought through recurrence, not through definition. But this takes time, so in presenting new material to a person trying to learn, we have to present it in the form of a pattern language, the 'cognitive map' that drives the problem solving process. Creativity is always a function of combining ideas, what is really being created is new meaning, not a program, or a house, or a poem, or a sculpture - these things are mere implementations of meaning. Ultimately meaning can derive only from experience, the pattern of life around us, so creativity is the language of experience, pattern language. The mind is the product of experience, creativity its modus operandi.
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Agrawal, Punit. "Program navigation analysis using machine learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32599.

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Developers invest a large portion of their development time exploring program source code to find task-related code elements and to understand the context of their task. The task context is usually not recorded at the end of the task and is forgotten over time. Similarly, it is not possible to share the task context with other developers working on related tasks. Proposed solutions to automatically record the summary of the code investigation suffer from methodological limitations related to the techniques and the data sources used to generate the summary as well as the granularity at which it is generated. To overcome these limitations, we investigate the use of machine learning techniques, in particular decision tree learning, to predict automatically the task context from session navigation transcripts obtained from developers performing tasks on the source code. We conducted a user study to collect navigation transcripts from developers engaged in source code exploration tasks. We used the data from the user study to train and test decision tree classifiers. We compared the decision tree algorithm with two existing approaches, and found that it compares positively in most cases. Additionally, we developed an Eclipse plug-in that generates automatically a developer session summary using the decision tree classifier learned from the data collected during the user study. We provide qualitative analysis of the effectiveness of this plug-in.
Les d\'eveloppeurs de logiciels investissent une grande partie de leur temps \`a explorer le code source pour trouver des \'el\'ements du code reli\'es \`a leurs t\^aches, et aussi pour mieux comprendre le contexte de leur t\^ache. Le contexte de leur t\^ache n'est g\'en\'eralement pas enregistr\'ee \`a la fin de leur s\'eance d'exploration de code et est oubli\'e au fil du temps. De m\^eme, il n'est pas possible de partager le contexte de leur t\^ache avec d'autres d\'eveloppeurs travaillant sur des t\^aches reli\'ees. Les solutions propos\'ees pour enregistrer automatiquement le r\'esum\'e de leur exploration du code souffrent de limitations m\'ethodologiques li\'ees aux techniques et aux sources de donn\'ees utilis\'ees pour g\'en\'erer le r\'esum\'e, ainsi qu'\`a la granularit\'e \`a laquelle il est g\'en\'er\'e. Pour surmonter ces limitations, nous \'etudions l'emploi de techniques d'apprentissage machine, en particulier l'arbre de d\'ecision d'apprentissage, pour pr\'evoir automatiquement le contexte de la t\^ache \`a partir des transcriptes de navigation d'une session d'exploration de code du d\'eveloppeur. Nous avons effectu\'e une \'etude de cas afin de recueillir des transcriptions de navigation g\'en\'er\'es par des d\'eveloppeurs lors de l'exploration du code source. Nous avons utilis\'e les donn\'ees de cette \'etude pour tester les classifications de l'arbre de d\'ecision. Nous avons compar\'e l'algorithme \`a arbre \`a d\'ecision avec deux approches existantes, et avons d\'emontr\'e que cette nouvelle approche se compare favorablement dans la plupart des cas. Additionnellement, nous avons d\'evelopp\'e un plug-in Eclipse qui g\'en\`ere automatiquement un
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SOUSA, Reudismam Rolim de. "Learning syntactic program transformations from examples." Universidade Federal de Campina Grande, 2018. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1712.

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Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-09-13T20:44:41Z No. of bitstreams: 1 REUDISMAM ROLIM DE SOUSA – TESE (PPGCC) 2018.pdf: 4395945 bytes, checksum: 2241c8bad2cdc8eda86eb53c2e64c227 (MD5)
Made available in DSpace on 2018-09-13T20:44:41Z (GMT). No. of bitstreams: 1 REUDISMAM ROLIM DE SOUSA – TESE (PPGCC) 2018.pdf: 4395945 bytes, checksum: 2241c8bad2cdc8eda86eb53c2e64c227 (MD5) Previous issue date: 2018-08-02
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Ferramentas como ErrorProne, ReSharper e PMD ajudam os programadores a detectar e/ou remover automaticamente vários padrões de códigos suspeitos, possíveis bugs ou estilo de código incorreto. Essas regras podem ser expressas como quick fixes que detectam e reescrevem padrões de código indesejados. No entanto, estender seus catálogos de regras é complexo e demorado. Nesse contexto, os programadores podem querer executar uma edição repetitiva automaticamente para melhorar sua produtividade, mas as ferramentas disponíveis não a suportam. Além disso, os projetistas de ferramentas podem querer identificar regras úteis para automatizarem. Fenômeno semelhante ocorre em sistemas de tutoria inteligente, onde os instrutores escrevem transformações complicadas que descrevem "falhas comuns" para consertar submissões semelhantes de estudantes a tarefas de programação. Nesta tese, apresentamos duas técnicas. REFAZER, uma técnica para gerar automaticamente transformações de programa. Também propomos REVISAR, nossa técnica para aprender quick fixes em repositórios. Nós instanciamos e avaliamos REFAZER em dois domínios. Primeiro, dados exemplos de edições de código dos alunos para corrigir submissões de tarefas incorretas, aprendemos transformações para corrigir envios de outros alunos com falhas semelhantes. Em nossa avaliação em quatro tarefas de programação de setecentos e vinte alunos, nossa técnica ajudou a corrigir submissões incorretas para 87% dos alunos. No segundo domínio, usamos edições de código repetitivas aplicadas por desenvolvedores ao mesmo projeto para sintetizar a transformação de programa que aplica essas edições a outros locais no código. Em nossa avaliação em 56 cenários de edições repetitivas de três grandes projetos de código aberto em C#, REFAZER aprendeu a transformação pretendida em 84% dos casos e usou apenas 2.9 exemplos em média. Para avaliar REVISAR, selecionamos 9 projetos e REVISAR aprendeu 920 transformações entre projetos. Atuamos como projetistas de ferramentas, inspecionamos as 381 transformações mais comuns e classificamos 32 como quick fixes. Para avaliar a qualidade das quick fixes, realizamos uma survey com 164 programadores de 124 projetos, com os 10 quick fixes que apareceram em mais projetos. Os programadores suportaram 9 (90%) quick fixes. Enviamos 20 pull requests aplicando quick fixes em 9 projetos e, no momento da escrita, os programadores apoiaram 17 (85%) e aceitaram 10 delas.
Tools such as ErrorProne, ReSharper, and PMD help programmers by automatically detecting and/or removing several suspicious code patterns, potential bugs, or instances of bad code style. These rules could be expressed as quick fixes that detect and rewrite unwanted code patterns. However, extending their catalogs of rules is complex and time-consuming. In this context, programmers may want to perform a repetitive edit into their code automatically to improve their productivity, but available tools do not support it. In addition, tool designers may want to identify rules helpful to be automated. A similar phenomenon appears in intelligent tutoring systems where instructors have to write cumbersome code transformations that describe “common faults” to fix similar student submissions to programming assignments. In this thesis, we present two techniques. REFAZER, a technique for automatically generating program transformations. We also propose REVISAR, our technique for learning quick fixes from code repositories. We instantiate and evaluate REFAZER in two domains. First, given examples of code edits used by students to fix incorrect programming assignment submissions, we learn program transformations that can fix other students’ submissions with similar faults. In our evaluation conducted on four programming tasks performed by seven hundred and twenty students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive code edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 56 scenarios of repetitive edits taken from three large C# open-source projects, REFAZER learns the intended program transformation in 84% of the cases and using only 2.9 examples on average. To evaluate REVISAR, we select 9 projects, and REVISAR learns 920 transformations across projects. We acted as tool designers, inspected the most common 381 transformations and classified 32 as quick fixes. To assess the quality of the quick fixes, we performed a survey with 164 programmers from 124 projects, showing the 10 quick fixes that appeared in most projects. Programmers supported 9 (90%) quick fixes. We submitted 20 pull requests applying our quick fixes to 9 projects and, at the time of the writing, programmers supported 17 (85%) and accepted 10 of them.
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李偉柏 and Wai-pak Li. "Learning algebra with computer-assisted learning program in a primary school." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31256399.

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Karam, V. (Viera). "Cooperative learning through narratives of the LAB studio learning program participants." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201908172771.

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Abstract. In 2012, Oulu University of Applied Sciences established non-degree training programs called ‘Oamk LABs’, which practice a new form of studio-based model of learning, they called the LAB studio model. Such model includes basic elements of traditional studio-based learning model (practice and reflection-in-action, solving real-life problems, active collaboration with peers and experts of the industry, as well as the learning space) and adds two more features of the multidisciplinary and international environment. This is maintained by keeping the application process open for Finnish and international students from various disciplines, as well as professionals for retraining and unemployed specialists. Since LAB studio learning model is new and evolving phenomena, there is a continuous need to conduct research and contribute to its development. This study has looked at the students’ perspective on the learning process by conducting narrative inquiry research in collaboration with two participants enrolled in one of the Oamk LABs programs. The primary research question for the study is: ‘How do different participants experience the LAB studio learning model?’ The answer to this is presented in the form of two stories placed in the Findings section. The stories were written using narrative analysis of the collected data (narrative interviews, sketched journeys and email conversations) and co-edited together with participants during the research process. The stories reveal two unique journeys reflecting various opinions on the learning model. Those opinions are shaped by the personal and professional background of the participants, as well as the unique circumstances under which each one has studied for the semester. Despite the differences both stories highlight various aspects of cooperative learning during the LAB studies. Thus, the second research question was formed: ‘How do participants experience cooperative learning as a part of the LAB studio learning model?’ The answer to this question is placed in the Discussion part of the research. Narratives revealed three main themes related to the challenges of cooperative learning in the LAB studies: communication issues, expectation gaps, as well as complications caused by the multidisciplinary and international environment.
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Rust, William J. "Learning to program in Java using robots /." Search for this dissertation online, 2006. http://wwwlib.umi.com/cr/ksu/main.

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Bheda, Anuj. "Predictive analytics of active learning based education." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113509.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 113-115).
Learning Analytics (LA) is defined as the collection, measurement, and analysis of data related to student performance such that the feedback from the analytical insights can be used to optimize student learning and improve student outcomes. Blended Learning (BL) is a teaching paradigm that involves a mix of face-to-face interactions in a classroom based setting along with instructional material distributed through an online medium. In this thesis, we explore the role of a blended learning model coupled with learning analytics in an introductory programming class for non-computer science students. We identify the features that were necessary for setting up the infrastructure of the course. These include discussions on preparing the course content materials and producing assignment exercises. We then talk about the various dynamics that were in play during the duration of the class by describing the interplay between watching video tutorials, listening to mini-lectures and performing active learning exercises that are backed by modern software development practices. Lastly, we spend time analyzing the data collected to create a predictive model that can measure student performance by defining the specifications of a machine learning algorithm along with many of its adjustable parameters. The system thus created will allow instructors to identify possible outliers in teaching efficacy, the feedback from which could then be used to tune course material for the betterment of student outcomes.
by Anuj Bheda.
S.M. in Engineering and Management
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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.
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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Thobani, Shaheen. "Improving e-Commerce sales using machine learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118511.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 68-70).
Trends show promising growth of the online e-Commerce industry. While the e-Commerce companies are aggressively moving towards digital sales and marketing, the customers are being bombarded with frequent and often irrelevant marketing communication from myriad sources. The thesis proposes understanding the digital purchase journeys of the customers from the lenses of both sellers and customers to make online sales and marketing efforts relevant and intelligent. The thesis applies the improved customer journey framework to identify the needs of the customers and goals of the seller at various stages of customer purchase journey. It discusses the need to take an integrated view of the purchase journey to improve the customer experience at the journey level. It illustrates with an example how to design end-to-end journeys - a starting point for consciously shaping the purchase journeys. Larger companies are using Machine Learning to improve marketing technologies and processes to create a competitive advantage and capture market share through digital presence. The thesis aims to understand and illustrate the applications of Machine Learning to digital sales and marketing ecosystem for the e- Commerce industry. It first understands the e-Commerce touchpoints using which customers interact with the brands and delves deeper into the underlying technologies powering these touchpoints. Then it illustrates and analyzes the application of Machine Learning to the e-Commerce website which includes search, recommendation system, and Product Detail Page with an aim to improve conversion, and to the advertising ecosystem which includes Data Management Platform and Demand Side Platform in order to enable prospecting and customer targeting. The thesis also illustrates and proposes the use of a framework called 'Machine Learning Canvas' to systematically apply Machine Learning to any system while keeping value proposition for the business in the center.
by Shaheen Thobani.
S.M. in Engineering and Management
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Zhen, Shuyi. "Learning in a pre-service teacher residency program." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1749.

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Teacher educators worldwide are seeking ways to develop stronger links with schools, to improve the quality of initial teacher preparation. In this study the researcher investigated a residency approach to initial teacher education (ITE) in a one-year Graduate Diploma of Education course designed to prepare primary school teachers at a university in Western Australia. A mixed methods inquiry approach was employed to examine the nature and quality of the professional learning experiences of the pre-service teacher participants. The Teacher Residency Program (TRP) was based on a medical residency model, with residents given a semester-length placement in two schools. Each placement provided a continuous two-day per week placement and concluded with a five-week fulltime block teaching experience. University course work complemented the in-school practical experience during each of the placement periods. The researcher used concurrent nested research design with quantitative data embedded in the qualitative data to explore the professional learning of the residents; Specifically, the researcher sought evidence of perspective transformation in the residents’ learning process, and a determination of how particular elements of the TRP supported or hindered residents’ professional development. The findings indicate that the residents perceived their professional learning as one of continuous growth. The data confirm that the TRP provided a professionally oriented study of teaching that provided time and opportunity for cumulative learning from both course work and practical teaching. Through the lens of a transformative learning paradigm, the study found that individual development in the TRP varies considerably. Further the residents experienced perspective transformation relating to a range of aspects about learning to teach. The process of transforming perspectives about teaching and learning was multidimensional, individualistic and contextually dependent. Elements of the TRP that assisted residents’ professional development were the concurrent university course work integrated with extended clinical school placement; the opportunities for continuous two-day per week school placement; the block practicum and the two different school placement learning experiences. Aspects that appeared to hinder residents’ professional development were related to the quantity and quality of the university course work offered; the discrepancies that the residents experienced between what they studied about teaching and what they experienced in schools; differences between mentor teachers, and the length of the course. The findings of this study have implications relevant to other ITE programs for improving the learning outcomes of pre-service teachers.
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Books on the topic "Learning to program"

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Johnston, Howard. Learning to program. Englewood Cliffs: Prentice/Hall, 1985.

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Johnston, Howard. Learning to program. Englewood Cliffs, NJ: Prentice/Hall International, 1985.

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Feldman, Isabel. The Learning Strategies program. [United States]: I. Feldman, 2003.

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Learning C. New York: McGraw-Hill, 1992.

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Aitken, Peter G. Learning C. Carmel, Ind., USA: SAMS, 1991.

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Armstrong, Margaret A. Learning FORTH. New York: Wiley, 1985.

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Learning VBScript. Beijing: O'Reilly, 1997.

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Learning BASIC. Carmel, Ind., USA: SAMS, 1992.

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Learning C++. New York: McGraw-Hill, 1991.

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Learning C. New York: McGraw-Hill, 1992.

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Book chapters on the topic "Learning to program"

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Neumeister, Kristie Speirs, and Virginia Hays Burney. "Professional Learning." In GIFTED Program Evaluation, 107–14. 2nd ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003235354-12.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Logic Program." In Encyclopedia of Machine Learning, 631. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_490.

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Beecher, Karl. "Learning to Program." In Bad Programming Practices 101, 1–7. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3411-2_1.

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Goertzel, Ben, Cassio Pennachin, and Nil Geisweiller. "Procedure Learning as Program Learning." In Atlantis Thinking Machines, 213–16. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-030-0_12.

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Utgoff, Paul E., James Cussens, Stefan Kramer, Sanjay Jain, Frank Stephan, Luc De Raedt, Ljupčo Todorovski, et al. "Inductive Program Synthesis." In Encyclopedia of Machine Learning, 537. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_398.

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Sałustowicz, Rafał, and Jürgen Schmidhuber. "Probabilistic Incremental Program Evolution: Stochastic search through program space." In Machine Learning: ECML-97, 213–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62858-4_86.

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Stahl, Gerry. "Doctoral Consortium Program." In Computer Support for Collaborative Learning, 751. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781315045467-207.

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Oakes, William, and Carla Zoltowski. "EPICS Program." In Service-Learning in the Computer and Information Sciences, 27–38. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118319130.ch2.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Program Synthesis From Examples." In Encyclopedia of Machine Learning, 805. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_672.

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Murray, Michael A., and Laura Balogh. "Learning as a Healing Experience." In The Therapeutic Inclusion Program, 61–75. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003270478-6.

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Conference papers on the topic "Learning to program"

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Braune, Gert, and Andreas Mühling. "Learning to program." In WiPSCE '20: Workshop in Primary and Secondary Computing Education. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3421590.3421597.

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Piwek, Paul, Michel Wermelinger, Robin Laney, and Richard Walker. "Learning to program." In CEP '19: Computing Education Practice. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3294016.3294024.

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Lishinski, Alex, Aman Yadav, Jon Good, and Richard Enbody. "Learning to Program." In ICER '16: International Computing Education Research Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2960310.2960329.

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McGowan, Aidan, Philip Hanna, Des Greer, and John Busch. "Learning to Program." In ITiCSE '17: Innovation and Technology in Computer Science Education. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3059009.3059020.

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Chand, Rajni, Raveena Goundar, and Pratish Raj. "Semester Zero: An Innovative Orientation and Nurturing Program." In Tenth Pan-Commonwealth Forum on Open Learning. Commonwealth of Learning, 2022. http://dx.doi.org/10.56059/pcf10.3941.

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The current Coronavirus (Covid-19) pandemic has affected many universities’ orientation as well as study programs. While some are still contemplating whether to open their doors for F2F studies, others face the challenge of fewer students enrolling due to travel restrictions. The University of the South Pacific (USP) will face a different kind of challenge. USP is a regional university co-owned by twelve South Pacific countries. Campuses and sub-campuses are situated in all these countries. Usually, orientation programmes are held at all campuses, but the majority of students travel early to USP’s main campus in Fiji for the orientation program. Due to Covid-19, students’ travel will be restricted, and the orientation programme will be a very small-scale event this year. Additionally, due to delays in Year 13 external examinations for one of its member countries (Fiji), USP will be forced to begin Semester One later. This means more than 3000 provisional students will have to wait for the University to begin in late March instead of the usual mid-February start. To keep these potential students engaged for close to two months, USP initiated an online orientation programme called ""Semester Zero."" The aim of the programme is to introduce these potential new students to USP using a Moodle platform instead of a traditional F2F orientation program, while at the same time incorporating basic numeracy, literacy, and digital skills necessary for new students. // This paper shares the planning, designing, and feedback from students about this program. Semester Zero employs technology and innovations that have a low carbon footprint.
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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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"Program Committee." In Fourth International Conference on Machine Learning and Applications. IEEE, 2005. http://dx.doi.org/10.1109/icmla.2005.54.

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"Program Committee." In 2015 Fifth International Conference on e-Learning (econf). IEEE, 2015. http://dx.doi.org/10.1109/econf.2015.7.

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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. California: 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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"Program committee." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6358955.

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Reports on the topic "Learning to program"

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Silverman, Howard. Integrative Medicine Distance-Learning Program. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada451755.

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Lintern, Gavan. The Learning Strategies Program: Concluding Remarks. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada226016.

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Griffith, David, Susan Heller-Zeisler, Joy Herman, Andrew Jackson, Janine Kerns, Donna Kimball, William E. Wallace, and Brian A. Weiss. Providing NIST Supervisors with a Continuous Learning Program. National Institute of Standards and Technology, April 2011. http://dx.doi.org/10.6028/nist.ir.7776.

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Wisniewski, Matt Wisniewski. Learning Alongside Grantees: Environment Program Examples and Reflections. San Francisco, CA United States: S. D. Bechtel, Jr. Foundation, December 2020. http://dx.doi.org/10.15868/socialsector.37832.

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Hoffman, Lee M., Clifford P. Hahn, Diane M. Hoffman, and Robin A. Dean. Evaluation of the Job Skills Education Program: Learning Outcomes. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada205352.

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Cianciolo, Anna T. Program Evaluation Metrics for U.S. Army Lifelong Learning Centers. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada465470.

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Knight, Ruth, and Kylie Kingston. Gaining feedback from children in The Love of Learning Program. Queensland University of Technology, November 2020. http://dx.doi.org/10.5204/rep.eprints.206154.

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This report details both the process undertaken to develop an evaluation instrument that can collect feedback from children in the Love of Learning program and feedback the children have provided. A total of 178 children who are beneficiaries of the program completed the survey, and 91% confirmed the program was positively supporting them. They provided their feedback using a 20-question survey which measured four protective factors that previous research suggests supports children to engage with and enjoy learning, helping them to thrive in school and life. The protective factors are known to foster social, emotional, and academic development and success. There is a strong positive association between these factors, and the results of the survey suggest the Love of Learning program is influencing children's attitude towards learning and school. This report highlights some of the design challenges and complexities when engaging children in participatory evaluation. Importantly, to ensure children are given an opportunity to provide feedback, they must be supported by their foster carer who need to also feel informed and confident to be part of the evaluation process and empower children to speak up. Further research will now be conducted to implement the evaluation process more widely and ascertain if the protective factors improve a child’s health, educational engagement, and performance.
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Schmid, Ute, and Fritz Wysotzki. Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada382307.

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WAKEFIELD, SHAWNA, and DANIELA KOERPPEN. Applying Feminist Principles to Program Monitoring, Evaluation, Accountability and Learning. Oxfam, July 2017. http://dx.doi.org/10.21201/2017.9965.

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Godfrey, Kathleen. Global Learning Outcomes of a Domestic Foreign Language Immersion Program. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1034.

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