Relevant bibliographies by topics / Theories of learning

Contents

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

Academic literature on the topic 'Theories of learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 June 2025

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Journal articles on the topic "Theories of learning"

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Isbell, Dan. "Learning Theories." General Music Today 25, no. 2 (2011): 19–23. http://dx.doi.org/10.1177/1048371311425684.

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Case, Robbie. "Theories of learning and theories of development." Educational Psychologist 28, no. 3 (1993): 219–33. http://dx.doi.org/10.1207/s15326985ep2803_3.

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Twomey, Elizabeth. "Linking learning theories and learning difficulties." Australian Journal of Learning Disabilities 11, no. 2 (2006): 93–98. http://dx.doi.org/10.1080/19404150609546812.

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Hartsell, Taralynn. "Learning Theories and Technology." International Journal of Information and Communication Technology Education 2, no. 1 (2006): 53–64. http://dx.doi.org/10.4018/jicte.2006010105.

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Coleman, Daniel. "Learning About Therapy Theories." Journal of Teaching in Social Work 23, no. 3-4 (2003): 73–89. http://dx.doi.org/10.1300/j067v23n03_07.

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Teso, Stefano, Roberto Sebastiani, and Andrea Passerini. "Structured learning modulo theories." Artificial Intelligence 244 (March 2017): 166–87. http://dx.doi.org/10.1016/j.artint.2015.04.002.

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Giannoukos, Georgios. "Main Learning Theories in Education." European Journal of Contemporary Education and E-Learning 2, no. 5 (2024): 93–100. https://doi.org/10.59324/ejceel.2024.2(5).06.

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Six major learning theories explain how people acquire knowledge and skills: behaviorism focuses on how environmental stimuli and consequences shape observable behaviors; cognitivism emphasizes internal mental processes and representations; constructivism highlights learners actively constructing their own understanding; humanism stresses personal experiences, values, and motivations; connectionism emphasizes neural networks and distributed representations; and sociocultural learning theory which according to this theory, learning takes place through collaborative activities with others, where individuals build upon their existing knowledge and skills. These influential theories have impacted educational practices and sparked ongoing debates as researchers and practitioners grapple with the complexity of learning.
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Byrne, Janice, and Olivier Toutain. "Research in Entrepreneurship: Learning from Learning Theories." Academy of Management Proceedings 2012, no. 1 (2012): 14080. http://dx.doi.org/10.5465/ambpp.2012.14080abstract.

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Naigles, Letitia R. "Why theories of word learning don't always work as theories of verb learning." Behavioral and Brain Sciences 24, no. 6 (2001): 1113–14. http://dx.doi.org/10.1017/s0140525x01320137.

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Bloom's theory of word learning has difficulty accounting for children's verb acquisition. There is no predominant preverbal event concept, akin to the preverbal object concept, to direct children's early event-verb mappings. Children may take advantage of grammatical and linguistic information in verb acquisition earlier than Bloom allows. A distinction between lexical and grammatical learning is difficult to maintain for verb acquisition.
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Prata, David, George França, and Marcelo Lisboa. "Entailment Issues to Integrate Learning Theories with Educational Technologies." International Journal of Information and Education Technology 6, no. 9 (2016): 726–30. http://dx.doi.org/10.7763/ijiet.2016.v6.783.

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Dissertations / Theses on the topic "Theories of learning"

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Sgroi, Daniel. "Theories of learning in economics." Thesis, University of Oxford, 2000. http://ora.ox.ac.uk/objects/uuid:b8d832af-57e7-45c2-a846-b69de3d25ec0.

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How should we model learning behaviour in economic agents? This thesis addresses this question in two distinct ways. In the first set of chapters the assumption is that agents learn through the observation of others. They use Bayesian updating which together with specific informational assumptions can generate the problem known as herding with the potential for significant welfare losses. In the final set of chapters the agent is instead modelled as learning by example. Here the agent cannot learn by observing others, but has a pool of experience to fall back on. This allows us to examine how an economic agent will perform if he sees a particular economic situation (or game) for the first time, but has experience of playing related games. The tool used to capture the notion of learning through example is a neural network. Throughout the thesis the central theme is that economic agents will naturally use as much information as they can to help them make decisions. In many cases this should mean they take into consideration others' actions or their own experiences in similar but non-identical situations. Learning throughout the thesis will be rational or bounded-rational in the sense that either the best possible way to learn will be utilized (so players achieve full rational play, for example, through Bayesian updating), or a suitable local error-minimizing algorithm will be developed (for example, a rule of thumb which optimizes play in a subclass of games, but not in the overall set of possible games). Several themes permeate the whole thesis, including the scope for firms or planners to manipulate the information that is used by agents for their own ends, the role of rules of thumb, and the realism of current theories of learning in economics.
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Yang, Liu. "Mathematical Theories of Interaction with Oracles." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/559.

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Paulsson, Fredrik. "Modularization of the Learning Architecture : Supporting Learning Theories by Learning Technologies." Doctoral thesis, Stockholm : Datavetenskap och kommunikation, Kungliga Tekniska högskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4712.

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Ng, Kee Siong, and kee siong@rsise anu edu au. "Learning Comprehensible Theories from Structured Data." The Australian National University. Research School of Information Sciences and Engineering, 2005. http://thesis.anu.edu.au./public/adt-ANU20051031.105726.

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This thesis is concerned with the problem of learning comprehensible theories from structured data and covers primarily classification and regression learning. The basic knowledge representation language is set around a polymorphically-typed, higher-order logic. The general setup is closely related to the learning from propositionalized knowledge and learning from interpretations settings in Inductive Logic Programming. Individuals (also called instances) are represented as terms in the logic. A grammar-like construct called a predicate rewrite system is used to define features in the form of predicates that individuals may or may not satisfy. For learning, decision-tree algorithms of various kinds are adopted.¶ The scope of the thesis spans both theory and practice. On the theoretical side, I study in this thesis¶ 1. the representational power of different function classes and relationships between them;¶ 2. the sample complexity of some commonly-used predicate classes, particularly those involving sets and multisets;¶ 3. the computational complexity of various optimization problems associated with learning and algorithms for solving them; and¶ 4. the (efficient) learnability of different function classes in the PAC and agnostic PAC models.¶ On the practical side, the usefulness of the learning system developed is demontrated with applications in two important domains: bioinformatics and intelligent agents. Specifically, the following are covered in this thesis:¶ 1. a solution to a benchmark multiple-instance learning problem and some useful lessons that can be drawn from it;¶ 2. a successful attempt on a knowledge discovery problem in predictive toxicology, one that can serve as another proof-of-concept that real chemical knowledge can be obtained using symbolic learning;¶ 3. a reworking of an exercise in relational reinforcement learning and some new insights and techniques we learned for this interesting problem; and¶ 4. a general approach for personalizing user agents that takes full advantage of symbolic learning.
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Ng, Kee Siong. "Learning comprehensible theories from structured data /." View thesis entry in Australian Digital Theses Program, 2005. http://thesis.anu.edu.au/public/adt-ANU20051031.105726/index.html.

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Chiang, Michael Chi-Hao. "Learning latent theories of relations and individuals." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/33750.

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Inductive learning of statistical models from relational data is a key problem in artificial intelligence. Two main approaches exist for learning with relational data, and this thesis shows how they can be combined in a uniform framework. The first approach aims to learn dependencies amongst features (relations and properties), e.g. how users' purchases of products depend on users' preferences of the products and associated properties of users and products. Such models abstract over individuals, and are compact and easy to interpret. The second approach learns latent properties of individuals that explain the observed features, without modelling interdependencies amongst features. Latent-property models have demonstrated good predictive accuracy in practise, and are especially useful when few properties and relations are observed. Interesting latent groupings of individuals can be discovered. Our approach aims to learn a unified representation for dependency structures for both observed features and latent properties. We develop a simple approximate EM algorithm for learning the unified representation, and experiments demonstrate cases when our algorithm can generate models that predicts better than dependency-based models of observed features as well as a state-of-the-art latent-property model. We extend our approximate EM algorithm to handle uncertainty about the number of values for latent properties. We search over the number of values and return error bounds, as an alternative to existing proposals based on sampling in the posterior distribution over the number of values. We also solve a specific case where dependencies involve functional relations, which induces a verbose model with many parameters. In comparison, the standard solution of aggregating over all values of the function yields a simple model that predicts poorly. We show how to learn an optimal intermediate-size representation efficiently by clustering the values of the function. The proposed method generates models that capture interesting clusters of function values, dominates the simple model in prediction, and can surpass the verbose model using much fewer parameters.
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Al-Azawei, Ahmed Habeeb Said. "Modelling e-learning adoption : the influence of learning style and universal learning theories." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/77921/.

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E-learning faces a high failure rate, particularly in developing nations due to a lack of public acceptance. One of the most commonly adopted theories for investigating this is the Technology Acceptance Model (TAM). However, TAM fails to consider the effect of individual and cultural differences, and environmental variables on users’ technology acceptance. The present research therefore sought to address TAM’s limitations in the elearning context, by considering individual differences adopting the Felder and Silverman Learning Styles Model (FSLSM) and environmental learning factors using the Universal Design for Learning (UDL) framework. It also attempts to identify any barriers to effective e-learning implementation in Iraq. A survey research design comprising analytical and descriptive methods was consequently adopted. Two experiments were conducted in Iraq to validate the proposed research framework from the perspective of undergraduate students. A third experiment was dedicated to in-depth understanding the hindrances to e-learning application in Iraq’s public-sector universities, from the standpoint of undergraduates and academic staff. The findings suggest that the explanatory power of TAM can be improved by integrating learning styles as moderators, although this psychological trait has limited ability to predict e-learning acceptance and learners’ perceptions as well as it is uncorrelated with academic performance. However, combining the UDL model with TAM enhances its power to predict e-learning acceptance and learners’ perceptions. Furthermore, the qualitative outcomes identify many barriers to e-learning implementation in Iraq, supporting the quantitative analysis and highlighting other factors that could influence e-learning acceptance. This study should provide valuable information for scholars, leadership, e-learning providers and instructors, while also contributing to TAM, learning styles and universal learning theories. It is the first of its kind to examine e-learning acceptance in Iraq, in terms of the integrated factors. This research is also the first to compare the influence of learning styles and universal learning theories on e-learning experience. Accordingly, it extends the existing literature and fills a research gap in the Iraqi context, with empirical implications being discussed for further research.
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Weibell, Christian J. "Principles of Learning: A Conceptual Framework for Domain-Specific Theories of Learning." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2759.

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This study is predicated on the belief that there does not now exist, nor will there ever exist, any single theory of learning that is broad enough to account for all types of learning yet specific enough to be maximally useful in practical application. Perhaps this dichotomy is the reason for the apparent gap between existing theories of learning and the practice of instructional design. As an alternative to any supposed grand theory of learning—and following the lead of prominent thinkers in the fields of clinical psychology and language teaching—this study proposes a shift toward principles. It presents a principle-based conceptual framework of learning, and recommends use of the framework as a guide for creating domain-specific theories of learning. The purpose of this study was to review theories of learning in the behavioral, cognitive, constructive, human, and social traditions to identify principles of learning local to those theories that might represent specific instances of more universal principles, fundamentally requisite to the facilitation of learning in general. Many of the ideas reviewed have resulted from, or been supported by, direct empirical evidence. Others have been suggested based on observational or practical experience of the theorist. The ideas come from different points in time, are described from a variety of perspectives, and emphasize different aspects and types of learning; yet there are a number of common themes shared among them regarding the means by which learning occurs. It is hypothesized that such themes represent universal and fundamental principles of learning. These principles were the objective of the present study. They have been sought through careful review and analysis of both theoretical and empirical literature by methods of textual research (Clingan, 2008) and constant comparative analysis (Glaser & Strauss, 1967). By way of textual research a methodological lens was defined to identify general themes, and by way of constant comparative analysis these themes were developed further through the analysis and classification of specific instances of those themes in the texts reviewed. Ten such principles were identified: repetition, time, step size, sequence, contrast, significance, feedback, context, engagement, and agency. These ten facilitative principles were then organized in the context of a comprehensive principles-of-learning framework, which includes the four additional principles of potential, target, change, and practice.
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Tang, Yuet-ho Maysie. "Implicit theories of intelligence among gifted children." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B29789886.

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Prieto, Pérez Nevia, and Martín Silvana San. "Teaching and learning Methods: Theories and Trends in L2." Tesis, Universidad de Chile, 2004. http://www.repositorio.uchile.cl/handle/2250/110138.

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Informe de Seminario para optar al grado de Licenciado en Lengua y Literatura Inglesa.<br>After many years of hard study and careful research, we can state that focusing on the teaching area is not the key to achieve a good proficiency when learning a foreign language. This idea is based on the fact that, even though there have been many attempts to develop a good method for teaching languages through many decades, none of them has proved to be accepted by the whole language teaching community. Since our work was originally planned to encompass teaching and learning, from our point of view the process of learning is undoubtedly the most important thing. Consequently it must be studied in depth.
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Books on the topic "Theories of learning"

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Scott, David. Theories of Learning. SAGE Publications Ltd, 2013. http://dx.doi.org/10.4135/9781446286173.

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B, Klein Stephen, and Mowrer Robert R. 1956-, eds. Contemporary learning theories. L. Erlbaum Associates, 1989.

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B, Klein Stephen, and Mowrer Robert R. 1956-, eds. Contemporary learning theories. L. Erlbaum Associates, 1989.

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Gibson, David C., and Dirk Ifenthaler. Computational Learning Theories. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65898-3.

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B, Klein Stephen, and Mowrer Robert R. 1956-, eds. Contemporary learning theories. L. Erlbaum, 1989.

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P, Steffe Leslie, and International Congress on Mathematical Education (7th : 1992 : Québec, Québec), eds. Theories of mathematical learning. L. Erlbaum Associates, 1996.

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Florence, Myles, ed. Second language learning theories. Arnold, 1998.

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Samuel, Shermis S., ed. Learning theories for teachers. 6th ed. Longman, 1999.

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Samuel, Shermis S., ed. Learning theories for teachers. 5th ed. HarperCollins, 1992.

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Florence, Myles, ed. Second language learning theories. 2nd ed. Arnold, 2004.

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Book chapters on the topic "Theories of learning"

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Sims, Roderick. "Which Learning Theories Learning theories ?" In Design Alchemy. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02423-3_4.

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Ryll, Wolfgang. "Learning Theories." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61467-5_5.

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Benson, Brooke A., Stephen C. Messer, and Alan M. Gross. "Learning Theories." In Handbook of Social Development. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-0694-6_4.

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O'Connor, Paul, Angela O'Dea, and Dara Byrne. "Learning Theories." In The Essential Handbook of Healthcare Simulation. CRC Press, 2023. http://dx.doi.org/10.1201/9781003296942-2.

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Alqahtani, Dalal Abdullah, Mohammed Yahya Alnaami, and Eiad Abdelmohsen Alfaris. "Learning Theories." In Novel Health Interprofessional Education and Collaborative Practice Program: Strategy and Implementation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3420-1_10.

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Lamy, Marie-Noëlle, and Regine Hampel. "Learning Theories." In Online Communication in Language Learning and Teaching. Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230592681_3.

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Domjan, Michael, and Melissa Burns. "Learning theories." In Encyclopedia of psychology, Vol. 5. American Psychological Association, 2000. http://dx.doi.org/10.1037/10520-013.

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Newman, Barbara M., and Philip R. Newman. "Learning Theories." In Theories of Human Development, 3rd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003014980-7.

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Behl, Joshua D., and Leonard A. Steverson. "Learning Theories." In Criminal Theory Profiles. Routledge, 2021. http://dx.doi.org/10.4324/9781003036609-5.

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Ahmad, Iqbal, and Rani Gul. "Service-Learning Theories." In Service-Learning. Routledge India, 2023. http://dx.doi.org/10.4324/9781003461760-10.

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Conference papers on the topic "Theories of learning"

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Alatawi, Areej, Ebru Burcu, Dimitris Kalogiros, and Jesús Requena Carrión. "Interactive Visual Learning in Machine Learning: A Cognitive Learning Theories-Driven Approach." In 2025 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2025. https://doi.org/10.1109/educon62633.2025.11016355.

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Alomyan, Hesham, and Deborah Green. "Learning Theories." In ICSET 2019: 2019 The 3rd International Conference on E-Society, E-Education and E-Technology. ACM, 2019. http://dx.doi.org/10.1145/3355966.3358412.

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Liakata, Maria, and Stephen Pulman. "Learning theories from text." In the 20th international conference. Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1220355.1220382.

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Farsal, Wissal, Samir Anter, and Mohammed Ramdani. "Deep Learning." In SITA'18: THEORIES AND APPLICATIONS. ACM, 2018. http://dx.doi.org/10.1145/3289402.3289538.

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Talaghzi, Jallal, Abdellah Bennane, Mohammed Majid Himmi, Mostafa Bellafkih, and Aziza Benomar. "Online Adaptive Learning." In SITA'20: Theories and Applications. ACM, 2020. http://dx.doi.org/10.1145/3419604.3419759.

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Achsas, Sanae, and El Habib Nfaoui. "Language representation learning models." In SITA'20: Theories and Applications. ACM, 2020. http://dx.doi.org/10.1145/3419604.3419773.

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Amraouy, Mohammed, Abdellah Bennane, Mohammed Majid Himmi, Mostafa Bellafkih, and Aziza BenOmar. "Detecting the Learner's Motivational State in Online Learning Situation Towards Adaptive Learning Environments." In SITA'20: Theories and Applications. ACM, 2020. http://dx.doi.org/10.1145/3419604.3419760.

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Abidar, Lahcen, Dounia Zaidouni, and Abdeslam Ennouaary. "Customer Segmentation With Machine Learning." In SITA'20: Theories and Applications. ACM, 2020. http://dx.doi.org/10.1145/3419604.3419794.

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Bahani, Khalid, Mohammed Moujabbir, and Mohammed Ramdani. "Fuzzy Rule Learning with Linguistic Modifiers." In SITA'18: THEORIES AND APPLICATIONS. ACM, 2018. http://dx.doi.org/10.1145/3289402.3289533.

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Haddouchi, Maissae, and Abdelaziz Berrado. "Assessing interpretation capacity in Machine Learning." In SITA'18: THEORIES AND APPLICATIONS. ACM, 2018. http://dx.doi.org/10.1145/3289402.3289549.

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Reports on the topic "Theories of learning"

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Klenk, Matthew, and Kenneth D. Forbus. Learning Domain Theories via Analogical Transfer. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada470404.

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Curnow, Christina, Rebecca Mulvaney, Robert Calderon, et al. Advanced Learning Theories Applied to Leadership Development. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462784.

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Klenk, Matthew, and Ken Forbus. Cross Domain Analogies for Learning Domain Theories. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada471251.

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Higdon, Grace Lyn. Nested Theories of Change for Adaptive Rigour. Institute of Development Studies (IDS), 2020. http://dx.doi.org/10.19088/creid.2020.010.

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This learning brief provides illustrative examples of three major adaptations the Coalition for Religious Equality and Inclusive Development (CREID) programme has undertaken while working within fragile contexts to promote freedom of religion or beilief (FoRB). These examples highlight how the programme has utilised embedded monitoring, evaluation, and learning (MEL) strategies to encourage what Ramalingam et al. (2019) refer to as ‘adaptive rigour’, a concept which underscores the importance of transparent documentation of programmatic decision making processes during programme adaptations. In particular this learning brief discusses the use of nested theories of change as a mechanism to enable adaptive rigour within the 'multitude of smalls' approach in the CREID programme.
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Kingsbury, Penelope. Analysis of Learning Organization Theories and their Application to Public Organizations. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada364145.

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Poczos, Barnabas. Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1572709.

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Orbán, Levente. Associative Learning in Psychology: Classical and Operant Conditioning Revisited. Orban Foundation, 2025. https://doi.org/10.69642/5323.

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Learning is a central concept in psychology, governing how organisms adapt to their environments. This paper revisits foundational theories of associative learning, with an emphasis on classical conditioning, as discovered by Ivan Pavlov, and operant conditioning, as developed by B.F. Skinner. Drawing from a transcript of a live undergraduate lecture, we explore theoretical models, empirical evidence, practical examples, and mathematical structures underlying these learning mechanisms. The aim is to distill key insights while highlighting the contemporary relevance of behaviorist principles in domains like behavioral design, education, and machine learning.
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Rashid, MdSanuwar, and Jong Geun Lee. How to Organize Course Evaluation Components to Optimize Learning Outcome: An Application of Pedagogical Theories. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1540.

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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Devereux, Stephen, and Anna Wolkenhauer. Agents, Coercive Learning, and Social Protection Policy Diffusion in Africa. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/ids.2021.068.

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This paper makes theoretical, empirical, and methodological contributions to the study of social policy diffusion, drawing on the case of social protection in Africa, and Zambia in particular. We examine a range of tactics deployed by transnational agencies (TAs) to encourage the adoption of cash transfers by African governments, at the intersection between learning and coercion, which we term ‘coercive learning’, to draw attention to the important role played by TA-commissioned policy drafting, evidence generation, advocacy, and capacity-building activities. Next, we argue for making individual agents central in the analysis of policy diffusion, because of their ability to reflect, learn, and interpret policy ideas. We substantiate this claim theoretically by drawing on practice theories, and empirically by telling the story of social protection policy diffusion in Zambia through three individual agents. This is complemented by two instances of self-reflexivity in which the authors draw on their personal engagements in the policy process in Zambia, to refine our conclusions about the interplay of structure and agency.
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