Relevant bibliographies by topics / Application of learning theory

Academic literature on the topic 'Application of learning theory'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Application of learning theory"

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Lowndes, Diane EN(G), Anwen EN(G) Boult, and Lap Yee (M) Wong EN. "An application of learning theory." Nursing Standard 5, no. 51 (September 11, 1991): 28–30. http://dx.doi.org/10.7748/ns.5.51.28.s47.

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Najim, K., and G. Oppenheim. "Learning systems: Theory and application." IEE Proceedings E Computers and Digital Techniques 138, no. 4 (1991): 183. http://dx.doi.org/10.1049/ip-e.1991.0025.

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Farhan, Bayan Yousef. "Application Of Path-Goal Leadership Theory And Learning Theory In A Learning Organization." Journal of Applied Business Research (JABR) 34, no. 1 (December 29, 2017): 13–22. http://dx.doi.org/10.19030/jabr.v34i1.10088.

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This paper used learning theory and path-goal theory to propose a learning leadership model. The practical side of these two theories is suitable for this proposed model, its agenda, and its tools for improving organizational learning. The model is based on the assumption that leaders need to develop their competences and the competences of subordinates by creating a continuous learning process through sharing, exchanging, and improving knowledge and experiences. The model states that learning leaders act as facilitators of the organizational learning process and adopt the right attitude to motivate subordinates for learning by removing obstacles and creating a learning culture.
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Landry-Meyer, Laura, Su Yun Bae, John Zibbel, Susan Peet, and Deborah G. Wooldridge. "Transformative Learning." International Journal of Adult Vocational Education and Technology 10, no. 4 (October 2019): 1–15. http://dx.doi.org/10.4018/ijavet.2019100101.

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The aim of this article is to connect transformative learning theory with the practice of teaching in higher education. Connecting theory to effective active learning pedagogy is good practice in teaching adults, andragogy. Using transformative learning theory as a guide, this article describes the historical evolution of transformative learning theory and describes specific application in higher education using Chickering and Gamson's principles of undergraduate education. The discussion of teaching and learning examples from face-to-face, online, service-learning, and short-term study abroad contexts provide the reader with concrete applications.
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Jin, Yuxi. "Development and Application of Social Learning Theory." Learning & Education 10, no. 7 (June 7, 2022): 183. http://dx.doi.org/10.18282/l-e.v10i7.3002.

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Social learning theory is the most mentioned theory in understanding people’s learning process. This theory mainly explains the learning process of individuals in a social environment. In a social environment, individuals can learn through imitation, observation, and modeling. Social learning methods take the thinking process into account and provide a more comprehensive explanation of human learning through understanding and observing the role of the learning process.
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Joseph, Sylvia. "Social Learning Theory Application on Bullying Phenomenon." Journal of International Business Research and Marketing 6, no. 6 (September 2021): 7–12. http://dx.doi.org/10.18775/10.18775/jibrm.1849-8558.2015.66.3001.

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This paper aims to examine the social learning theory utilization in countering the bullying phenomenon in Egypt. The paper investigates the consequences of bullying on mental and emotional health of the bullied person. The purpose of this research is to shed light on the bullying incidents happening in Egypt and its relationship with the social learning theory.
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Chauhan, S. P., E. F. Magann, C. B. McAninch, R. B. Gherman, and J. C. Morrison. "Application of learning theory to obstetric maloccurrence." Journal of Maternal-Fetal & Neonatal Medicine 13, no. 3 (January 2003): 203–7. http://dx.doi.org/10.1080/jmf.13.3.203.207.

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Price, Vincent, and John Archbold. "Development and application of social learning theory." British Journal of Nursing 4, no. 21 (November 23, 1995): 1263–68. http://dx.doi.org/10.12968/bjon.1995.4.21.1263.

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Norman, Leanne. "Learning in sports coaching: theory and application." Leisure/Loisir 40, no. 4 (October 2016): 495–96. http://dx.doi.org/10.1080/14927713.2016.1276730.

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Chauhan, S. P., E. F. Magann, C. B. McAninch, R. B. Gherman, and J. C. Horrison. "Application of Learning Theory to Obstetric Maloccurrence." Obstetrical & Gynecological Survey 58, no. 10 (October 2003): 650–52. http://dx.doi.org/10.1097/01.ogx.0000088882.02275.b2.

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Dissertations / Theses on the topic "Application of learning theory"

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Cleeton, G. "Development and application of a theory of learning barriers." Thesis, Keele University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306150.

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Hu, Qiao Ph D. Massachusetts Institute of Technology. "Application of statistical learning theory to plankton image analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/39206.

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Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2006.
Includes bibliographical references (leaves 155-173).
A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed.
(cont.) One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not "good" ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large real-world dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.
by Qiao Hu.
Ph.D.
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Plaza, Cecilia Maria. "The Application of Transformative Learning Theory to Curricular Evaluation." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/194354.

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Purpose: The purpose of this study was to develop a conceptual framework for curricular evaluation based on transformative learning theory and to demonstrate its use in evaluating a professional curriculum. Transformative learning theory considers the process of constructing knowledge through critical reflection on the content, process, and premise of an experience. Methods: Critical reflection was operationalized by using the College's Outcomes Expected document to provide the overarching curricular framework for a reflective portfolio developed by pharmacy students at the University of Arizona College of Pharmacy (UACOP). Content reflection consisted of curricular mapping based on student and faculty questionnaires as well as comparison to the American Association of Colleges of Pharmacy (AACP) Center for the Advancement of Pharmaceutical Education (CAPE) Educational Outcomes 2004. Process reflection focused on best practices literature-based indicators and student self-efficacy measures. Premise reflection included both content and process reflection to develop global recommendations. Results: The population consisted of 284 Doctor of Pharmacy (PharmD) students at the UACOP during the 2004-2005 academic year. Transformative learning theory provides a potentially valuable tool for curricular evaluation by considering the content, process, and premise of construction of knowledge about the pharmacy curricula at respective schools and colleges of pharmacy. This study also demonstrated how transformative learning theory can be applied to both make sense of and use existing data in curricular evaluation. Content reflection revealed concordance between student and faculty ranking of domain and associated competency coverage in their respective curricular maps. Process reflection revealed areas of needed improvement including student and faculty buy-in and the dual use of the portfolio for learning and assessment. Premise reflection provided several global recommendations that other schools and colleges of pharmacy could use in implementing portfolio assessment.
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Shi, Bin. "A Mathematical Framework on Machine Learning: Theory and Application." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3876.

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The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, we apply the optimization algorithms in sparse subspace clustering. A new algorithm, CoCoSSC, is proposed to improve the current sample complexity under the condition of the existence of noise and missing entries. Gradient-based optimization methods have been increasingly modeled and inter- preted by ordinary differential equations (ODEs). Existing ODEs in the literature are, however, inadequate to distinguish between two fundamentally different meth- ods, Nesterov’s acceleration gradient method for strongly convex functions (NAG-SC) and Polyak’s heavy-ball method. In this paper, we derive high-resolution ODEs as more accurate surrogates for the two methods in addition to Nesterov’s acceleration gradient method for general convex functions (NAG-C), respectively. These novel ODEs can be integrated into a general framework that allows for a fine-grained anal- ysis of the discrete optimization algorithms through translating properties of the amenable ODEs into those of their discrete counterparts. As a first application of this framework, we identify the effect of a term referred to as gradient correction in NAG-SC but not in the heavy-ball method, shedding deep insight into why the for- mer achieves acceleration while the latter does not. Moreover, in this high-resolution ODE framework, NAG-C is shown to boost the squared gradient norm minimization at the inverse cubic rate, which is the sharpest known rate concerning NAG-C itself. Finally, by modifying the high-resolution ODE of NAG-C, we obtain a family of new optimization methods that are shown to maintain the accelerated convergence rates as NAG-C for minimizing convex functions.
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Mouton, Hildegarde Suzanne. "Reinforcement learning : theory, methods and application to decision support systems." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5304.

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Thesis (MSc (Applied Mathematics))--University of Stellenbosch, 2010.
ENGLISH ABSTRACT: In this dissertation we study the machine learning subfield of Reinforcement Learning (RL). After developing a coherent background, we apply a Monte Carlo (MC) control algorithm with exploring starts (MCES), as well as an off-policy Temporal-Difference (TD) learning control algorithm, Q-learning, to a simplified version of the Weapon Assignment (WA) problem. For the MCES control algorithm, a discount parameter of τ = 1 is used. This gives very promising results when applied to 7 × 7 grids, as well as 71 × 71 grids. The same discount parameter cannot be applied to the Q-learning algorithm, as it causes the Q-values to diverge. We take a greedy approach, setting ε = 0, and vary the learning rate (α ) and the discount parameter (τ). Experimentation shows that the best results are found with set to 0.1 and constrained in the region 0.4 ≤ τ ≤ 0.7. The MC control algorithm with exploring starts gives promising results when applied to the WA problem. It performs significantly better than the off-policy TD algorithm, Q-learning, even though it is almost twice as slow. The modern battlefield is a fast paced, information rich environment, where discovery of intent, situation awareness and the rapid evolution of concepts of operation and doctrine are critical success factors. Combining the techniques investigated and tested in this work with other techniques in Artificial Intelligence (AI) and modern computational techniques may hold the key to solving some of the problems we now face in warfare.
AFRIKAANSE OPSOMMING: Die fokus van hierdie verhandeling is die masjienleer-algoritmes in die veld van versterkingsleer. ’n Koherente agtergrond van die veld word gevolg deur die toepassing van ’n Monte Carlo (MC) beheer-algoritme met ondersoekende begintoestande, sowel as ’n afbeleid Temporale-Verskil beheer-algoritme, Q-leer, op ’n vereenvoudigde weergawe van die wapentoekenningsprobleem. Vir die MC beheer-algoritme word ’n afslagparameter van τ = 1 gebruik. Dit lewer belowende resultate wanneer toegepas op 7 × 7 roosters, asook op 71 × 71 roosters. Dieselfde afslagparameter kan nie op die Q-leer algoritme toegepas word nie, aangesien dit veroorsaak dat die Q-waardes divergeer. Ons neem ’n gulsige aanslag deur die gulsigheidsparameter te verstel na ε = 0. Ons varieer dan die leertempo ( α) en die afslagparameter (τ). Die beste eksperimentele resultate is behaal wanneer = 0.1 en as die afslagparameter vasgehou word in die gebied 0.4 ≤ τ ≤ 0.7. Die MC beheer-algoritme lewer belowende resultate wanneer toegepas op die wapentoekenningsprobleem. Dit lewer beduidend beter resultate as die Q-leer algoritme, al neem dit omtrent twee keer so lank om uit te voer. Die moderne slagveld is ’n omgewing ryk aan inligting, waar dit kritiek belangrik is om vinnig die vyand se planne te verstaan, om bedag te wees op die omgewing en die konteks van gebeure, en waar die snelle ontwikkeling van die konsepte van operasie en doktrine lei tot sukses. Die tegniekes wat in die verhandeling ondersoek en getoets is, en ander kunsmatige intelligensie tegnieke en moderne berekeningstegnieke saamgesnoer, mag dalk die sleutel hou tot die oplossing van die probleme wat ons tans in die gesig staar in oorlogvoering.
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Gianvecchio, Steven. "Application of information theory and statistical learning to anomaly detection." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623563.

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In today's highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection.
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Collins, Andrew. "Evaluating reinforcement learning for game theory application learning to price airline seats under competition." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/69751/.

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Applied Game Theory has been criticised for not being able to model real decision making situations. A game's sensitive nature and the difficultly in determining the utility payoff functions make it hard for a decision maker to rely upon any game theoretic results. Therefore the models tend to be simple due to the complexity of solving them (i.e. finding the equilibrium). In recent years, due to the increases of computing power, different computer modelling techniques have been applied in Game Theory. A major example is Artificial Intelligence methods e.g. Genetic Algorithms, Neural Networks and Reinforcement Learning (RL). These techniques allow the modeller to incorporate Game Theory within their models (or simulation) without necessarily knowing the optimal solution. After a warm up period of repeated episodes is run, the model learns to play the game well (though not necessarily optimally). This is a form of simulation-optimization. The objective of the research is to investigate the practical usage of RL within a simple sequential stochastic airline seat pricing game. Different forms of RL are considered and compared to the optimal policy, which is found using standard dynamic programming techniques. The airline game and RL methods displays various interesting phenomena, which are also discussed. For completeness, convergence proofs for the RL algorithms were constructed.
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Narasimha, Rajesh. "Application of Information Theory and Learning to Network and Biological Tomography." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19889.

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Studying the internal characteristics of a network using measurements obtained from endhosts is known as network tomography. The foremost challenge in measurement-based approaches is the large size of a network, where only a subset of measurements can be obtained because of the inaccessibility of the entire network. As the network becomes larger, a question arises as to how rapidly the monitoring resources (number of measurements or number of samples) must grow to obtain a desired monitoring accuracy. Our work studies the scalability of the measurements with respect to the size of the network. We investigate the issues of scalability and performance evaluation in IP networks, specifically focusing on fault and congestion diagnosis. We formulate network monitoring as a machine learning problem using probabilistic graphical models that infer network states using path-based measurements. We consider the theoretical and practical management resources needed to reliably diagnose congested/faulty network elements and provide fundamental limits on the relationships between the number of probe packets, the size of the network, and the ability to accurately diagnose such network elements. We derive lower bounds on the average number of probes per edge using the variational inference technique proposed in the context of graphical models under noisy probe measurements, and then propose an entropy lower (EL) bound by drawing similarities between the coding problem over a binary symmetric channel and the diagnosis problem. Our investigation is supported by simulation results. For the congestion diagnosis case, we propose a solution based on decoding linear error control codes on a binary symmetric channel for various probing experiments. To identify the congested nodes, we construct a graphical model, and infer congestion using the belief propagation algorithm. In the second part of the work, we focus on the development of methods to automatically analyze the information contained in electron tomograms, which is a major challenge since tomograms are extremely noisy. Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medically relevant objects with sizes in the range of 10-1000 nm A fundamental step in the statistical inference of large amounts of data is to segment relevant 3D features in cellular tomograms. Procedures for segmentation must work robustly and rapidly in spite of the low signal-to-noise ratios inherent in biological electron microscopy. This work evaluates various denoising techniques and then extracts relevant features of biological interest in tomograms of HIV-1 in infected human macrophages and Bdellovibrio bacterial tomograms recorded at room and cryogenic temperatures. Our approach represents an important step in automating the efficient extraction of useful information from large datasets in biological tomography and in speeding up the process of reducing gigabyte-sized tomograms to relevant byte-sized data. Next, we investigate automatic techniques for segmentation and quantitative analysis of mitochondria in MNT-1 cells imaged using ion-abrasion scanning electron microscope, and tomograms of Liposomal Doxorubicin formulations (Doxil), an anticancer nanodrug, imaged at cryogenic temperatures. A machine learning approach is formulated that exploits texture features, and joint image block-wise classification and segmentation is performed by histogram matching using a nearest neighbor classifier and chi-squared statistic as a distance measure.
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Jalalzai, Hamid. "Learning from multivariate extremes : theory and application to natural language processing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT043.

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Les extrêmes apparaissent dans une grande variété de données. Par exemple,concernant les données hydrologiques, les extrêmes peuvent correspondre à des inondations, des moussons voire des sécheresses. Les données liées à l’activité humaine peuvent également conduire à des situations extrêmes, dans le cas des transactions bancaires, le montant alloué à une vente peut être considérable et dépasser les transactions courantes. Un autre exemple lié à l’activité humaine est la fréquence des mots utilisés : certains mots sont omniprésents alors que d’autres sont très rares. Qu’importe le contexte applicatif, les extrêmes qui sont rares par définition, correspondent à des données particulières. Ces événements sont notamment alarmants au vu de leur potentiel impact désastreux. Cependant, les données extrêmes sont beaucoup moins considérées dans les statistiques modernes ou les pratiques courantes d’apprentissage machine, principalement car elles sont considérablement sous représentées : ces événements se retrouvent noyés - à l’ère du ”big data” - par une vaste majorité de données classiques et non extrêmes. Ainsi, la grande majorité des outils d’apprentissage machine qui se concentrent naturellement sur une distribution dans son ensemble peut être inadaptée sur les queues de distribution où se trouvent les observations extrêmes. Dans cette thèse, les défis liés aux extrêmes sont détaillés et l’accent est mis sur le développement de méthodes dédiées à ces données. La première partie se consacre à l’apprentissage statistique dans les régions extrêmes. Dans le chapitre 4, des garanties non asymptotiques sur l’erreur d’estimation de la mesure angulaire empirique sont étudiées et permettent d’améliorer des méthodes de détection d’anomalies par minimum volume set sur la sphère. En particulier, le problème de la minimisation du risque empirique pour la classification binaire dédiée aux échantillons extrêmes est traitée au chapitre 5. L’analyse non paramétrique et les garanties qui en résultent sont détaillées. L’approche est adaptée pour traiter de nouveaux échantillons se trouvant hors de l’enveloppe convexe formée par les données rencontrées. Cette propriété d’extrapolation est l’élément clé et charnière nous permettant de concevoir de nouvelles représentations conservant un label donné et d’ainsi augmenter la quantité de données. Le chapitre 6 se concentre sur l’apprentissage de cette représentation à queue lourde (pour être précis, à variation régulière) à partir d’une distribution d’entrée. Les illustrations montrent une meilleure classification des extrêmes et conduit à la génération de phrases cohérentes. Enfin, le chapitre 7 propose d’analyser la structure de dépendance des extrêmes multivariés. En constatant que les extrêmes se concentrent au sein de groupes où les variables explicatives ont tendance à prendre –de manière récurrente–de grandes valeurs simultanément ; il en résulte un problème d’optimisation visant à identifier ces sous-groupes grâce à des moyennes pondérées des composantes
Extremes surround us and appear in a large variety of data. Natural data likethe ones related to environmental sciences contain extreme measurements; inhydrology, for instance, extremes may correspond to floods and heavy rainfalls or on the contrary droughts. Data related to human activity can also lead to extreme situations; in the case of bank transactions, the money allocated to a sale may be considerable and exceed common transactions. The analysis of this phenomenon is one of the basis of fraud detection. Another example related to humans is the frequency of encountered words. Some words are ubiquitous while others are rare. No matter the context, extremes which are rare by definition, correspond to uncanny data. These events are of particular concern because of the disastrous impact they may have. Extreme data, however, are less considered in modern statistics and applied machine learning, mainly because they are substantially scarce: these events are out numbered –in an era of so-called ”big data”– by the large amount of classical and non-extreme data that corresponds to the bulk of a distribution. Thus, the wide majority of machine learning tools and literature may not be well-suited or even performant on the distributional tails where extreme observations occur. Through this dissertation, the particular challenges of working with extremes are detailed and methods dedicated to them are proposed. The first part of the thesisis devoted to statistical learning in extreme regions. In Chapter 4, non-asymptotic bounds for the empirical angular measure are studied. Here, a pre-established anomaly detection scheme via minimum volume set on the sphere, is further im-proved. Chapter 5 addresses empirical risk minimization for binary classification of extreme samples. The resulting non-parametric analysis and guarantees are detailed. The approach is particularly well suited to treat new samples falling out of the convex envelop of encountered data. This extrapolation property is key to designing new embeddings achieving label preserving data augmentation. Chapter 6 focuses on the challenge of learning the latter heavy-tailed (and to be precise regularly varying) representation from a given input distribution. Empirical results show that the designed representation allows better classification performanceon extremes and leads to the generation of coherent sentences. Lastly, Chapter7 analyses the dependence structure of multivariate extremes. By noticing that extremes tend to concentrate on particular clusters where features tend to be recurrently large simulatenously, we define an optimization problem that identifies the aformentioned subgroups through weighted means of features
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Scaggs, Anne Marie. "Student Perspectives on Application of Theory to Practice in Field Practicums." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6112.

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The field practicum is designed to offer students the opportunity to integrate knowledge and practice prior to graduation; however, students continue to lack the ability to connect theory to practice within the field practicum. The purpose of this qualitative case study was to explore the beliefs, attitudes, and perspectives of social work students regarding the application of theory to practice within the field practicum. The conceptual framework included concepts of empowerment, empowerment theory, and social constructivism. The research question addressed how social work students at a local university described the issues related to connecting theory to practice within the field practicum. Data collection involved interviews with 6 social work practicum students, observations, and document analysis. Data were coded and analyzed to identify 4 themes: learned theories, concerns, theory to practice, and student beliefs related to theory and practice. Findings confirmed students' inability to connect theory to practice. Findings were used to develop a project incorporating simulated learning environments in social work curricula to increase the connection of theory to practice. Findings may be used to enhance students' ability to integrate theory into practice, which may strengthen the profession of social work through improved service delivery at local, state, national, and global levels.
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Books on the topic "Application of learning theory"

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Long, Huey B. Self-directed learning: Application & theory. [Athens, Ga.]: Adult Education Department, [University of Georgia, 1988.

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Hassanien, Aboul Ella, ed. Machine Learning Paradigms: Theory and Application. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02357-7.

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Manifold learning theory and applications. Boca Raton, Fla: CRC, 2012.

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Clinical applications of learning theory. Hove: Psychology Press, 2011.

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Najim, K. Learning automata: Theory and applications. Oxford, OX, U.K: Pergamon, 1994.

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Alfred North Whitehead on learning and education: Theory and application. Newcastle: Cambridge Scholars Press, 2005.

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Ponton, Michael K. A quasi-linear behavioral model and an application to self-directed learning. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

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Learning curves: Theory, models, and applications. Boca Raton: CRC Press, 2011.

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Huang, Kaizhu, Amir Hussain, Qiu-Feng Wang, and Rui Zhang, eds. Deep Learning: Fundamentals, Theory and Applications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06073-2.

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Hanson, Stephen José, Werner Remmele, and Ronald L. Rivest, eds. Machine Learning: From Theory to Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56483-7.

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Book chapters on the topic "Application of learning theory"

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Tanaka, Akinori, Akio Tomiya, and Koji Hashimoto. "Application to Superstring Theory." In Deep Learning and Physics, 173–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9_12.

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Banyard, Philip, and Nicky Hayes. "Learning and remembering." In Psychology: Theory and Application, 257–322. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3007-1_5.

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Dobberfuhl-Quinlan, Jennifer. "Exploring Sociocultural Theory Application in Online Language Courses." In Learning and Collaboration Technologies. Learning and Teaching, 265–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91152-6_21.

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Shawky, Doaa, and Ashraf Badawi. "Towards a Personalized Learning Experience Using Reinforcement Learning." In Machine Learning Paradigms: Theory and Application, 169–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_8.

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Landauer, Tom. "Latent Semantic Analysis: Theory, Method and Application." In Computer Support for Collaborative Learning, 742–43. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781315045467-202.

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Caramanis, Constantine, and Shie Mannor. "An Inequality for Nearly Log-Concave Distributions with Applications to Learning." In Learning Theory, 534–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_37.

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Lowe, Richard. "Animation of Diagrams: An Aid to Learning?" In Theory and Application of Diagrams, 475–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44590-0_40.

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Câmara Pereira, Francisco, and Amíilcar Cardoso. "Clouds: A Module for Automatic Learning of Concept Maps." In Theory and Application of Diagrams, 468–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44590-0_38.

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Ramadan, Rabie A., and Ahmed B. Altamimi. "BCLO—Brainstorming and Collaborative Learning Optimization Algorithms." In Machine Learning Paradigms: Theory and Application, 393–412. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_19.

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Saidi, Rania, Waad Bouaguel, and Nadia Essoussi. "Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient." In Machine Learning Paradigms: Theory and Application, 3–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_1.

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Conference papers on the topic "Application of learning theory"

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Koohang, Alex, Liz Riley, Terry Smith, and Jeanne Schreurs. "E-Learning and Constructivism: From Theory to Application." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3321.

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This paper presents a learner-centered model for designing e-leaming assignments/activities within e-learning environments. The model is based on constructivism learning theory. The model includes two categories - the learning design elements (comprised of fundamental design elements and collaborative elements) and the learning assessment elements (self-assessment, team assessment, and facilitator’s assessment). The application of the model is shown through various working examples. A factual situation using the model within an e-learning course is presented to further demonstrate the application of the model in an actual e-learning environment.
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Salamah, Umi, Nuril Mufidah, Idrus Muchsin Bin Agil, and Iffah Maulana Putri Hanum Soumena. "Application of Behavioristic Learning Theory in Learning “Ta’lim Afkar”." In International Conference on Engineering, Technology and Social Science (ICONETOS 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210421.090.

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Li, Ning, Zhidong Gao, and Xiaomei Qin. "The Theory and Application of Blended Learning." In 2017 International Conference on Economic Development and Education Management (ICEDEM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icedem-17.2017.31.

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Xing, Dr Eric. "Probabilistic Graphical Models-Theory, Algorithm, and Application." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.126.

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Obonyo, Stephen, and Daniel Ruiru. "Multitask Learning or Transfer Learning? Application to Cancer Detection." In 11th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008495805480555.

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Jin-gang, Jiang, Sui Xiu-lin, Yang Wei, Lv Ning, and Zhang Jian-yi. "Application of TRIZ theory in problem based learning." In 2015 10th International Conference on Computer Science & Education (ICCSE). IEEE, 2015. http://dx.doi.org/10.1109/iccse.2015.7250378.

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Zhang, Xiaoying, Hong Xie, and John C. S. Lui. "Heterogeneous Information Assisted Bandit Learning: Theory and Application." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00213.

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Wen, Ping. "Application of Bruner's Learning Theory in Mathematics Studies." In Proceedings of the International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/cesses-18.2018.53.

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Volchansky, Nadia. "CONSIDERATIONS FOR TEACHING DIGITAL PLATFORMS: SKILLS, THEORY, OR APPLICATION?" In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.0335.

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Wahyuningsih, Sapti, and Darmawan Satyananda. "The Level of Creative Thinking Skill in Graph Theory Application Course." In 2nd International Conference on Learning Innovation. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008411403030307.

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Reports on the topic "Application of learning theory"

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Kingsbury, Penelope. Analysis of Learning Organization Theories and their Application to Public Organizations. Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada364145.

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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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Babkin, Vladyslav V., Viktor V. Sharavara, Volodymyr V. Sharavara, Vladyslav V. Bilous, Andrei V. Voznyak, and Serhiy Ya Kharchenko. Using augmented reality in university education for future IT specialists: educational process and student research work. CEUR Workshop Proceedings, July 2021. http://dx.doi.org/10.31812/123456789/4632.

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The article substantiates the feature of using augmented reality (AR) in university training of future IT specialists in the learning process and in the research work of students. The survey of university teachers analyzed the most popular AR applications for training future IT specialists (AR Ruler, AR Physics, Nicola Tesla, Arloon Geometry, AR Geometry, GeoGebra 3D Graphing Calculator, etc.), disclose the main advantages of the applications. The methodological basis for the implementation of future IT specialists research activities towards the development and use of AR applications is substantiated. The content of the activities of the student’s scientific club “Informatics studios” of Borys Grinchenko Kyiv University is developed. Students as part of the scientific club activity updated the mobile application, and the model bank corresponding to the topics: “Polyhedrons” for 11th grade, as well as “Functions, their properties and graphs” for 10th grade. The expediency of using software tools to develop a mobile application (Android Studio, SDK, NDK, QR Generator, FTDS Dev, Google Sceneform, Poly) is substantiated. The content of the stages of development of a mobile application is presented. As a result of a survey of students and pupils the positive impact of AR on the learning process is established.
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Iatsyshyn, Anna V., Valeriia O. Kovach, Yevhen O. Romanenko, and Andrii V. Iatsyshyn. Cloud services application ways for preparation of future PhD. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3248.

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Currently, it is important in Ukraine to harmonize cloud technologies application with European and world scientific initiatives. Need to modernize preparation of future PhDs is caused by challenges of new information, globalized society and digital transformation of all spheres of life, including education and science. The research was aimed to determine features of cloud services application in preparation of future PhDs. Concepts of “cloud computing”, “cloud technologies”, “cloud learning technologies”, “cloud services”, “cloud oriented environment” were analyzed. Experience of cloud technologies and their services application in educational and scientific space in researches of foreign and Ukrainian students was considered. Ukrainian experience in preparation of future PhD of various specialties with cloud services application was analyzed. It was emphasized that approaches improving to preparation of future PhDs based on cloud services application would increase their level of digital competence. It is recommended to include a separate course or module of specific discipline on work with cloud technologies and services during preparation of future PhDs. It is important to improve disciplines and tools content to support education process. It can be learning of disciplines using cloud technologies or services by future PhD’s. Also, cloud services application to support scientific and scientific-organizational activities will increase level of organization and implementation of scientific research. It is important to create cloud-oriented environment for preparation of future PhDs in higher education and research institutions. Making cloud-oriented educational and scientific environment should be based on principles of open education. It is recommended to use cloud-based platforms and services (G Suite for Education; Microsoft Office 365; specialized SaaS (CoCalc or other)).
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Nosenko, Yuliya H., Maiia V. Popel, and Mariya P. Shyshkina. The state of the art and perspectives of using adaptive cloud-based learning systems in higher education pedagogical institutions (the scope of Ukraine). [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3246.

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The article deals with the problems of using adaptive cloud-based learning systems (ACLS) in the modern high-tech educational environment and expanding access to them as tools of educational and research activity at higher education pedagogical institutions in Ukraine. The conceptual apparatus of cloud-based adaptive learning systems application and design is considered; their main characteristics are revealed; the ways of their pedagogical application are described. The experience of Institute of Information Technologies and Learning Tools of NAES of Ukraine on designing and applying of the cloud-based learning and research environment is outlined. The results of the survey of 31 higher education pedagogical institutions on using ACLS are presented. It is established that in the near future ACLS will become the driving force behind the development of new pedagogy, new strategies for personalizing education, and expanding opportunities for active learning.
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental repairs. Dental arches in detachable prosthodontics may be categorized using Convolutional neural networks (CNN). By properly positioning the teeth, machine learning in CAD/CAM software can reestablish healthy inter-maxillary connections. AI may assist with accurate color matching in challenging cosmetic scenarios that include a single central incisor or many front teeth. Intraoral detectors can identify implant placements in implant prosthodontics and instantly input them into CAD software. The design and execution of dental implants could potentially be improved by utilizing AI.
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Iatsyshyn, Anna V., Valeriia O. Kovach, Volodymyr O. Lyubchak, Yurii O. Zuban, Andriy G. Piven, Oleksandra M. Sokolyuk, Andrii V. Iatsyshyn, Oleksandr O. Popov, Volodymyr O. Artemchuk, and Mariya P. Shyshkina. Application of augmented reality technologies for education projects preparation. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3856.

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After analysis of scientific literature, we defined that concept of “augmented reality” has following synonyms: “advanced reality”, “improved reality”, “enriched reality”, “mixed reality” and “hybrid reality”. Analysis of scientific literature and own practical experience of the use of augmented reality technologies application in educational practices allowed to state next: augmented reality technologies have a great potential for application in education; there are some cases of augmented reality use for school education; positive aspects of augmented reality technologies application in higher education institutions are confirmed by experiments (isolated cases); only few universities in Ukraine apply augmented reality technologies to educate students; only few universities in Ukraine have special subjects or modules in schedule to teach students to develop augmented reality technologies; various scientific events, mass events, competitions are held in Ukraine, and specialized training on the augmentation of augmented reality technologies is carried out, but this is non-systematic and does not have special state orientation and support. Features of introduction of virtual and augmented reality technologies at Sumy State University (Ukraine) are identified: “e-learning ecosystems” was created; in 2019, augmented and virtual reality research laboratory was established. Advantages and disadvantages of project activity in education are described: project activity is one of the most important components of educational process; it promotes creative self-development and self-realization of project implementers and forms various life competencies. It is determined that augmented reality application for implementation of educational projects will help: to increase students’ interest for educational material; formation of new competences; increase of students’ motivation for independent educational and cognitive activity; activation of educational activities; formation of positive motivation for personal and professional growth; conditions creation for development of personal qualities (creativity, teamwork, etc.). Current trends in implementation of educational projects were identified: most of the winner projects were implemented using augmented reality technology; augmented reality technologies were used in projects to teach different disciplines in higher education institutions. Augmented reality technology application for project activity has positive impact on learning outcomes and competitiveness of the national workforce; it will enhance the country’s position in the global economic space.
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Kramarenko, Tetiana H., Olha S. Pylypenko, and Vladimir I. Zaselskiy. Prospects of using the augmented reality application in STEM-based Mathematics teaching. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3753.

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The purpose of the study is improving the methodology of teaching Mathematics using cloud technologies and augmented reality, analyzing the peculiarities of the augmented reality technology implementing in the educational process. Attention is paid to the study of adaptation of Augmented Reality technology implementing in teaching mathematical disciplines for students. The task of the study is to identify the problems requiring theoretical and experimental solutions. The object of the study is the process of teaching Mathematics in higher and secondary education institutions. The subject of the study is augmented reality technology in STEM-based Mathematics learning. In the result of the study an overview of modern augmented reality tools and their application practices was carried out. The peculiarities of the mobile application 3D Calculator with Augmented reality of Dynamic Mathematics GeoGebra system usage in Mathematics teaching are revealed.
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MONAKO, T. P. THE PANDEMIC AND THE EDUCATION SYSTEM. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-2-2-79-84.

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The article deals with the issue of distance learning, which was sharply introduced into the education system during the pandemic. The application of distance learning to the entire education system has both positive and negative sides. At this point in time, as practice shows, there are much more negative sides.
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Kanivets, Oleksandr V., Irina М. Kanivets, Natalia V. Kononets, Tetyana М. Gorda, and Ekaterina O. Shmeltser. Development of mobile applications of augmented reality for projects with projection drawings. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3745.

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We conducted an analysis of the learning aids used in the study of general technical disciplines. This allowed us to draw an analogy between physical and virtual models and justify the development of a mobile application to perform tasks on a projection drawing. They showed a technique for creating mobile applications for augmented reality. The main stages of the development of an augmented reality application are shown: the development of virtual models, the establishment of the Unity3D game engine, the development of a mobile application, testing and demonstration of work. Particular attention is paid to the use of scripts to rotate and move virtual models. The in-house development of the augmented reality mobile application for accomplishing tasks on a projection drawing is presented. The created mobile application reads, recognizes marker drawings and displays the virtual model of the product on the screen of the mobile device. It has been established that the augmented reality program developed by the team of authors as a mobile pedagogical software can be used to perform tasks both with independent work of students and with the organization of classroom activities in higher education institutions.
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