Готові списки джерел за темами / Theory of applied learning of competencivism

Добірка наукової літератури з теми "Theory of applied learning of competencivism"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Theory of applied learning of competencivism"

Zhou, Ding-Xuan, Qiang Wu, and Yiming Ying. "Learning Theory." Abstract and Applied Analysis 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/138960.

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Costa, Roberto D., Gustavo F. Souza, Ricardo A. M. Valentim, and Thales B. Castro. "The theory of learning styles applied to distance learning." Cognitive Systems Research 64 (December 2020): 134–45. http://dx.doi.org/10.1016/j.cogsys.2020.08.004.

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van der Molen, Popko. "Reversal theory, learning and psychotherapy." British Journal of Guidance and Counselling 14, no. 2 (May 1, 1986): 125–39. http://dx.doi.org/10.1080/03069888600760141.

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van der Molen, Popko P. "Reversal Theory, Learning and Psychotherapy." British Journal of Guidance & Counselling 14, no. 2 (May 1986): 125–39. http://dx.doi.org/10.1080/03069888608253504.

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Jacobs, Robert A., and John K. Kruschke. "Bayesian learning theory applied to human cognition." Wiley Interdisciplinary Reviews: Cognitive Science 2, no. 1 (May 17, 2010): 8–21. http://dx.doi.org/10.1002/wcs.80.

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Lee, Jaemu, and Du-Gyu Kim. "Adaptive Learning System Applied Bruner’ EIS Theory." IERI Procedia 2 (2012): 794–801. http://dx.doi.org/10.1016/j.ieri.2012.06.173.

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RAKHLIN, ALEXANDER, SAYAN MUKHERJEE, and TOMASO POGGIO. "STABILITY RESULTS IN LEARNING THEORY." Analysis and Applications 03, no. 04 (October 2005): 397–417. http://dx.doi.org/10.1142/s0219530505000650.

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The problem of proving generalization bounds for the performance of learning algorithms can be formulated as a problem of bounding the bias and variance of estimators of the expected error. We show how various stability assumptions can be employed for this purpose. We provide a necessary and sufficient stability condition for bounding the bias and variance for the Empirical Risk Minimization algorithm, and various sufficient conditions for bounding bias and variance of estimators for general algorithms. We discuss settings in which it is possible to obtain exponential bounds, and we prove an extension of the bounded-difference inequality for "almost always" stable algorithms.

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Cornwell, John M., and Pamela A. Manfredo. "Kolb'S Learning Style Theory Revisited." Educational and Psychological Measurement 54, no. 2 (June 1994): 317–27. http://dx.doi.org/10.1177/0013164494054002006.

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Freitas, Elias J. R., Leonardo S. Prado, Marcos V. F. Silva, Vinícius A. Alvarenga, and Adrielle C. Santana. "Active Learning Strategy Applied to Control Theory Teaching." International Journal of Advanced Engineering Research and Science 9, no. 9 (2022): 001–8. http://dx.doi.org/10.22161/ijaers.99.1.

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Knouse, Stephen B. "Brand loyalty and sequential learning theory." Psychology and Marketing 3, no. 2 (1986): 87–98. http://dx.doi.org/10.1002/mar.4220030205.

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Дисертації з теми "Theory of applied learning of competencivism"

Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.

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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of predictive accuracy. Specifically, ML models perform 49-100\% better than unbiased methods. However, we cannot rely on parameter estimations. For example, Chapter 4 introduced a net prediction problem regarding fraudulent property claims in insurance. Despite the fact that we got extraordinary results in terms of predictive power, the complexity of the problem restricted us from getting behavioral insight. Contrarily, statistical models are easily interpretable. Coefficients give us the sign, the magnitude and the statistical significance. We can learn behavior from marginal impacts and elasticities. Chapter 5 analyzed another prediction problem in the insurance market, particularly, how the combination of self-reported data and risk categorization could improve the detection of risky potential customers in insurance markets. Results were also quite impressive in terms of prediction, but again, we did not know anything about the direction or the magnitude of the features. However, by using a Probit model, we showed the benefits of combining statistic models with ML-DL models. The Probit model let us get generalizable insights on what type of customers are likely to misreport, enhancing our results. Likewise, Chapter 2 is a clear example of how causal inference can benefit from ML and DL methods. These techniques allowed us to capture that 70 days before each auction there were abnormal behaviors in daily prices. By doing so, we could apply a solid statistical model and we could estimate precisely what the net effect of the mandated auctions in Spain was. This thesis aims at combining advantages of both methodologies, machine learning and econometrics, boosting their strengths and attenuating their weaknesses. Thus, we used ML and statistical methods side by side, exploring predictive performance and interpretability. Several conditions can be inferred from the nature of both approaches. First, as we have observed throughout the chapters, ML and traditional econometric approaches solve fundamentally different problems. We use ML and DL techniques to predict, not in terms of traditional forecast, but making our models generalizable to unseen data. On the other hand, traditional econometrics has been focused on causal inference and parameter estimation. Therefore, ML is not replacing traditional techniques, but rather complementing them. Second, ML methods focus in out-of-sample data instead of in-sample data, while statistical models typically focus on goodness-of-fit. It is then not surprising that ML techniques consistently outperformed traditional techniques in terms of predictive accuracy. The cost is then biased estimators. Third, the tradition in economics has been to choose a unique model based on theoretical principles and to fit the full dataset on it and, in consequence, obtaining unbiased estimators and their respective confidence intervals. On the other hand, ML relies on data driven selection models, and does not consider causal inference. Instead of manually choosing the covariates, the functional form is determined by the data. This also translates to the main weakness of ML, which is the lack of inference of the underlying data-generating process. I.e. we cannot derive economically meaningful conclusions from the coefficients. Focusing on out-of-sample performance comes at the expense of the ability to infer causal effects, due to the lack of standard errors on the coefficients. Therefore, predictors are typically biased, and estimators may not be normally distributed. Thus, we can conclude that in terms of out-sample performance it is hard to compete against ML models. However, ML cannot contend with the powerful insights that the causal inference analysis gives us, which allow us not only to get the most important variables and their magnitude but also the ability to understand economic behaviors.

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Zhang, Yue. "Sparsity in Image Processing and Machine Learning: Modeling, Computation and Theory." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523017795312546.

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Andersson, Carl. "Deep learning applied to system identification : A probabilistic approach." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397563.

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Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification. Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes. Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.

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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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Grieve, Susan M. "Cognitive Load Theory Principles Applied to Simulation Instructional Design for Novice Health Professional Learners." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/hpd_pt_stuetd/78.

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While the body of evidence supporting the use of simulation-based learning in the education of health professionals is growing, howor why simulation-based learning works is not yet understood. There is a clear need for evidence, grounded in contemporary educational theory, to clarify the features of simulation instructional design that optimize learning outcomes and efficiency in health care professional students. Cognitive Load Theory (CLT) is a theoretical framework focused on a learner’s working memory capacity. One principle of CLT is example based learning. While this principle has been applied in both traditional classroom and laboratory settings, and has shown positive performance and learning outcomes, example based learning has not yet been applied to the simulation setting. This study had two main objectives: to explore if the example-based learning principle could successfully be applied to the simulation learning environment, and to establish response process validation evidence for a tool designed to measure types of cognitive load. Fifty-eight novice students from nursing, podiatric medicine, physician assistant, physical and occupational therapy programs participated in a blinded randomized control study. The dependent variable was the simulation brief. Participants were randomly assigned to either a traditional brief or a facilitated tutored problem brief. Performance outcomes were measured with verbal communications skill presented in the Introduction, Situation, Background, Assessment, Recommendation (I-SBAR) format. Response process evidence was collected from cognitive interviews of 11 students. Results indicate participation in a tutored problem brief led to statistically significant differences at t(52)=-3.259, p=.002 in verbal communication performance compared to students who participated in a traditional brief. Effect size for this comparison was d=(6.06-4.61)/1.63 = .89 (95% CI 0.32-1.44). Response process evidence demonstrated that additional factors unique to the simulationlearning environment should be accounted for when measuring cognitive load in simulation based learning (SBL). This study suggests that example based learning principles can be successfully applied to SBL and result in positive performance outcomes for health professions students. Additionally, measures of cognitive load do not appear to capture all contribution toload imposed by the simulation environment.

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Chim, Tat-mei Alice, and 詹達美. "An instructional design theory guide for blended learning courses." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30406213.

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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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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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Youngleson, Penelope. "Flourishing in fragility: how to build antifragile ecosystems of learning, that nurture healthy vulnerability, in fragile environments in the Western Cape (South Africa) with at-risk learners." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/32352.

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This research is a qualitative, autoethnographic study of antifragility in fragile spaces. It was written using data from Applied Theatre workshops, rehearsals and exercises; as well as questionnaires, semi-structured interviews and open discussions in focus groups with at-risk learners from Quintile 1-3 high schools, their educators, senior management staff, parents, caregivers and peers. Methodologically, social constructionism functioned as the schematic map that positioned the writing/writer between the self and others, and provided the philosophical scaffolding necessary to elucidate data analysis and interpretation. Institutional theory and organisational culture centered the analytical framework once thematic analysis had been conducted across the data sets. This reflexive, feminist paper exhumes and explores fragile spaces in Western Cape Quintile 1-3 schools, using drama and conscious, performed acts of vulnerability (on and off stage) as a means of activating antifragility in the performer and the observer. The data collection took place in the Western Cape in South Africa, and specifically refers to learners and their networks and blended learning ecosystems in that context. Noted conversants include Brown, Taleb and Butler. The findings of this study include a shift in how we define “success” in a fragile environment and an acknowledgment of antifragility as a strategy that is always in motion. Static achievement and a singular definition of learner excellence are shown to be the undesirable opposite of iterative antifragility and adaptive, holistic executive function and socio-cultural competence; and learner wholeness (as experienced and embodied by the learner themselves) is referred to as “flourishing”.

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Opdenbosch, Patrick. "Auto-Calibration and Control Applied to Electro-Hydraulic Poppet Valves." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19758.

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Modern control design is sometimes accompanied by the challenge of dealing with nonlinear systems or plants. In some situations, due to the complexity of the plant and the unavailability of suitable models, the controls engineer opts for developing control schemes based on look-up tables. These tables, typically populated with the steady state inverse input-output characteristics of the plant, are used to compensate the plant via open-loop or closed-loop to solve the control problem. In an effort to present a new alternative, a general theoretical framework for online auto-calibration and control of general nonlinear systems is developed in this dissertation. This technique simultaneously learns the inverse input-state mapping (i.e. the calibration mapping) of the plant while forcing its state to follow a prescribed desired trajectory. The main requirements for the successful application of the novel control law are knowledge of the order of the plant and some generic data to initialize the inverse mapping. This last requirement can be easily fulfilled by using steady-state data or the equilibrium points of the plant. In this approach, the inverse mapping is learned from the current and past states. The learning is accomplished in a composite manner by employing input and state errors. The map is used simultaneously in the feedforward path to control the plant. The performance of the plant subject to this novel controller is validated through simulations and experimental data. The new control method is applied to a novel Electro-Hydraulic Poppet Valve (EHPV). These valves are used in a Wheatstone bridge arrangement for motion control of hydraulic actuators. This is preferred over the conventional use of spool valves due to the energy savings potential. It is shown in this dissertation that this method improves the value of using these types of valves for motion control in hydraulics. This is due to the combination of self-learning (auto-calibration) and better performance for a more efficient operation of hydraulic equipment. Additionally, it is shown that the auto-calibration of the valves can be used for health monitoring of the same, which consequently improves their reliability and expedites maintenance downtime.

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Книги з теми "Theory of applied learning of competencivism"

Railean, Elena. Handbook of research on applied learning theory and design in modern education. Hershey PA: Information Science Reference, 2016.

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E, Hunt David. Beginning with ourselves: In practice, theory, and human affairs. Cambridge, MA: Brookline Books, 1987.

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Bednyĭ, G. Z. The Russian theory of activity: Current applications to design and learning. Mahwah, N.J: Lawrence Erlbaum Associates, 1997.

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Fay, Fransella, and Thomas Laurie F, eds. Experimenting with personal construct psychology. London: Routledge & Kegan Paul, 1988.

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University of North London. Faculty of Environmental and Social Studies. BSc Applied social science SP 301 Theory and practice of organisations: Learning resource pack and study guide. [London]: University of North London, 1993.

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Ashwin, Ram, and Leake David B, eds. Goal-driven learning. Cambridge, Mass: MIT Press, 1995.

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Cziko, Gary. Without miracles: Universal selection theory and the second Darwinian revolution. Cambridge, Mass: MIT Press, 1995.

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APPLIED LEARNING THEORY: STUDENT RESOURCE MANUAL: Student Resource Manual. Kendall-Hunt, 2003.

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Understanding Applied Learning: Theory and Practice for Teachers and Lecturers. Taylor & Francis Group, 2017.

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Blandford, Sonia, and Tanya Ovenden-Hope. Understanding Applied Learning: Theory and Practice for Teachers and Lecturers. Taylor & Francis Group, 2017.

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Forsyth, David. "A Little Learning Theory." In Applied Machine Learning, 49–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_3.

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Myles, Florence. "Building a Comprehensive Second Language Acquisition Theory." In Conceptualising 'Learning' in Applied Linguistics, 225–39. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230289772_13.

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Larsen-Freeman, Diane. "Having and Doing: Learning from a Complexity Theory Perspective." In Conceptualising 'Learning' in Applied Linguistics, 52–68. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230289772_4.

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van den Dobbelsteen, John J., Mustafa Karahan, and Umut Akgün. "Theory on Psychomotor Learning Applied to Arthroscopy." In Effective Training of Arthroscopic Skills, 17–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44943-1_3.

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Pienemann, Manfred. "A Cognitive View of Language Acquisition: Processability Theory and Beyond." In Conceptualising 'Learning' in Applied Linguistics, 69–88. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230289772_5.

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Schuster, Alfons. "Using Chaos Theory for the Genetic Learning of Fuzzy Controllers." In Innovations in Applied Artificial Intelligence, 382–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24677-0_40.

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Borda, Monica, Romulus Terebes, Raul Malutan, Ioana Ilea, Mihaela Cislariu, Andreia Miclea, and Stefania Barburiceanu. "Supervised Deep Learning Classification Algorithms." In Randomness and Elements of Decision Theory Applied to Signals, 205–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90314-5_15.

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Zambrano R., Jimmy, Paul A. Kirschner, and Femke Kirschner. "How cognitive load theory can be applied to collaborative learning." In Advances in Cognitive Load Theory, 30–39. Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780429283895-3.

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Noriega, A., J. M. Sierra, J. L. Cortizo, M. J. Prieto, F. F. Linera, and J. A. Martín. "Project-Based Learning Applied to Mechatronics Teaching." In New Trends in Educational Activity in the Field of Mechanism and Machine Theory, 49–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00108-7_6.

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Murillo-Olmos, Jesus, Erick Rodríguez-Esparza, Marco Pérez-Cisneros, Daniel Zaldivar, Erik Cuevas, Gerardo Trejo-Caballero, and Angel A. Juan. "Thresholding Algorithm Applied to Chest X-Ray Images with Pneumonia." In Metaheuristics in Machine Learning: Theory and Applications, 359–407. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70542-8_16.

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Li, Jinci. "Wireless, amphibious theory for reinforcement learning." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825527.

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Unda, Xavier L., and Valentina Ramos. "EXPECTANCY THEORY APPLIED TO AN EDUCATIONAL CONTEXT: A LONGITUDINAL STUDY APPLIED IN POSTGRADUATE COURSES." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.2027.

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Yu, Shu-Yin. "Research on the Learning Effect of Experiential Learning Theory Applied to Design Education." In The European Conference on Education 2022. The International Academic Forum(IAFOR), 2022. http://dx.doi.org/10.22492/issn.2188-1162.2022.38.

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Kewo, Cecilia Lelly, and Ventje Senduk. "Method of Problem Based Learning of Learning in Course Theory on Soft Skills Competence of Students." In First International Conference on Applied Science and Technology (iCAST 2018). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200813.032.

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Lopes, Leonardo, Lucas Valem, Daniel Pedronette, Ivan Guilherme, João Papa, Marcos Santana, and Danilo Colombo. "Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos." In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008974604040412.

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Sitthisak, Onjira, Lester Gilbert, and Dietrich Albert. "Adaptive Learning Using an Integration of Competence Model with Knowledge Space Theory." In 2013 IIAI International Conference on Advanced Applied Informatics (IIAIAAI). IEEE, 2013. http://dx.doi.org/10.1109/iiai-aai.2013.15.

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Jung, Hyun, Christian Suloway, Tianyi Miao, Elijah F. Edmondson, David R. Morcock, Claire Deleage, Yanling Liu, Jack R. Collins, and Curtis Lisle. "Integration of Deep Learning and Graph Theory for Analyzing Histopathology Whole-slide Images." In 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2018. http://dx.doi.org/10.1109/aipr.2018.8707424.

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Mou, Liqiang, Ziwen Wang, and Qin Gu. "Research on Some Key Technologies of Wireless Sensor Networks Based on Optimization Theory." In 2020 2nd International Conference on Applied Machine Learning (ICAML). IEEE, 2020. http://dx.doi.org/10.1109/icaml51583.2020.00046.

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Morrison, G. R. "Binomial Probability Theory Supporting a Learning System Applied to Exploration Decision Making." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/186972-ms.

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Cichy, Blazej, Krzysztof Galkowski, Eric Rogers, and Anton Kummert. "2D systems theory applied to iterative learning control of spatio-temporal dynamics." In Control (MSC). IEEE, 2010. http://dx.doi.org/10.1109/cca.2010.5611269.

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Звіти організацій з теми "Theory of applied learning of competencivism"

BAGIYAN, A., and A. VARTANOV. SYSTEMS ACQUISITION IN MULTILINGUAL EDUCATION: THE CASE OF AXIOLOGICALLY CHARGED LEXIS. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2077-1770-2021-13-4-3-48-61.

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Анотація:

The process of mastering, systematizing and automatizing systems language skills occupies a key place in the theory and practice of teaching foreign languages and cultures. Following the main trends of modern applied linguistics in the field of multilingual research, we hypothesize the advisability of using the lexical approach in mastering the entire complex of systems skills (grammar, vocabulary, phonology, functions, discourse) in students receiving multilingual education at higher educational institutions. In order to theoretically substantiate the hypothesis, the authors carry out structural, semantic, and phonological analysis of the main lexical units (collocations). After this, linguodidactic analysis of students’ hypothetical problems and, as a result, problems related to the teaching of relevant linguistic and axiological features is carried out. At the final stage of the paper, a list of possible outcomes from the indicated linguistic and methodological problematic situations is given. This article is the first in the cycle of linguodidactic studies of the features of learning and teaching systems language skills in a multilingual educational space.

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