Relevant bibliographies by topics / Importance of learning

Contents

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

Academic literature on the topic 'Importance of learning'

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

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Journal articles on the topic "Importance of learning"

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TIMPAU, Cristina. "Importance of Self-Directed Learning." Logos Universality Mentality Education Novelty. Section: SOCIAL SCIENCES 04, no. 01 (June 30, 2015): 37–49. http://dx.doi.org/10.18662/lumenss.2015.0401.03.

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Nishanthi, Rajathurai. "The Importance of Learning English in Today World." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 871–74. http://dx.doi.org/10.31142/ijtsrd19061.

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Kydyrmoldina, M., and Zh Nurzhanova. "The Importance of Learning and Teaching English Language." Bulletin of L.N. Gumilyov Eurasian National University. Philology Series 123, no. 2 (2018): 105–8. http://dx.doi.org/10.32523/2616-678x-2018-123-2-105-108.

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Knopf, Jeffrey W. "The importance of international learning." Review of International Studies 29, no. 2 (April 2003): 185–207. http://dx.doi.org/10.1017/s0260210503001852.

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A programme of research on learning in international relations began developing in the 1980s. However, learning research has not realised its potential. This article seeks to stimulate new work on learning by analysing why learning is important in international relations and outlining a research focus that reflects this assessment of learning's significance. The research so far has mostly treated learning as a foreign policy phenomenon, but this fails to capture one of the major reasons for interest in learning. Learning matters in part because of long-standing debates about whether it is possible to make progress in reducing the amount of armed conflict in world politics. For such progress to occur, it is likely that some form of learning would have to take place. However, learning by just a single state will often not be sufficient to change the quality of international outcomes. There thus needs to be research specifically on the possibility of shared learning by two or more states, a research focus this article will label ‘international learning’. A few illustrative examples will demonstrate the feasibility of doing research on shared, cross-national learning.
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Yoder-Wise, Patricia S. "The Importance of Learning." Journal of Continuing Education in Nursing 46, no. 5 (May 1, 2015): 195–96. http://dx.doi.org/10.3928/00220124-20150420-10.

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Anarbaeva, Fotima. "THE IMPORTANCE OF ELECTRONIC EDUCATION METHODOLOGY." CURRENT RESEARCH JOURNAL OF PEDAGOGICS 02, no. 06 (June 30, 2021): 127–30. http://dx.doi.org/10.37547/pedagogics-crjp-02-06-24.

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E-learning is based on the use of multimedia and Internet technologies to improve the quality and opportunities of education using an innovative approach to education. With this in mind, this article discusses the specifics of e-learning and traditional education, the research of foreign scholars on e-learning methods, the importance of e-learning methods and Sergeev S.F., Nabi Y.A., Ibraimov A.E. such as the research of scientists in the field of e-learning methodology.
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Bansal, Neeta, and Parmod Kumar. "E-Learning- Importance and Challenges." Shikshan Anveshika 5, no. 2 (2015): 93. http://dx.doi.org/10.5958/2348-7534.2015.00018.5.

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O'Hear, Anthony. "The importance of traditional learning." British Journal of Educational Studies 35, no. 2 (June 1987): 102–14. http://dx.doi.org/10.1080/00071005.1987.9973755.

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Tripathi, Ramna. "Importance and Improvements in Teaching-Learning process through Effective Evaluation Methodologies." ESSENCE International Journal for Environmental Rehabilitation and Conservation 9, no. 2 (December 31, 2018): 7–16. http://dx.doi.org/10.31786/09756272.18.9.2.202.

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Tangirov, Khurram Ergashevich, Dustmurod Mamasolievich Jomurodov, and Shodiya Kholmamatovna Murodkasimova. "The importance of e-learning and e-learning resources in individualized learning." Asian Journal of Multidimensional Research (AJMR) 10, no. 3 (2021): 464–69. http://dx.doi.org/10.5958/2278-4853.2021.00176.2.

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Dissertations / Theses on the topic "Importance of learning"

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Fang, Tongtong. "Learning from noisy labelsby importance reweighting: : a deep learning approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264125.

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Noisy labels could cause severe degradation to the classification performance. Especially for deep neural networks, noisy labels can be memorized and lead to poor generalization. Recently label noise robust deep learning has outperformed traditional shallow learning approaches in handling complex input data without prior knowledge of label noise generation. Learning from noisy labels by importance reweighting is well-studied. Existing work in this line using deep learning failed to provide reasonable importance reweighting criterion and thus got undesirable experimental performances. Targeting this knowledge gap and inspired by domain adaptation, we propose a novel label noise robust deep learning approach by importance reweighting. Noisy labeled training examples are weighted by minimizing the maximum mean discrepancy between the loss distributions of noisy labeled and clean labeled data. In experiments, the proposed approach outperforms other baselines. Results show a vast research potential of applying domain adaptation in label noise problem by bridging the two areas. Moreover, the proposed approach potentially motivate other interesting problems in domain adaptation by enabling importance reweighting to be used in deep learning.
Felaktiga annoteringar kan sänka klassificeringsprestanda.Speciellt för djupa nätverk kan detta leda till dålig generalisering. Nyligen har brusrobust djup inlärning överträffat andra inlärningsmetoder när det gäller hantering av komplexa indata Befintligta resultat från djup inlärning kan dock inte tillhandahålla rimliga viktomfördelningskriterier. För att hantera detta kunskapsgap och inspirerat av domänanpassning föreslår vi en ny robust djup inlärningsmetod som använder omviktning. Omviktningen görs genom att minimera den maximala medelavvikelsen mellan förlustfördelningen av felmärkta och korrekt märkta data. I experiment slår den föreslagna metoden andra metoder. Resultaten visar en stor forskningspotential för att tillämpa domänanpassning. Dessutom motiverar den föreslagna metoden undersökningar av andra intressanta problem inom domänanpassning genom att möjliggöra smarta omviktningar.
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Zhu, Ying. "PREFERENCES: OPTIMIZATION, IMPORTANCE LEARNING AND STRATEGIC BEHAVIORS." UKnowledge, 2016. http://uknowledge.uky.edu/cs_etds/46.

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Preferences are fundamental to decision making and play an important role in artificial intelligence. Our research focuses on three group of problems based on the preference formalism Answer Set Optimization (ASO): preference aggregation problems such as computing optimal (near optimal) solutions, strategic behaviors in preference representation, and learning ranks (weights) for preferences. In the first group of problems, of interest are optimal outcomes, that is, outcomes that are optimal with respect to the preorder defined by the preference rules. In this work, we consider computational problems concerning optimal outcomes. We propose, implement and study methods to compute an optimal outcome; to compute another optimal outcome once the first one is found; to compute an optimal outcome that is similar to (or, dissimilar from) a given candidate outcome; and to compute a set of optimal answer sets each significantly different from the others. For the decision version of several of these problems we establish their computational complexity. For the second topic, the strategic behaviors such as manipulation and bribery have received much attention from the social choice community. We study these concepts for preference formalisms that identify a set of optimal outcomes rather than a single winning outcome, the case common to social choice. Such preference formalisms are of interest in the context of combinatorial domains, where preference representations are only approximations to true preferences, and seeking a single optimal outcome runs a risk of missing the one which is optimal with respect to the actual preferences. In this work, we assume that preferences may be ranked (differ in importance), and we use the Pareto principle adjusted to the case of ranked preferences as the preference aggregation rule. For two important classes of preferences, representing the extreme ends of the spectrum, we provide characterizations of situations when manipulation and bribery is possible, and establish the complexity of the problem to decide that. Finally, we study the problem of learning the importance of individual preferences in preference profiles aggregated by the ranked Pareto rule or positional scoring rules. We provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decided all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples is NP-hard. We obtain similar results for the case of weighted profiles.
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Shelton, Christian R. (Christian Robert) 1975. "Importance sampling for reinforcement learning with multiple objectives." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86774.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 115-118).
This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find the sparsity of data, the partial observability of the domain, and the multiple objectives of the agent to cause serious problems for existing reinforcement learning algorithms. We employ importance sampling (likelihood ratios) to achieve good performance in partially observable Nlarkov decision processes with few data. Our importance sampling estimator requires no knowledge about the environment and places few restrictions on the method of collecting data. It can be used efficiently with reactive controllers, finite-state controllers, or policies with function approximation. We present theoretical analyses of the estimator and incorporate it into a reinforcement learning algorithm. Additionally, this method provides a complete return surface which can be used to balance multiple objectives dynamically. We demonstrate the need for multiple goals in a variety of applications and natural solutions based on our sampling method. The thesis concludes with example results from employing our algorithm to the domain of automated electronic market-making.
by Christian Robert Shelton.
Ph.D.
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Schwarb, Hillary. "The importance of stimulus-response rules in sequence learning." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28221.

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Tran, Van-Tinh. "Selection Bias Correction in Supervised Learning with Importance Weight." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1118/document.

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Dans la théorie de l'apprentissage supervisé, l'hypothèse selon laquelle l'échantillon de d'apprentissage et de test proviennent de la même distribution de probabilité, joue un rôle crucial. Malheureusement, cette hypothèse essentielle est souvent violée en présence d'un biais de sélection. Dans ce contexte, les algorithmes d'apprentissage supervisés standards peuvent souffrir d'un biais significatif. Dans cette thèse, nous abordons le problème du biais de sélection en apprentissage supervisé en utilisant la méthode de pondération de l'importance ("importance weighting" en anglais).Dans un premier temps, nous présentons le cadre formel de l'apprentissage supervisé et discutons des effets potentiellement néfastes du biais sur les performances prédictives. Nous étudions ensuite en détail comment les techniques de pondération de l'importance permettent, sous certaines hypothèses, de corriger le biais de sélection durant l'apprentissage de modèles génératifs et discriminants. Nous étudions enfin le potentiel des réseaux bayésiens comme outils de représentation graphique des relations d'indépendances conditionnelles entre les variables du problème et celles liées au mécanisme de sélection lui-même. Nous illustrons sur des exemples simples comment la graphe, construit avec de la connaissance experte, permet d'identifier a posteriori un sous-ensemble restreint de variables sur lesquelles « agir » pour réduire le biais.Dans un second temps, nous accordons une attention particulière au « covariate shift », i.e. un cas particulier de biais de sélection où la distribution conditionnelle P(y|x) est invariante entre l'échantillon d'apprentissage et de test. Nous proposons deux méthodes pour améliorer la pondération de l'importance en présence de covariate shift. Nous montrons d'abord que le modèle non pondéré est localement moins biaisé que le modèle pondéré sur les échantillons faiblement pondérés, puis nous proposons une première méthode combinant les modèles pondérés et non pondérés afin d'améliorer les performances prédictives dans le domaine cible. Enfin, nous étudions la relation entre le covariate shift et le problème des données manquantes dans les jeux de données de petite taille et proposons une seconde méthode qui utilise des techniques d'imputation de données manquantes pour corriger le covariate shift dans des scénarios simples mais réalistes. Ces méthodes sont validées expérimentalement sur de nombreux jeux de données
In the theory of supervised learning, the identical assumption, i.e. the training and test samples are drawn from the same probability distribution, plays a crucial role. Unfortunately, this essential assumption is often violated in the presence of selection bias. Under such condition, the standard supervised learning frameworks may suffer a significant bias. In this thesis, we address the problem of selection bias in supervised learning using the importance weighting method. We first introduce the supervised learning frameworks and discuss the importance of the identical assumption. We then study the importance weighting framework for generative and discriminative learning under a general selection scheme and investigate the potential of Bayesian Network to encode the researcher's a priori assumption about the relationships between the variables, including the selection variable, and to infer the independence and conditional independence relationships that allow selection bias to be corrected.We pay special attention to covariate shift, i.e. a special class of selection bias where the conditional distribution P(y|x) of the training and test data are the same. We propose two methods to improve importance weighting for covariate shift. We first show that the unweighted model is locally less biased than the weighted one on low importance instances, and then propose a method combining the weighted and the unweighted models in order to improve the predictive performance in the target domain. Finally, we investigate the relationship between covariate shift and the missing data problem for data sets with small sample sizes and study a method that uses missing data imputation techniques to correct the covariate shift in simple but realistic scenarios
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Westermark, Hanna. "Deep Learning with Importance Sampling for Brain Tumor MR Segmentation." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289574.

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Segmentation of magnetic resonance images is an important part of planning radiotherapy treat-ments for patients with brain tumours but due to the number of images contained within a scan and the level of detail required, manual segmentation is a time consuming task. Convolutional neural networks have been proposed as tools for automated segmentation and shown promising results. However, the data sets used for training these deep learning models are often imbalanced and contain data that does not contribute to the performance of the model. By carefully selecting which data to train on, there is potential to both speed up the training and increase the network’s ability to detect tumours. This thesis implements the method of importance sampling for training a convolutional neural network for patch-based segmentation of three dimensional multimodal magnetic resonance images of the brain and compares it with the standard way of sampling in terms of network performance and training time. Training is done for two different patch sizes. Features of the most frequently sampled volumes are also analysed. Importance sampling is found to speed up training in terms of number of epochs and also yield models with improved performance. Analysis of the sampling trends indicate that when patches are large, small tumours are somewhat frequently trained on, however more investigation is needed to confirm what features may influence the sampling frequency of a patch.
Segmentering av magnetröntgenbilder är en viktig del i planeringen av strålbehandling av patienter med hjärntumörer. Det höga antalet bilder och den nödvändiga precisionsnivån gör dock manuellsegmentering till en tidskrävande uppgift. Faltningsnätverk har därför föreslagits som ett verktyg förautomatiserad segmentering och visat lovande resultat. Datamängderna som används för att träna dessa djupinlärningsmodeller är ofta obalanserade och innehåller data som inte bidrar till modellensprestanda. Det finns därför potential att både skynda på träningen och förbättra nätverkets förmåga att segmentera tumörer genom att noggrant välja vilken data som används för träning. Denna uppsats implementerar importance sampling för att träna ett faltningsnätverk för patch-baserad segmentering av tredimensionella multimodala magnetröntgenbilder av hjärnan. Modellensträningstid och prestanda jämförs mot ett nätverk tränat med standardmetoden. Detta görs förtvå olika storlekar på patches. Egenskaperna hos de mest valda volymerna analyseras också. Importance sampling uppvisar en snabbare träningsprocess med avseende på antal epoker och resulterar också i modeller med högre prestanda. Analys av de oftast valda volymerna indikerar att under träning med stora patches förekommer små tumörer i en något högre utsträckning. Vidareundersökningar är dock nödvändiga för att bekräfta vilka aspekter som påverkar hur ofta en volym används.
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Farahani, Gohar Omidvar. "Existence and Importance of Online Interaction." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/27169.

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This research explored the existence and importance of interaction in online courses as perceived by online learners and instructors. The study was based on data from online students and instructors in the fall 2002 semester at Mid-Atlantic Community College(1). Two web-based surveys were used to collect data. Eighty-eight of 267 online students completed the survey, for a response rate of 33%. The study was based on constructivist theory which suggested that students learn by actively participating in the learning process through interaction with the instructor, other students, and course materials. This interaction was measured by different online interaction modalities and a five-step interactivity model developed by Salmon. This model suggested that the intensity of interactivity involves five steps: access and motivation, online socialization, information exchange, knowledge construction, and development. In addition, student characteristics (age and gender) and pedagogical variables (online experience and learning preferences) were included. Findings of the survey revealed that students perceived a moderate to high level of availability in a majority of the interactivity modalities. The highest interaction was reported between students and instructor through email communication and feedback on students? work by instructors. In addition, student ratings of the availability of different interaction modalities in online instruction were correlated with their perceptions of the importance of these modalities. Students reported satisfaction with the level of interactivity in their online courses. In contrast, responses to Salmon?s model revealed a high level of unavailability of the various interactivity criteria. The result of instructor survey, based on 13 responses, revealed that online instructors perceive interaction with students through email communication and providing feedback on their work were important. They did not perceive many of the interactivity criteria introduced by this research to be important. Therefore, they reported these criteria as unavailable in their online courses. This study is important because the extent of systematic research on availability and importance of online interaction is limited. (1)- To preserve the anonymity of respondents, this name is a pseudonym.
Ph. D.
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Barbeite, Francisco. "Importance of learning and development opportunity to job choice decisions." Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/28567.

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Mathialagan, Clint Solomon. "VIP: Finding Important People in Images." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/53706.

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People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This work analyzes the concept of the importance of individuals in group photographs. We address two specific questions - Given an image, who are the most important individuals in it? Given multiple images of a person, which image depicts the person in the most important role? We introduce a measure of importance of people in images and investigate the correlation between importance and visual saliency. We find that not only can we automatically predict the importance of people from purely visual cues, incorporating this predicted importance results in significant improvement in applications such as im2text (generating sentences that describe images of groups of people).
Master of Science
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Taylor, Teresa, Jamie Branam Kridler, and Mary Langenbrunner. "Importance of Community Connections: Strategies for Intervention & Prevention." Digital Commons@Georgia Southern, 2016. https://digitalcommons.georgiasouthern.edu/nyar_savannah/2016/2016/84.

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Community connections play a vital role in strategies for intervention and prevention. An interactive presentation will focus on successful collaborations involving holistic approaches, service-learning and a comparison and contrast of communities (East Tennessee and the LA Watts District).
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Books on the topic "Importance of learning"

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Arts, Royal Society of, ed. Start right: The importance of early learning. London: Royal Society of Arts, 1994.

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Goldstein, David K. Computer-based data and organizational learning: The importance of managers' stories. Cambridge, Mass: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology, 1992.

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Agony in education: The importance of struggle in the process of learning. Westport, Conn: Bergin & Garvey, 1994.

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Speaking to think, thinking to speak: The importance of talk in the learning process. Portsmouth, NH: Boynton/Cook Publishers, 1995.

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McGuinness, Catherine M. Towards parent partnership: A consideration of the importance, relevance and complexity of parental involvement in children's learning. [s.l: The author], 1999.

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Banks, Carys. Analysing Ethnographic Research Methods: The Importance of Retaining Immersion During the Analytic Process Using Research From UK-Based Learning Disability Social Care Settings. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526495990.

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Forum on early learning: Investing in our children, investing in our future : hearing of the Committee on Health, Education, Labor, and Pensions, United States Senate, One Hundred Seventh Congress, second session on examining the quality of early childhood learning programs, focusing on the importance of early childhood cognitive development, January 24, 2002. Washington: U.S. G.P.O., 2002.

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author, Rutherford Amber C., and Marzano Robert J. author, eds. Identifying critical content: Classroom techniques to help students know what is important. West Palm Beach, Florida: Learning Sciences International, 2014.

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Clerides, Sofronis. Is "learning-by-exporting" important?: Micro-dynamic evidence from Colombia, Mexico and Morocco. Cambridge, MA: National Bureau of Economic Research, 1996.

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Halstead, Lester M. The search for and discovery of self: The most fascinating and important person you will ever meet. [Salt Lake City, UT]: Agreka Books, 1999.

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Book chapters on the topic "Importance of learning"

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Stanley, Todd. "The Importance of 21st-Century Skills." In Authentic Learning, 17–28. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003233152-3.

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Poortman, Cindy L., and Chris Brown. "The importance of Professional Learning Networks." In Networks For Learning, 10–19. Abingdon, Oxon; New York, NY: Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315276649-2.

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Collins, Megan. "Learning the importance of falseness." In The New Narcissus in the Age of Reality Television, 15–35. Names: Collins, Megan, author.Title: The new narcissus in the age of reality television / Megan Collins.Description: 1 Edition. | New York : Routledge, 2017. | Series: Classical and contemporary social theory: Routledge, 2017. http://dx.doi.org/10.4324/9781315463490-2.

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Li, Lihong, Vadim Bulitko, and Russell Greiner. "Batch Reinforcement Learning with State Importance." In Machine Learning: ECML 2004, 566–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_53.

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Hohenstein, Jill, and Theano Moussouri. "The importance of methods and methodology for museum practice." In Museum Learning, 31–51. Abingdon, Oxon ; New York, NY : Routhledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315696447-3.

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Diesendruck, Maurice, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, and Sinead A. Williamson. "Importance Weighted Generative Networks." In Machine Learning and Knowledge Discovery in Databases, 249–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46147-8_15.

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Udell, Chad. "The Importance of Curation for Successful Mobile Experiences." In Mastering Mobile Learning, 205–8. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119036883.ch31.

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Beslmeisl, Magdalena, Rebecca Reuter, and Jürgen Mottok. "The Importance of Writing in Software Engineering Education." In Interactive Collaborative Learning, 315–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50337-0_29.

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Frick, Theodore W. "Importance of Educology for Improving Education Systems." In Learning, Design, and Technology, 1–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-17727-4_92-1.

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Sanz-González, José L., and Diego Andina. "Importance Sampling Techniques in Neural Detector Training." In Machine Learning: ECML 2001, 431–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44795-4_37.

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Conference papers on the topic "Importance of learning"

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Wang, Shuaiqiang, Xiaoming Xi, and Yilong Yin. "Importance weighted passive learning." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398611.

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Beygelzimer, Alina, Sanjoy Dasgupta, and John Langford. "Importance weighted active learning." In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553381.

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Nodet, Pierre, Vincent Lemaire, Alexis Bondu, Antoine Cornuejols, and Adam Ouorou. "Importance Reweighting for Biquality Learning." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533349.

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Cridlin, Leah D. "The importance of hands-on learning." In ILSC® 2007: Proceedings of the International Laser Safety Conference. Laser Institute of America, 2007. http://dx.doi.org/10.2351/1.5056625.

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Sagawa, Miyako, Hernan Aguirre, Fabio Daolio, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, and Kiyoshi Tanaka. "Learning variable importance to guide recombination." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850229.

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Gerasimou, Simos, Hasan Ferit Eniser, Alper Sen, and Alper Cakan. "Importance-driven deep learning system testing." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377811.3380391.

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Gerasimou, Simos, Hasan Ferit Eniser, Alper Sen, and Alper Cakan. "Importance-driven deep learning system testing." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377812.3390793.

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Rizk, Elsa, Stefan Vlaski, and Ali H. Sayed. "Optimal Importance Sampling for Federated Learning." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413655.

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Loog, Marco. "Nearest neighbor-based importance weighting." In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349714.

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de-Paz, M. Violante, Ricardo Lucas, Elsa Galbis, and Nieves Iglesias. "BILINGUALISM: THE IMPORTANCE OF SPEAKING LANGUAGES." In 10th International Conference on Education and New Learning Technologies. IATED, 2018. http://dx.doi.org/10.21125/edulearn.2018.1689.

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Reports on the topic "Importance of learning"

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Lee, JuYoung, and Jihyeong Son. Importance of Exploratory Writing in Critical Thinking and Learning. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-81.

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Tremaine, Robert L., and Donna J. Seligman. Learning Organizations: Their Importance to Systems Acquisition in DoD. Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada624742.

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Jackson, C. Kirabo, and Elias Bruegmann. Teaching Students and Teaching Each Other: The Importance of Peer Learning for Teachers. Cambridge, MA: National Bureau of Economic Research, August 2009. http://dx.doi.org/10.3386/w15202.

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Yorke, Louise, Pauline Rose, Stephen Bayley, Darge Wole Meshesha, and Paul Ramchandani. The Importance of Students’ Socio-Emotional Learning, Mental Health, and Wellbeing in the Time of COVID-19. Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-ri_2021/025.

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In this policy brief, we set out the importance of focusing on students’ socio-emotional learning, especially in the context of the ongoing COVID-19 pandemic. We first consider the role of socio-emotional learning in students’ education and development and also their mental health and wellbeing, and then identify specific areas that we suggest have particular importance in supporting students’ education and development during and beyond the COVID-19 pandemic.
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Cilliers, Jacobus, Eric Dunford, and James Habyarimana. What Do Local Government Education Managers Do to Boost Learning Outcomes? Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/064.

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Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of performance are: i) the frequency of school visits; ii) school and teacher incentives administered by the district manager; and iii) performance review of staff. Although the model is not causal, these findings suggest the importance of robust systems to motivate district staff, schools, and teachers, that include frequent monitoring of schools. They also show the importance of surveying subordinates of managers, in order to produce richer information on management practices.
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Bayley, Stephen, Darge Wole, Louise Yorke, Paul Ramchandani, and Pauline Rose. Researching Socio-Emotional Learning, Mental Health and Wellbeing: Methodological Issues in Low-Income Contexts. Research on Improving Systems of Education (RISE), April 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/068.

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This paper explores methodological issues relating to research on children’s socio-emotional learning (SEL), mental health and wellbeing in low- and lower-middle-income countries. In particular, it examines the key considerations and challenges that researchers may face and provides practical guidance for generating reliable and valid data on SEL, mental health and wellbeing in diverse settings and different cultural contexts. In so doing, the paper draws on the experience of recent research undertaken in Ethiopia to illustrate some of the issues and how they were addressed. The present study extends earlier 2018-2019 RISE Ethiopia research, expanding its scope to consider further aspects of SEL, mental health and wellbeing in the particular context of COVID-19. In particular, the research highlights that the pandemic has brought to the fore the importance of assessing learning, and learning loss, beyond academic learning alone.
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Bhattacharjea, Suman, Sehar Saeed, Rajib Timalsina, and Syeed Ahamed. Citizen-led Assessments: A Model for Evidence-based Advocacy and Action to Improve Learning. Australian Council for Educational Research, June 2021. http://dx.doi.org/10.37517/978-1-74286-636-9.

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Designed as household-based assessments, citizen-led assessments (CLAs) are implemented by local organizations who assess children in their homes, thus reaching the most marginalized children, families, and communities, often in remote areas. CLAs add an essential piece of information for truly monitoring progress and help realistically represent the learning levels of all children – at national, regional, and global levels. By using simple tools and easy-to-understand reports, CLAs engage parents and community members in discussions about learning and help foster understanding of the importance of ensuring quality education through civil action. In this publication, members from organizations conducting CLAs in India, Pakistan, Nepal and Bangladesh provide an overview of the CLA model and illustrate a range of ways in which the model has been implemented in the four South Asian countries to monitor and improve learning. In all four countries, the initiative is known as the Annual Status of Education Report, or ASER – a word that means ‘impact’ in three of these four countries. By design, ASER assesses foundational reading and numeracy skills.
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Higdon, Grace Lyn. Nested Theories of Change for Adaptive Rigour. Institute of Development Studies (IDS), December 2020. http://dx.doi.org/10.19088/creid.2020.010.

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This learning brief provides illustrative examples of three major adaptations the Coalition for Religious Equality and Inclusive Development (CREID) programme has undertaken while working within fragile contexts to promote freedom of religion or beilief (FoRB). These examples highlight how the programme has utilised embedded monitoring, evaluation, and learning (MEL) strategies to encourage what Ramalingam et al. (2019) refer to as ‘adaptive rigour’, a concept which underscores the importance of transparent documentation of programmatic decision making processes during programme adaptations. In particular this learning brief discusses the use of nested theories of change as a mechanism to enable adaptive rigour within the 'multitude of smalls' approach in the CREID programme.
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Clerides, Sofronis, Saul Lach, and James Tybout. Is "Learning-by-Exporting" Important? Micro-Dynamic Evidence from Colombia, Mexico and Morocco. Cambridge, MA: National Bureau of Economic Research, August 1996. http://dx.doi.org/10.3386/w5715.

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Harrison, Ann, Leslie Martin, and Shanthi Nataraj. Learning Versus Stealing: How Important are Market-Share Reallocations to India's Productivity Growth? Cambridge, MA: National Bureau of Economic Research, January 2011. http://dx.doi.org/10.3386/w16733.

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