Relevant bibliographies by topics / Approaches to learning

Contents

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

Academic literature on the topic 'Approaches to learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Approaches to learning"

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Urhahne, Detlef. "Learning approaches." Educational Psychology 40, no. 5 (May 27, 2020): 533–34. http://dx.doi.org/10.1080/01443410.2020.1755503.

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Wiltshire, Monica. "Approaches to learning." Early Years Educator 17, no. 11 (March 2, 2016): 28–30. http://dx.doi.org/10.12968/eyed.2016.17.11.28.

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Duff, Angus, and Sam McKinstry. "Students' Approaches to Learning." Issues in Accounting Education 22, no. 2 (May 1, 2007): 183–214. http://dx.doi.org/10.2308/iace.2007.22.2.183.

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This paper provides an overview of the students' approaches to learning (SAL) literature, including a review of the models, theories, and research instruments. SAL research has developed largely in the United Kingdom and Australasia, where its concepts are widely understood by academics. Yet little research using these ideas has been done in North America. To encourage American accounting educators to redress this imbalance, the paper describes the motivations for undertaking SAL research, describes conceptions of learning, and reviews a number of inventories developed by SAL scholars for applied research in the field. In addition, this paper traces the development of SAL research in the discipline of accounting education. Finally, the paper reviews 19 extant articles to offer suggestions for future research.
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Wasson, Barbara, and Paul A. Kirschner. "Learning Design: European Approaches." TechTrends 64, no. 6 (May 13, 2020): 815–27. http://dx.doi.org/10.1007/s11528-020-00498-0.

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Abstract Research on instructional and learning design is ‘booming’ in Europe, although there has been a move from a focus on content and the way to present it in a formal educational context (i.e., instruction), to a focus on complex learning, learning environments including the workplace, and access to learner data available in these environments. We even see the term ‘learning experience design’ (Neelen and Kirschner 2020) to describe the field. Furthermore, there is an effort to empower teachers (and even students) as designers of learning (including environments and new pedagogies), and to support their reflection on their own practice as part of their professional development (Hansen and Wasson 2016; Luckin et al. 2016; Wasson et al. 2016). While instructional design is an often heard term in the United States and refers to “translating principles of learning and instruction into plans for instructional materials, activities, information resources, and evaluation” (Smith and Ragan 1999), Europe tends to lean more towards learning design as the key for providing efficient, effective, and enjoyable learning experiences. This is not a switch from an instructivist to a constructivist view nor from a teacher-centred to a student-centred paradigm. It is, rather, a different mind-set where the emphasis is on the goal (i.e., learning) rather than the approach (i.e., instruction). Designing learning opportunities in a technology enhanced world builds on theories of human learning and cognition, opportunities provided by technology, and principles of instructional design. New technology both expands and challenges some instructional design principles by opening up new opportunities for distance collaboration, intelligent tutoring and support, seamless and ubiquitous learning and assessment technologies, and tools for thinking and thought. In this article, the authors give an account of their own and other research related to instructional and learning design, highlight related European research, and point to future research directions.
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Albergaria-Almeida, Patrícia, José Joaquim Teixeira-Dias, Mariana Martinho, and Chinthaka Balasooriya. "Kolb’s Learning Styles and Approaches to Learning." International Journal of Knowledge Society Research 1, no. 3 (July 2010): 1–16. http://dx.doi.org/10.4018/jksr.2010070101.

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The purpose of this study is to investigate if the teaching, learning and assessment strategies conceived and implemented in a higher education chemistry course promote the development of conceptual understanding, as intended. Thus, our aim is to analyse the learning styles and the approaches to learning of chemistry undergraduates with better grades.This study took place during the 1st semester of the school year 2009/2010. This research was carried out in a naturalistic setting, within the context of chemistry classes for 1st year science and engineering courses, at the University of Aveiro, in Portugal. The class was composed of 100 students. At the end of the semester, the 8 chemistry students with the highest grades were selected for interview. Data was collected through Kolb’s Learning Styles Inventory, through Approaches and Study Skills Inventory for Students, through non-participant observation, through the analysis of students’ participation in online forums and lab books.The overall results show that the students with better grades possess the assimilator learning style, that is usually associated to the archetypal chemist. Moreover, the students with the highest grades revealed a conception of learning emphasising understanding. However, these students diverged both in their learning approaches and in their preferences for teaching strategies. The majority of students adopted a deep approach or a combination of a deep and a strategic approach, but half of them revealed their preference for teaching-centred strategies.
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Cuthbert, Peter F. "The student learning process: Learning styles or learning approaches?" Teaching in Higher Education 10, no. 2 (April 2005): 235–49. http://dx.doi.org/10.1080/1356251042000337972.

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Sharma, Divesh S. "Accounting students' learning conceptions, approaches to learning, and the influence of the learning–teaching context on approaches to learning." Accounting Education 6, no. 2 (June 1997): 125–46. http://dx.doi.org/10.1080/096392897331532.

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Rajaratnam, Navin, and SuzanneMaria D′cruz. "Learning styles and learning approaches - Are they different?" Education for Health 29, no. 1 (2016): 59. http://dx.doi.org/10.4103/1357-6283.178924.

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Ahamad, Maksud, and Nesar Ahmad. "Machine Learning Approaches to Digital Learning Performance Analysis." International Journal of Computing and Digital Systems 10, no. 1 (November 25, 2021): 963–71. http://dx.doi.org/10.12785/ijcds/100187.

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Battou, Amal, Omar Baz, and Driss Mammass. "Learning Design Approaches for Designing Virtual Learning Environments." Communications on Applied Electronics 5, no. 9 (September 26, 2016): 31–37. http://dx.doi.org/10.5120/cae2016652369.

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Dissertations / Theses on the topic "Approaches to learning"

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Potari, Despina. "Learning approaches in mathematics." Thesis, University of Edinburgh, 1987. http://hdl.handle.net/1842/12130.

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Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.

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Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.
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Effraimidis, Dimitros. "Computation approaches for continuous reinforcement learning problems." Thesis, University of Westminster, 2016. https://westminsterresearch.westminster.ac.uk/item/q0y82/computation-approaches-for-continuous-reinforcement-learning-problems.

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Optimisation theory is at the heart of any control process, where we seek to control the behaviour of a system through a set of actions. Linear control problems have been extensively studied, and optimal control laws have been identified. But the world around us is highly non-linear and unpredictable. For these dynamic systems, which don’t possess the nice mathematical properties of the linear counterpart, the classic control theory breaks and other methods have to be employed. But nature thrives by optimising non-linear and over-complicated systems. Evolutionary Computing (EC) methods exploit nature’s way by imitating the evolution process and avoid to solve the control problem analytically. Reinforcement Learning (RL) from the other side regards the optimal control problem as a sequential one. In every discrete time step an action is applied. The transition of the system to a new state is accompanied by a sole numerical value, the “reward” that designate the quality of the control action. Even though the amount of feedback information is limited into a sole real number, the introduction of the Temporal Difference method made possible to have accurate predictions of the value-functions. This paved the way to optimise complex structures, like the Neural Networks, which are used to approximate the value functions. In this thesis we investigate the solution of continuous Reinforcement Learning control problems by EC methodologies. The accumulated reward of such problems throughout an episode suffices as information to formulate the required measure, fitness, in order to optimise a population of candidate solutions. Especially, we explore the limits of applicability of a specific branch of EC, that of Genetic Programming (GP). The evolving population in the GP case is comprised from individuals, which are immediately translated to mathematical functions, which can serve as a control law. The major contribution of this thesis is the proposed unification of these disparate Artificial Intelligence paradigms. The provided information from the systems are exploited by a step by step basis from the RL part of the proposed scheme and by an episodic basis from GP. This makes possible to augment the function set of the GP scheme with adaptable Neural Networks. In the quest to achieve stable behaviour of the RL part of the system a modification of the Actor-Critic algorithm has been implemented. Finally we successfully apply the GP method in multi-action control problems extending the spectrum of the problems that this method has been proved to solve. Also we investigated the capability of GP in relation to problems from the food industry. These type of problems exhibit also non-linearity and there is no definite model describing its behaviour.
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Chang, Yu-Han Ph D. Massachusetts Institute of Technology. "Approaches to multi-agent learning." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33932.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 165-171).
Systems involving multiple autonomous entities are becoming more and more prominent. Sensor networks, teams of robotic vehicles, and software agents are just a few examples. In order to design these systems, we need methods that allow our agents to autonomously learn and adapt to the changing environments they find themselves in. This thesis explores ideas from game theory, online prediction, and reinforcement learning, tying them together to work on problems in multi-agent learning. We begin with the most basic framework for studying multi-agent learning: repeated matrix games. We quickly realize that there is no such thing as an opponent-independent, globally optimal learning algorithm. Some form of opponent assumptions must be necessary when designing multi-agent learning algorithms. We first show that we can exploit opponents that satisfy certain assumptions, and in a later chapter, we show how we can avoid being exploited ourselves. From this beginning, we branch out to study more complex sequential decision making problems in multi-agent systems, or stochastic games. We study environments in which there are large numbers of agents, and where environmental state may only be partially observable.
(cont.) In fully cooperative situations, where all the agents receive a single global reward signal for training, we devise a filtering method that allows each individual agent to learn using a personal training signal recovered from this global reward. For non-cooperative situations, we introduce the concept of hedged learning, a combination of regret-minimizing algorithms with learning techniques, which allows a more flexible and robust approach for behaving in competitive situations. We show various performance bounds that can be guaranteed with our hedged learning algorithm, thus preventing our agent from being exploited by its adversary. Finally, we apply some of these methods to problems involving routing and node movement in a mobilized ad-hoc networking domain.
by Yu-Han Chang.
Ph.D.
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Flaherty, Drew. "Artistic approaches to machine learning." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200191/1/Drew_Flaherty_Thesis.pdf.

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This research is about how Artificial Intelligence and Machine Learning may impact creative practice. The thesis looks at various implementations and models related to the subject from different cultural and technical viewpoints. The project also provides experimental creative outcomes from my personal practice along with a qualitative study into attitudes and perspectives from other creative practitioners.
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Yu, Kai. "Statistical Learning Approaches to Information Filtering." Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-25120.

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Kashima, Hisashi. "Machine learning approaches for structured data." 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/135953.

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Chen, Zhe Haykin Simon S. "Stochastic approaches for correlation-based learning." *McMaster only, 2004.

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Boots, Byron. "Spectral Approaches to Learning Predictive Representations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/131.

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A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must obtain an accurate environment model, and then plan to maximize reward. However, for complex domains, specifying a model by hand can be a time consuming process. This motivates an alternative approach: learning a model directly from observations. Unfortunately, learning algorithms often recover a model that is too inaccurate to support planning or too large and complex for planning to succeed; or, they require excessive prior domain knowledge or fail to provide guarantees such as statistical consistency. To address this gap, we propose spectral subspace identification algorithms which provably learn compact, accurate, predictive models of partially observable dynamical systems directly from sequences of action-observation pairs. Our research agenda includes several variations of this general approach: spectral methods for classical models like Kalman filters and hidden Markov models, batch algorithms and online algorithms, and kernel-based algorithms for learning models in high- and infinite-dimensional feature spaces. All of these approaches share a common framework: the model’s belief space is represented as predictions of observable quantities and spectral algorithms are applied to learn the model parameters. Unlike the popular EM algorithm, spectral learning algorithms are statistically consistent, computationally efficient, and easy to implement using established matrixalgebra techniques. We evaluate our learning algorithms on a series of prediction and planning tasks involving simulated data and real robotic systems.
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Pellegrini, Giovanni. "Relational Learning approaches for Recommender Systems." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/318892.

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Learning on relational data is a relevant task in the machine learning community. Extracting information from structured data is a non-trivial task due to the combinatorial complexity of the domain and the necessity to construct methods that work on collections of values of different sizes rather than fixed representations. Relational data can naturally be interpreted as graphs, a class of flexible and expressive structures that can model data from diverse domains,from biology to social interactions. Graphs have been used in a huge variety of contexts, such as molecular modelling, social networks, image processing and recommendation systems. In this manuscript, we tackle some challenges in learning on relational data by developing new learning methodologies. Specifically, in our first contribution, we introduce a new class of metrics for relational data based on relational features extraction technique called Type ExtensionTrees. This class of metrics defines the (dis)similarity of two nodes in a graph by exploiting the nested structure of their relational neighborhood at different depth steps. In our second contribution, we developed a new strategy to collect the information of multisets of data values by introducing a new framework of learnable aggregators called Learning Aggregation Functions.We provide a detailed description of the methodologies and an extensive experimental evaluation on synthetic and real world data to assess the expressiveness of the proposed models. A particular focus is given to the application of these methods to the recommendation systems domain, exploring the combination of the proposed methods with recent techniques developed for Constructive Preference Elicitation and Group Recommendation tasks.
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Books on the topic "Approaches to learning"

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Stobie, Tristian D. Approaches to learning. Petersfield: European Council of International Schools., 1997.

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Approaches to learning. Ypsilanti, Mich: Highscope Press, 2012.

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Kim, Reid D., Hresko Wayne P, and Swanson H. Lee 1947-, eds. Cognitive approaches to learning disabilities. 3rd ed. Austin, TX: Pro-Ed, 1996.

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Holistic approaches to language learning. Frankfurt am Main: P. Lang, 2005.

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Inc, ebrary, ed. Machine learning approaches to bioinformatics. Singapore: World Scientific, 2010.

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1943-, Kueker D. W., and Smith Carl H. 1950-, eds. Learning and geometry: Computational approaches. Boston: Birkhäuser, 1996.

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Harteis, Christian, David Gijbels, and Eva Kyndt, eds. Research Approaches on Workplace Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89582-2.

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Cutting, Roger, and Rowena Passy, eds. Contemporary Approaches to Outdoor Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85095-1.

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Touretzky, David, ed. Connectionist Approaches to Language Learning. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4008-3.

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Kueker, David W., and Carl H. Smith, eds. Learning and Geometry: Computational Approaches. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4088-4.

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Book chapters on the topic "Approaches to learning"

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Singh, Nirbhay N., Diane E. D. Deitz, and Judy Singh. "Behavioral Approaches." In Learning Disabilities, 375–414. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9133-3_13.

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Daelemans, Walter. "Machine Learning Approaches." In Text, Speech and Language Technology, 285–304. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9273-4_17.

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Yang, Ching-Chi, and Lih-Yuan Deng. "Statistical Learning Approaches." In Dimensionality Reduction in Data Science, 169–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_8.

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Venugopal, Deepak, and Max Garzon. "Machine Learning Approaches." In Dimensionality Reduction in Data Science, 179–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_9.

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Abbott, Tina. "Social learning approaches." In Social and Personality Development, 51–66. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003209300-6.

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Liu, Han, Alexander Gegov, and Mihaela Cocea. "Ensemble Learning Approaches." In Studies in Big Data, 63–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23696-4_6.

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Misra, Pradeep Kumar. "Approaches to Learning." In Learning and Teaching for Teachers, 17–36. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3077-4_2.

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Kim, Rina, and Lillie R. Albert. "Methodological Approaches." In Mathematics Teaching and Learning, 33–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13542-7_3.

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Hengst, Bernhard. "Hierarchical Approaches." In Adaptation, Learning, and Optimization, 293–323. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3_9.

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Lavrač, Nada, Vid Podpečan, and Marko Robnik-Šikonja. "Unified Representation Learning Approaches." In Representation Learning, 143–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68817-2_6.

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Conference papers on the topic "Approaches to learning"

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Li, Yumeng, Pingfeng Wang, and Weirong Xiao. "Uncertainty Quantification of Atomistic Materials Simulation with Machine Learning Potentials." In 2018 AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-2166.

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Anıktar, Serhat, and Ayfer Aytuğ. "DESIGNING LEARNING SPACES TO DIFFERENT LEARNING APPROACHES." In International Technology, Education and Development Conference. IATED, 2016. http://dx.doi.org/10.21125/iceri.2016.1119.

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Sen, Gabriel, Albert Adeboye, and Oluwole Alagbe. "STUDENT LEARNING APPROACHES OF ARCHITECTURE STUDENTS: DEEP OR SURFACE LEARNING APPROACH." In 14th International Technology, Education and Development Conference. IATED, 2020. http://dx.doi.org/10.21125/inted.2020.2588.

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Yuen, Andrew. "Blended learning in economics and finance courses at business school." In International Conference on New Approaches in Education. Global, 2019. http://dx.doi.org/10.33422/icnaeducation.2019.07.394.

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Isaac, Benson, and Douglas L. Allaire. "A Dynamic Data-Driven Approach to Optimal Offline Learning for Online Flight Capability Estimation." In 18th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-1444.

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Yılmaz, Veysel. "Financial Machine Learning." In 4th International Symposium on Innovative Approaches in Social, Human and Administrative Sciences. SETSCI, 2019. http://dx.doi.org/10.36287/setsci.4.8.035.

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Khalil, Mohammad. "Probabilistic Approaches to Transfer Learning." In Proposed for presentation at the 4th annual Sandia Machine Learning and Deep Learning Workshop held July 19-22, 2021 in ,. US DOE, 2021. http://dx.doi.org/10.2172/1882483.

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Tena Cucala, David J., Bernardo Cuenca Grau, and Boris Motik. "Faithful Approaches to Rule Learning." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/50.

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Rule learning involves developing machine learning models that can be applied to a set of logical facts to predict additional facts, as well as providing methods for extracting from the learned model a set of logical rules that explain symbolically the model's predictions. Existing such approaches, however, do not describe formally the relationship between the model's predictions and the derivations of the extracted rules; rather, it is often claimed without justification that the extracted rules `approximate' or `explain' the model, and rule quality is evaluated by manual inspection. In this paper, we study the formal properties of Neural-LP--a prominent rule learning approach. We show that the rules extracted from Neural-LP models can be both unsound and incomplete: on the same input dataset, the extracted rules can derive facts not predicted by the model, and the model can make predictions not derived by the extracted rules. We also propose a modification to the Neural-LP model that ensures that the extracted rules are always sound and complete. Finally, we show that, on several prominent benchmarks, the classification performance of our modified model is comparable to that of the standard Neural-LP model. Thus, faithful learning of rules is feasible from both a theoretical and practical point of view.
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Shehu Kabir, Fatima. "Application of Unified Theory of Acceptance and Use of Technology to Learning Management System Use: A Study of Ahmadu Bello University Distance Learning Centre." In 3rd International Conference on New Approaches in Education. GLOBALKS, 2021. http://dx.doi.org/10.33422/3rd.icnaeducation.2021.07.26.

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Wen, Xue. "MOOC Learning Outcome Prediction Using Machine Learning Approaches." In 2022 AERA Annual Meeting. Washington DC: AERA, 2022. http://dx.doi.org/10.3102/1885130.

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Reports on the topic "Approaches to learning"

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Tucker, Jennifer S. Mobile Learning Approaches for U.S. Army Training. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada528742.

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Villavicencio, Xuzel, and Caitlin Coflan. Hybrid learning International experiences with multimodal approaches. EdTech Hub, July 2022. http://dx.doi.org/10.53832/edtechhub.0112.

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Gress, Gabriel. Understanding machine learning approaches for partial differential equations. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1669073.

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Bhagavatula, Vijayakumar. Advanced Signal Processing and Machine Learning Approaches for EEG Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada535204.

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Tillett, Will, and Oliver Jones. ‘Improving Rural Sanitation in Challenging Contexts’ Sanitation Learning Hub Learning Brief 8. The Sanitation Learning Hub, Institute of Development Studies, March 2021. http://dx.doi.org/10.19088/slh.2021.006.

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Of the two billion people worldwide lacking access to at least basic sanitation, seven out of ten live in rural areas. Progress has been made on increasing rural sanitation and access levels are rising, but barriers remain in reaching the ‘last mile’ or some 10 to 20 per cent of the population who live in the most challenging contexts. The factors affecting the ability of households to construct and use toilets, as well as the challenges sanitation programmes face in reaching specific groups, are highly diverse. Applying one-size fits all approaches has been proven not to work; therefore, we need more nuanced, adapted, and targeted approaches to capture the universality element of the Sustainable Development Goals (SDGs) and ensure that no one is left behind. However, we recognise that challenges can be persistent and there are limited documented examples of how to overcome these challenges at scale. The Sanitation Learning Hub, UNICEF, and WaterAid commissioned this study to map rural sanitation approaches in challenging contexts and the guidance currently being used, drawing out emerging experiences and lessons. It involved key informant interviews (KIIs) with 44 interviewees, and consulting over 180 documented resources. This Learning Brief provides an overview of the study findings.
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6

Meeker, Jessica. Mutual Learning for Policy Impact: Insights from CORE. Sharing Experience and Learning on Approaches to Influence Policy and Practice. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/core.2021.005.

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On 23 June 2021, Southern Voice and the Institute of Development Studies co-hosted an online dialogue which aimed to enhance efforts to inform and influence policy by sharing learning between CORE projects, at different stages in their policy engagement activities, on their approaches and experiences at sub-national, national, and regional levels. The event was attended by over 70 participants from across the CORE cohort and highlighted the experiences of CORE partners, Partnership for Economic Policy (PEP), International Centre for Research on Women (ICRW), and Group for the Analysis of Development (GRADE). This learning guide captures the practical insights and advice from the event to help inform the practice of both participants and other projects across the portfolio. The guide is structured around the key challenges identified in influencing policy, particularly within the changing parameters of the current pandemic, highlighting key messages and examples from the three partners.
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Grogger, Jeffrey, Sean Gupta, Ria Ivandic, and Tom Kirchmaier. Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28293.

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Sihi, Debjani, Kanad Basu, and Debjani Singh. Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769721.

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9

Zeidenstein, Sondra, and Kirsten Moore. Learning About Sexuality: A Practical Beginning. Population Council, 1996. http://dx.doi.org/10.31899/pgy1996.1007.

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“Learning About Sexuality: A Practical Beginning” is divided into three main parts. The first includes approaches that program staff, activists, and researchers are taking to understand people’s experiences of sexuality. The second explores the explicit and implicit links among health-seeking behavior, contraceptive practice, reproductive health, and sexuality. The chapters in part three focus on activities that challenge entrenched attitudes and behavior about sexuality that have real and potentially harmful effects on women’s and men’s reproductive health. The book features program and research work in all regions of the world with women, men, girls, and boys. The chapters are written by authors from over a dozen countries, with over half the contributions coming from developing countries. Collectively, these chapters represent an exploration of the relationship of sexuality to reproductive health, contraceptive practice, and overall well-being. For all their variety of place, approach, and focus, a number of common themes emerge.
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Ma, Yue, and Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.

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Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount of textual data for the medical domain and the rich content of Snomed CT, such an approach comes with the benefit that no manually labelled corpus is required. We also show that the type information for Snomed CT concept is an important feature to be examined for such a system. We test and evaluate the approach using two types of texts. Experimental results show that the proposed approach is promising to assist Snomed CT development.
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