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Relevant bibliographies by topics / Accelerative learning

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Academic literature on the topic 'Accelerative learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Accelerative learning"

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Knight, Debbie. "Learning styles and accelerative learning: An appraisal." Australian Journal of Learning Disabilities 2, no. 3 (September 1997): 25–28. http://dx.doi.org/10.1080/19404159709546538.

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Poynting, Scott, and Greg Noble. "‘Rekindling the Spark’: Teachers' Experiences of ‘Accelerative Learning’." Australian Journal of Education 42, no. 1 (April 1998): 32–48. http://dx.doi.org/10.1177/000494419804200103.

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SEVERAL teacher training packages called ‘Accelerative Learning’ (AL) have recently attracted widespread support among teachers in Australian schools. AL purports to expound a ‘brain theory’, covering various ‘learning styles’. This project investigated the experience of teachers who were attracted by AL. The authors surveyed AL texts, conducted participant observation in AL training, and interviewed AL proponents including 24 teachers at three Western Sydney secondary schools. We found a sense of revitalisation for teachers undertaking AL. While the brain theory of AL resonates with teachers'

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Noble, Greg, and Scott Poynting. "’Weird Science’ and ‘Common Sense’: the discursive construction of accelerative learning." Discourse: Studies in the Cultural Politics of Education 19, no. 2 (August 1998): 141–56. http://dx.doi.org/10.1080/0159630980190201.

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Poonoosamy, Mico. "THE INFLUENCE OF PERSONALITY TYPE ON FOREIGN LANGUAGE LEARNING: A CRITIQUE OF THE ACCELERATIVE INTEGRATED METHOD." PEOPLE: International Journal of Social Sciences 5, no. 3 (November 27, 2019): 142–52. http://dx.doi.org/10.20319/pijss.2019.53.142152.

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Olszewski-Kubilius, Paula. "Talent Search." Journal of Secondary Gifted Education 9, no. 3 (February 1998): 106–13. http://dx.doi.org/10.1177/1932202x9800900303.

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In this paper, the purpose and rationale of talent search is presented, followed by a discussion of the phenomenon of talent search. Talent search includes three important components: diagnosis and evaluation of domains and levels of talent; educational placement and guidance; and talent development opportunities, including summer programs, distance learning programs, contests, competitions, and other related events. There is a solid research base that supports the validity of the talent search identification protocol, the success of students in accelerative programs and the benefits of partic

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Carroll, Brandon. "Teaching FSL with AIM? An elementary school case study." SURG Journal 4, no. 2 (March 11, 2011): 21–22. http://dx.doi.org/10.21083/surg.v4i2.1261.

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The publication of the Roadmap for Canada’s Linguistic Duality 2008 – 2013 by the Canadian government has presented a challenge to the country’s ministries of education: to double, by the year 2013, the number of graduates from Canadian secondary schools who have acquired acquired a functional knowledge of their second language. The goal set out by this publication has yet again heightened the polemic around the most effective way to learn a second language. Contributing to the corpus of instructional materials for the teaching of FSL in Canada, Wendy Maxwell, a French teacher in British Colum

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KEREN-PORTNOY, TAMAR. "Facilitation and practice in verb acquisition." Journal of Child Language 33, no. 3 (August 2006): 487–518. http://dx.doi.org/10.1017/s0305000906007495.

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This paper presents a model of syntax acquisition, whose main points are as follows: Syntax is acquired in an item-based manner; early learning facilitates subsequent learning – as evidenced by the accelerating rate of new verbs entering a given structure; and mastery of syntactic knowledge is typically achieved through practice – as evidenced by intensive use and common word order errors – and this slows down learning during the early stages of acquiring a structure.The facilitation and practice hypotheses were tested on naturalistic production samples of six Hebrew-acquiring children ranging

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Mulyana, Enceng. "AKSELERASI PENINGKATAN KOMPETENSI PENDIDIK DAN TENAGA KEPENDIDIKAN NONFORMAL." JIV 2, no. 2 (December 31, 2007): 4–10. http://dx.doi.org/10.21009/jiv.0202.1.

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This article discusses various steps to increase the competence of PTK-PNF. First, it describes the roles of non-formal education in the national education system and the existing problems in the non-formal education. Then, the strategies to accelerate the competencies of PTK-PNF are introduced to cover the basic competencies and mapping of PTK-PNF. Further, the model of acceleration and need analysis to improve the competencies of PTK-PNF and the model of controlling the quality of the competence are elaborated and a set of controlling principles are identified. The article concludes that acc

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Rahma, Ulifa. "Effectiveness of Self-Regulated Learning Training in order to Enhance Self-Directed Learning Skill of Acceleration Students at MTsN Malang." GATR Global Journal of Business and Social Science Review (GJBSSR) Vol.5(3) Jul-Sep 2017 5, no. 3 (June 5, 2017): 106–13. http://dx.doi.org/10.35609/gjbssr.2017.5.3(14).

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Objective - The acceleration class is a special education which is formed to facilitate gifted children to accelerate their study. Junior high school students who attend acceleration program need self-directed learning skills. The research is to know the effectiveness of self-regulated learning training to enhance the self-directed learning skill of acceleration junior high school students. Methodology/Technique - The approach of the research is quantitative research with pre-experimental type one-group pretest-post-test design. The subject of the research is the 16 students of 9th grade at ac

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Adebiyi, Abdulafeez, Olatunde Abidakun, and V’yacheslav Akkerman. "Acceleration of Premixed Flames in Obstructed Pipes with Both Extremes Open." Energies 13, no. 16 (August 7, 2020): 4094. http://dx.doi.org/10.3390/en13164094.

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Premixed flame propagation in obstructed channels with both extremes open is studied by means of computational simulations of the reacting flow equations with a fully-compressible hydrodynamics, transport properties (heat conduction, diffusion and viscosity) and an Arrhenius chemical kinetics. The aim of this paper is to distinguish and scrutinize various regimes of flame propagation in this configuration depending on the geometrical and thermal-chemical parameters. The parametric study includes various channel widths, blockage ratios, and thermal expansion ratios. It is found that the interpl

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Dissertations / Theses on the topic "Accelerative learning"

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Scharn, Kay. "Accelerative learning in review." Online version, 1999. http://www.uwstout.edu/lib/thesis/1999/1999scharnk.pdf.

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Mou, Dai, and manchurian0@yahoo com. "The Use of Suggestion as a Classroom Learning Strategy in China and Australia: An Assessment Scale with Structural Equation Explanatory Models in Terms of Stress, Depression, Learning Styles and Academic Grades." RMIT University. Education, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070207.152256.

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This study is innovative in that it draws together the concepts of suggestion from several cultural groups and develops an inventory to account for variations the occurrence of scale to studies the relatively new area of the effects of suggestion in classrooms and compares effect on personality and academic variables. As new ideas and knowledge become more widespread and accepted by the community and teaching profession, precision in the applications of suggestion in the classroom is being seen as more important. Although new to education, suggestion and similar variations has al

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Mathari, Bakthavatsalam Pagalavan. "Hardware Acceleration of a Neighborhood Dependent Component Feature Learning (NDCFL) Super-Resolution Algorithm." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366034621.

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Samal, Kruttidipta. "FPGA acceleration of CNN training." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54467.

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This thesis presents the results of an architectural study on the design of FPGA- based architectures for convolutional neural networks (CNNs). We have analyzed the memory access patterns of a Convolutional Neural Network (one of the biggest networks in the family of deep learning algorithms) by creating a trace of a well-known CNN architecture and by developing a trace-driven DRAM simulator. The simulator uses the traces to analyze the effect that different storage patterns and dissonance in speed between memory and processing element, can have on the CNN system. This insight is then used cre

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Singh, Karanpreet. "Accelerating Structural Design and Optimization using Machine Learning." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104114.

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Machine learning techniques promise to greatly accelerate structural design and optimization. In this thesis, deep learning and active learning techniques are applied to different non-convex structural optimization problems. Finite Element Analysis (FEA) based standard optimization methods for aircraft panels with bio-inspired curvilinear stiffeners are computationally expensive. The main reason for employing many of these standard optimization methods is the ease of their integration with FEA. However, each optimization requires multiple computationally expensive FEA evaluations, making their

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Li, Zheng. "Accelerating Catalyst Discovery via Ab Initio Machine Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95915.

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In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The fr

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Erickson, Xavante. "Acceleration of Machine-Learning Pipeline Using Parallel Computing." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441722.

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Researchers from Lund have conducted research on classifying images in three different categories, faces, landmarks and objects from EEG data [1]. The researchers used SVMs (Support Vector Machine) to classify between the three different categories [2, 3]. The scripts written to compute this had the potential to be extremely parallelized and could potentially be optimized to complete the computations much faster. The scripts were originally written in MATLAB which is a propriety software and not the most popular language for machine learning. The aim of this project is to translate the MATLAB

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Irani, Arya John. "Utilizing negative policy information to accelerate reinforcement learning." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53481.

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A pilot study by Subramanian et al. on Markov decision problem task decomposition by humans revealed that participants break down tasks into both short-term subgoals with a defined end-condition (such as "go to food") and long-term considerations and invariants with no end-condition (such as "avoid predators"). In the context of Markov decision problems, behaviors having clear start and end conditions are well-modeled by an abstraction known as options, but no abstraction exists in the literature for continuous constraints imposed on the agent's behavior. We propose two representations to

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Rodrigues, Maia-Pinto Renata, and Fleith Denise de Souza. "Learning acceleration for gifted students: Favorable and unfavorable arguments." Pontificia Universidad Católica del Perú, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/102530.

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This paper analyzes acceleration in education as a practice for meeting the educational needs of gifted students, and points out favorable and unfavorable arguments on the use of this practice. Acceleration is an educational practice consisting of several teaching strategies designed to encourage academically gifted students and reduce their time spent in school. It promotes faster learning by matching the curriculum to the student’s level of knowledge, interest and motivation. There are several arguments in favor of acceleration, such as the improvement of academic performance, self-esteem an

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Obeda, Larry. "Impact of Learning Acceleration Program on Students Academic Success." Thesis, Wingate University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10685692.

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<p> This study is a review of the Learning Acceleration Program and the impact it has on student academic success in the Rural School District (pseudonym). This mixed-methods study used qualitative and quantitative data analyses to identify the impact that the Learning Acceleration Program has on the overall attendance and graduation rates for the district. The study also provided an understanding of the impact the Learning Acceleration Program has on perceptions as it pertains to the program. Data for this study were collected for the period of three academic school years on attendance, gradu

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Books on the topic "Accelerative learning"

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1930-, Gritton Charles E., ed. Suggestive accelerative learning techniques. New York: Gordon and Breach Science Publishers, 1986.

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Louise, Goll, and Accelerated Learning Systems, eds. Accelerate your learning. Aylesbury: Accelerated Learning Systems Ltd, 1992.

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Rose, Colin. Accelerate your learning. Aylesbury: Accelerated Learning Systems Ltd, 1992.

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Organization development: Accelerating learning and transformation. 2nd ed. New Delhi, India: SAGE/Response Business Books, 2011.

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Rose, Colin. Accelerated learning. 5th ed. Aylesbury: Accelerated Learning Systems, 1991.

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Rose, Colin. Accelerated learning. New York, N.Y: Dell Pub. Co, 1987.

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Rose, Colin. Accelerated learning. 4th ed. Aylesbury: Accelerated Learning Systems, 1989.

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Study skills strategies: Accelerate your learning. Menlo Park, Calif: Crisp Publications, 1994.

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Lengefeld, Uelaine. Study skills strategies: Accelerate your learning. Menlo Park, Calif: Crisp Publications, 1994.

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Accelerated learning. New York, N.Y: Dell Pub. Co., 1987.

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Book chapters on the topic "Accelerative learning"

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Roberts, Peter W., and Saurabh A. Lall. "Accelerating Learning About Accelerators." In Observing Acceleration, 187–201. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00042-4_11.

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Owens, David H. "Acceleration and Successive Projection." In Iterative Learning Control, 377–402. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6772-3_13.

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Thomke, Stefan. "Accelerating Learning by Experimentation." In Management of the Fuzzy Front End of Innovation, 125–40. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01056-4_10.

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Thijssen, Thomas J. P., Fons T. J. Vernooij, and Pieter Stein. "Accelerating Learning through Gaming?" In The Power of Technology for Learning, 25–41. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8747-9_2.

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García, Daniel, Ana González, and José R. Dorronsoro. "Accelerating Kernel Perceptron Learning." In Lecture Notes in Computer Science, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_17.

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Zhou, Zhi-Hua, Yang Yu, and Chao Qian. "Subset Selection: Acceleration." In Evolutionary Learning: Advances in Theories and Algorithms, 285–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5956-9_18.

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Bernard, James. "Accelerating Educational Transformation Through ICT." In Creating Holistic Technology-Enhanced Learning Experiences, 209–16. Rotterdam: SensePublishers, 2013. http://dx.doi.org/10.1007/978-94-6209-086-6_13.

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Abdelouahab, Kamel, Maxime Pelcat, and François Berry. "Accelerating the CNN Inference on FPGAs." In Deep Learning in Computer Vision, 1–40. First edition. | Boca Raton, FL : CRC Press/Taylor and Francis, 2020. |: CRC Press, 2020. http://dx.doi.org/10.1201/9781351003827-1.

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Ji, Hangxu, Gang Wu, and Guoren Wang. "Accelerating ELM Training over Data Streams." In Proceedings in Adaptation, Learning and Optimization, 182–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_20.

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Guitton, Pierre, Robert Kasprzyk, and Jeannine Sorge. "Dow:Sustaining Change and Accelerating Growth through Business-Focused Learning." In Business Driven Action Learning, 14–28. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230285866_2.

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Conference papers on the topic "Accelerative learning"

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Israni, Kumar Chris, and Colleen Watson. "Applying the Concept of Accelerative Learning for Design and Delivery of Process and Personnel Safety Leadership Programs in Oil & Gas Assets." In SPE Health, Safety, Security, Environment, & Social Responsibility Conference - North America. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/184433-ms.

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Kucherov, Valery, Amy McDonald, Ivan Ivanov, and Janet Rose. "The Application of the Accelerative Learning Cycle to the Design and Delivery of Safety Leadership Programs for Personnel of Onshore and Offshore Upstream Oil Assets." In SPE Annual Caspian Technical Conference & Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/177351-ms.

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Kucherov, Valery, Amy McDonald, Ivan Ivanov, and Janet Rose. "The Application of the Accelerative Learning Cycle to the Design and Delivery of Safety Leadership Programs for Personnel of Onshore and Offshore Upstream Oil Assets (Russian)." In SPE Annual Caspian Technical Conference & Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/177351-ru.

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Kale, David, and Yan Liu. "Accelerating Active Learning with Transfer Learning." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.160.

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Wang, Yu, Lixue Xia, Ming Cheng, Tianqi Tang, Boxun Li, and Huazhong Yang. "RRAM based learning acceleration." In the International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2968455.2981124.

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Qian, Yuhua, Jiye Liang, and Wei Wei. "Accelerating incomplete feature selection." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212472.

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Santos, A. C. F., P. Fonseca, L. F. S. Coelho, Floyd D. McDaniel, and Barney L. Doyle. "Can Accelerators Accelerate Learning?" In APPLICATION OF ACCELERATORS IN RESEARCH AND INDUSTRY: Twentieth International Conference. AIP, 2009. http://dx.doi.org/10.1063/1.3120016.

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Liu, Chuanjian, Yunhe Wang, Kai Han, Chunjing Xu, and Chang Xu. "Learning Instance-wise Sparsity for Accelerating Deep Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/416.

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Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks. Most of existing approaches used to accelerate deep models by manipulating parameters or filters without data, e.g., pruning and decomposition. In contrast, we study this problem from a different perspective by respecting the difference between data. An instance-wise feature pruning is developed by identifying informative features for different instances. Specifically, by investigating a feature decay regularization, we expect intermediate feature

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Chen, Pu, and Hung-Hsuan Chen. "Accelerating Matrix Factorization by Overparameterization." In 1st International Conference on Deep Learning Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009885600890097.

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Zhao, Ruizhe, Wayne Luk, Xinyu Niu, Huifeng Shi, and Haitao Wang. "Hardware Acceleration for Machine Learning." In 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 2017. http://dx.doi.org/10.1109/isvlsi.2017.127.

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Reports on the topic "Accelerative learning"

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Lacy, Susan Whitney, and Charles Snider. Machine Learning and Code Acceleration. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463447.

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Daniels, Matthew, Autumn Toney, Melissa Flagg, and Charles Yang. Machine Intelligence for Scientific Discovery and Engineering Invention. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200099.

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The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.

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Rodriguez, Dominic, Emily Marie Gaffney, Taylor Marie Stewart, Christopher A. Apblett, and Joan Tafoya. Accelerating Learning with Set-Based Concurrent Engineering. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1605517.

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Duarte, Javier, and et al. FPGAs as a Service to Accelerate Machine Learning Inference. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1570210.

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Lavadenz, Magaly, Elvira Armas, and Rosalinda Barajas. Preventing Long-Term English Learners: Results from a Project-Based Differentiated ELD Intervention Program. CEEL, 2012. http://dx.doi.org/10.15365/ceel.article.2012.1.

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<p>In this article the authors describe efforts taken by a small southern California school district to develop and implement an innovative, research-based English Language Development program to address a growing concern over long-term English Learners (LTELs) in their district. With support from the Weingart Foundation this afterschool program served 3<sup>rd</sup> and 7<sup>th</sup> grade LTELs between 2008–2011 to accelerate language and literacy acquisition and prevent prolonged EL status. Program evaluation results indicated that the intervention was associa

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