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Journal articles on the topic 'Accelerative learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

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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2

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' ‘scientific’ training, its formulaic solutions deal with the craft aspect of teaching. Teachers are seeking immediately practical solutions to the crisis they are experiencing with intensification of their work. Appearing to ‘cater to’ learners overlooked in ‘conventional’ teaching, AL's formulas fix differences in cultures of learning into individual, biological ones — reinforcing social inequalities. Nevertheless an important grain of good sense can be identified in teachers' common sense of AL, in its returning of schools' attention to pedagogy.

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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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5

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 participation. Talent search has had an effect on general education, particularly with influencing ideas about students' readiness for learning and the timing and pace of instruction. Talent search programs also need to take steps to ensure greater access, particularly to economically disadvantaged students.

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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 Columbia, developed the AIM (Accelerative Integrated Method). The AIM proposes to accelerate the learning of the target language through the use of gestures (The Gesture Approach) so that students can understand and speak in the second language (SL) as early as possible. In spite of the growing popularity and favorable reception of the program by teachers, there is very little research examining its effectiveness in the classroom. This article proposes to add to the current body of research by examining the efficiency of the AIM for the teaching of FSL on a practical and theoretical level. Data acquired from a proficiency test administered to elementary core French students taught with the AIM will serve as a springboard in defining the potential outcomes one can attain with the program. Finally, a review of the literature on the AIM as well as the use of gesture in the SL classroom will bring into evidence the theoretical merits of the method.

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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 from ages 1;1 to 2;7 (average ages 1;6 to 2;4 months). Results show that most structures did in fact accelerate; the notion of ‘practice’ is supported by the inverse correlation found between number of verbs and number of errors in the earliest productions in a given structure; and the absence of acceleration in a minority of the structures is due to the fact that they involve relatively less practice.

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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 accelerating the competencies of PTK-PNF is an urgent need and can not be avoided to achieve the objectives of national education. This could be done through pre-service training, inand on-service trainings or distance learning programs. Total Quality Management and Professional Approach are recommended to be implemented continuously in each program.

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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 acceleration MTsN 1 Malang. The instruments to gather data is scale and interview. To get data on knowledge and skill of training self-regulated learning, it is used test of knowledge, observation, interview, and worksheet. Findings - The result of showed that the student's self-directed learning skill is enhanced after self-regulated learning training. The knowledge and skill of self-regulated learning students also improved. The training has significant impact on the knowledge and skill on self-regulated learning that is a factor in improving self-directed learning skill of the students. As a conclusion, this research showed that self-regulated learning training proved effective in increasing self directed learning skill of acceleration students at MTsN 1 Malang. Novelty - Self regulated learning is one of the effective method to enhance self-directed learning students Type of Paper: Empirical Keywords: Self-Directed Learning Skill; Self-Regulated Learning Training JEL Classification: I21, I25.

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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 interplay of these three critical parameters determines a regime of flame propagation. Specifically, either a flame propagates quasi-steady, without acceleration, or it experiences three consecutive distinctive phases (quasi-steady propagation, acceleration and saturation). This study is mainly focused on the flame acceleration regime. The accelerating phase is exponential in nature, which correlates well with the theoretical prediction from the literature. The accelerating trend also qualitatively resembles that from semi-open channels, but acceleration is substantially weaker when both extremes are open. Likewise, the identified regime of quasi-steady propagation fits the regime of flame oscillations, found for the low Reynolds number flames. In addition, the machine learning logistic regression algorithm is employed to characterize and differentiate the parametric domains of accelerating and non-accelerating flames.

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Ma, Dongqi, and Hui Lin. "An Accelerating Iterative Learning Control Based on an Adjustable Learning Interval." Journal of Control Science and Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1731676.

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An iterative learning control algorithm with an adjustable interval is proposed for nonlinear systems to accelerate the convergence rate of iterative learning control. Forλ-norm, the monotonic convergence of ILC was analyzed, and the corresponding convergence conditions were obtained. The results showed that the convergence rate was mainly determined by the controlled object, the control law gain, the correction factor, and the iteration interval size and that the control law gain was corrected in real time in the modified interval and the modified interval shortened as the number of iterations increased, further accelerating the convergence. The numerical simulation shows the effectiveness of the proposed method.

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Gibson, Trish, Donald Kerr, and Ron Fisher. "Accelerating supply chain management learning: identifying enablers from a university-industry collaboration." Supply Chain Management: An International Journal 21, no. 4 (June 13, 2016): 470–84. http://dx.doi.org/10.1108/scm-10-2014-0343.

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Purpose There is an acknowledged need to advance the supply chain management (SCM) learning agenda, with an emphasis on integration. This paper discuss an Australian university–industry collaboration aimed at accelerating SCM learning and offers some insight into models for building a forward-looking SCM. Design/methodology/approach The research is an exploratory case study of the industry–university collaboration, using grounded theory procedures. The primary data involved 25 interviews with key participants from the university and industry partners, and from the first cohort of students in the undergraduate program developed within the collaboration. Findings A theoretical framework for accelerating SCM learning was developed from the case study data; it comprises six constructs that influence, at strategic and operational levels, the acceleration of SCM learning. Four cross-construct concepts from the framework that form the cornerstones of accelerated learning are discussed in some detail. Research limitations/implications The framework facilitates an examination of successes and shortfalls in the case study collaboration and generates a deeper understanding of critical elements for progressing the SCM learning agenda, and expanding SCM education. As with all qualitative research, the results may not be generalisable; testing the relevance and usefulness of the framework with the field will be an important next step. Practical implications As the framework identifies conditions, characteristics and capacities of organisations and individuals that support the acceleration of SCM learning, it can provide assistance in designing future university–industry collaborations for advancing SCM learning. Originality/value The framework identifies critical success factors for alliances and partnerships aimed at accelerating learning in an emerging body of knowledge such as SCM.

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Petersen, Sobah Abbas, Manuel Oliveira, Kristin Hestetun, and Anette Østbø Sørensen. "ALF - a Framework for Evaluating Accelerated Learning in Industry." International Journal of Serious Games 6, no. 3 (September 20, 2019): 81–99. http://dx.doi.org/10.17083/ijsg.v6i3.314.

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Games have long been considered as a means to support effective learning, motivate learners and accelerate their learning. Several successful studies using game-based learning are reported in the literature. However, there appears to be a research gap on systematically evaluating accelerated learning in game environments. The main research question we address in this paper is how can we evaluate accelerated learning in game-based learning environments? The main contribution of this paper will be a framework for evaluating accelerated learning in games (ALF). We will illustrate the use of this framework by describing studies conducted in the Norwegian industrial project ALTT (Accelerate Learning Through Technology), aimed at capacity building in the aluminium industry, where we have co-designed a game for accelerating learning about the electrolysis process for extracting aluminium and heat balance in the aluminium production cells.

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Hesse, Bradford William. "Role of the Internet in Solving the Last Mile Problem in Medicine." Journal of Medical Internet Research 21, no. 10 (October 28, 2019): e16385. http://dx.doi.org/10.2196/16385.

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Internet-augmented medicine has a strong role to play in ensuring that all populations benefit equally from discoveries in the medical sciences. Yet, data from the Centers for Disease Control and Prevention collected from 1999 to 2014 suggested that during the first phase of internet diffusion, progress against mortality has stalled, and in some cases, receded in rural areas that are traditionally underserved by medical and broadband resources. This problem of failing to extend the benefits of extant medical knowledge equitably to all populations regardless of geography can be framed as the “last mile problem in health care.” In theory, the internet should help solve the last mile problem by making the best knowledge in the world available to everyone worldwide at a low cost and no delay. In practice, the antiquated supply chains of industrial age medicine have been slow to yield to the accelerative forces of evolving internet capacity. This failure is exacerbated by the expanding digital divide, preventing residents of isolated, geographically distant communities from taking full advantage of the digital health revolution. The result, according to the Federal Communications Commission’s (FCC’s) Connect2Health Task Force, is the unanticipated emergence of “double burden counties,” ie, counties for which the mortality burden is high while broadband access is low. The good news is that a convergence of trends in internet-enabled health care is putting medicine within striking distance of solving the last mile problem both in the United States and globally. Specific trends to monitor over the next 25 years include (1) using community-driven approaches to bridge the digital divide, (2) addressing structural disconnects in care through P4 Medicine, (3) meeting patients at “point-of-need,” (4) ensuring that no one is left behind through population management, and (5) self-correcting cybernetically through the learning health care system.

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Jiang, Chao, David Ojika, Sofia Vallecorsa, Thorsten Kurth, Prabhat, Bhavesh Patel, and Herman Lam. "Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA." EPJ Web of Conferences 245 (2020): 09014. http://dx.doi.org/10.1051/epjconf/202024509014.

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AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.

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Linton, Frank, and David House. "Accelerating workplace learning." International Journal of Continuing Engineering Education and Lifelong Learning 12, no. 5/6 (2002): 419. http://dx.doi.org/10.1504/ijceell.2002.002143.

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Martin, Cathy. "Accelerating Unfinished Learning." Mathematics Teacher: Learning and Teaching PK-12 113, no. 10 (October 2020): 774–76. http://dx.doi.org/10.5951/mtlt.2020.0233.

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Esmaeilzadeh, Hadi, and Jongse Park. "Machine Learning Acceleration." IEEE Micro 39, no. 5 (September 1, 2019): 6–7. http://dx.doi.org/10.1109/mm.2019.2934772.

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Huda, Miftakhul. "Managerial Communication System for the Operation of Learning Acceleration Programs." Jurnal Iqra' : Kajian Ilmu Pendidikan 4, no. 2 (December 23, 2019): 168–81. http://dx.doi.org/10.25217/ji.v4i2.521.

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Research on managerial communication in special classes was essential because it has unique characteristics and need specialized administrative too. This study aimed to find out the mixture of communication in the managerial communication system for the implementation of accelerated classes at State Senior High School 3 Surakarta. The type of this research was descriptive qualitative. The research subjects consisted of the school principals, the administrators of the acceleration program, and the parents of accelerated students at State Senior High School 3 Surakarta. The Data collection techniques in this study used interview, observation, and documentation techniques. The mixture of communication, which was consists of four components, those are advertising (socialization), personal sales, sales promotion, and public relations contained in the managerial communication system for the acceleration of learning programs. There were three forms of socialization programs for accelerating programs; visiting schools, maximizing brochures and web utilization. Types of socialization itself include selective demand advertising and indirect socialization. Based on the school's potential, a socialization model can be developed in the form of making school magazines, parent/guardian forums, and holding event activities. Keywords: Managerial Communication System, Operation of Learning Acceleration Programs

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Chambers, Phil. "Accelerate your learning." Industrial and Commercial Training 37, no. 6 (October 2005): 314–18. http://dx.doi.org/10.1108/00197850510617604.

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RADWAN, ELSAYED, and EIICHIRO TAZAKI. "ROUGH SETS AND GENETIC ALGORITHMS IN LEARNING CELLULAR NEURAL NETWORKS CLONING TEMPLATE FOR DECISION MAKING SYSTEM." International Journal of Neural Systems 14, no. 01 (February 2004): 57–68. http://dx.doi.org/10.1142/s0129065704001851.

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We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

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Tahini, Imad, and Aliaksei Dadykin. "Techniques of new application for acquisition foreign languages: development stages and modern trend." ITM Web of Conferences 27 (2019): 06001. http://dx.doi.org/10.1051/itmconf/20192706001.

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In the conditions of scientific and technical progress and more developing international contacts in the field of science, technology, and culture, the practical knowledge of a foreign language becomes one of the most important characteristics of a highly qualified specialist of any profile. One of the important factors in the development of the system to accelerating learning is the demands of adults themselves. Therefore, we need to create an automated system fast and effective teaching another language that uses the mini-max criterion, which provides the maximum possible result to the minimum required amount of effort on the part of the student. The essence of the approach is to replace the description in our native language with visual objects using the latest technology for implementing this approach. This means that an intermediate language is introduced, not tied to any of the known languages. The Purpose of the research is to develop a methodology (technique) for applying the Visual Approach in the educational environment created on the basis of a distributed management system to accelerate the acquisition of foreign language skills by adults. The system provides the process of controlling the formation of speech skills to a threshold level, allowing the transition from language learning to its improvement and acceleration. On the basis of this new generation using continuous evaluation of the competency level of the trainee, visual models, and information technology, got them a new incarnation, opens a new direction in construction a new version of Learning Management System - E-AMS.

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Rajkumar, R., Arnav Kaushal, and Aishik Saha. "Accelerating Machine Learning Research Using Transfer Learning." Indian Journal of Computer Science 3, no. 2 (April 1, 2018): 7. http://dx.doi.org/10.17010/ijcs/2018/v3/i2/123212.

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Biookaghazadeh, Saman, Pravin Kumar Ravi, and Ming Zhao. "Toward Multi-FPGA Acceleration of the Neural Networks." ACM Journal on Emerging Technologies in Computing Systems 17, no. 2 (April 2021): 1–23. http://dx.doi.org/10.1145/3432816.

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High-throughput and low-latency Convolutional Neural Network (CNN) inference is increasingly important for many cloud- and edge-computing applications. FPGA-based acceleration of CNN inference has demonstrated various benefits compared to other high-performance devices such as GPGPUs. Current FPGA CNN-acceleration solutions are based on a single FPGA design, which are limited by the available resources on an FPGA. In addition, they can only accelerate conventional 2D neural networks. To address these limitations, we present a generic multi-FPGA solution, written in OpenCL, which can accelerate more complex CNNs (e.g., C3D CNN) and achieve a near linear speedup with respect to the available single-FPGA solutions. The design is built upon the Intel Deep Learning Accelerator architecture, with three extensions. First, it includes updates for better area efficiency (up to 25%) and higher performance (up to 24%). Second, it supports 3D convolutions for more challenging applications such as video learning. Third, it supports multi-FPGA communication for higher inference throughput. The results show that utilizing multiple FPGAs can linearly increase the overall bandwidth while maintaining the same end-to-end latency. In addition, the design can outperform other FPGA 2D accelerators by up to 8.4 times and 3D accelerators by up to 1.7 times.

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Zemzem, Wiem, and Moncef Tagina. "A Novel Exploration/Exploitation Policy Accelerating Learning in Both Stationary and Non-Stationary Environment Navigation Tasks." International Journal of Computer and Electrical Engineering 7, no. 3 (2015): 149–58. http://dx.doi.org/10.17706/ijcee.2015.7.3.149-158.

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Webster, Patty D., Sidhartha Deka, Anisa Ismail, Amy F. Stern, and Pierre M. Barker. "Using a Multicountry Learning Network to Harvest and Rapidly Spread Implementation Knowledge across Programs Aimed to Reduce Mother-to-Child Transmission of HIV and Improve Nutrition: Perspectives and Lessons Learned for Similar Large-Scale Initiatives." Journal of the International Association of Providers of AIDS Care (JIAPAC) 18 (January 1, 2019): 232595821984745. http://dx.doi.org/10.1177/2325958219847452.

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As countries pursue UNAIDS’s 90-90-90 target for ending the AIDS epidemic, success is dependent on learning how to deliver effective care. We describe a learning network and mechanisms used to foster communication and sharing of ideas and results across 6 countries in the Partnership for HIV-Free Survival. The network used 2 forms of peer exchange, in-person and virtual, and a variety of knowledge management mechanisms to harvest and spread key learning. Key learning included valuable insights on how to design and convene a multicountry learning network, including top enablers of success and practical insights on the network’s value. The network was instrumental in accelerating learning about improving care. Our experience shows the value of creating a quality improvement–driven, multicountry learning network to accelerate the pace of improving care systems. Government ownership and adaptation of collaborative learning efforts to the country context must be considered when designing future networks.

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Manukian, Haik, Fabio L. Traversa, and Massimiliano Di Ventra. "Accelerating deep learning with memcomputing." Neural Networks 110 (February 2019): 1–7. http://dx.doi.org/10.1016/j.neunet.2018.10.012.

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Katerynych, L., M. Veres, and E. Safarov. "Neural networks’ learning process acceleration." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2020): 313–21. http://dx.doi.org/10.15407/pp2020.02-03.313.

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This study is devoted to evaluating the process of training of a parallel system in the form of an artificial neural network, which is built using a genetic algorithm. The methods that allow to achieve this goal are computer simulation of a neural network on multi-core CPUs and a genetic algorithm for finding the weights of an artificial neural network. The performance of sequential and parallel training processes of artificial neural network is compared.

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Moshovos, Andreas, Jorge Albericio, Patrick Judd, Alberto Delmas Lascorz, Sayeh Sharify, Tayler Hetherington, Tor Aamodt, and Natalie Enright Jerger. "Value-Based Deep-Learning Acceleration." IEEE Micro 38, no. 1 (January 2018): 41–55. http://dx.doi.org/10.1109/mm.2018.112130309.

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Yulianingsih, Wiwin, Gunarti Dwi Lestari, Utari Dewi, and Rezka Arina Rahma. "Learning Society in Accelerating the Increase of English Understanding and Communicating at Mahesa Putri English Camp." Journal of Nonformal Education 7, no. 1 (February 28, 2021): 39–46. http://dx.doi.org/10.15294/jne.v7i1.26481.

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Camp is one form of non-formal education that is used as a place to live while undergoing language courses. This study is generally aimed at determining the learning society in accelerating the improvement of understanding skills and communication skills in a camp-based English course for students in Mahesa Putri Camp in English Village. The purpose of this study is to determine the role of camp as an enhancer of understanding ability, and the role of camp as an acceleration of communication patterns. The research method used is descriptive qualitative which describes something objectively, based on data analysis conducted at the time of data collection and after data collection. Data collection techniques used were observation, interviews and documentation. In this study, the subjects of the study were managers, tutors and students at Mahesa Putri Camp English Village Pare, Kediri. From the results of the study, it was found that the students experienced an increase in understanding of learning and motivation from activities in the campThe importance of this research to provide and understanding of the existence of camp provides an acceleration in learning english.

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Torres Valdés, Rosa María, Carolina Lorenzo Álvarez, Javier Castro Spila, and Alba Santa Soriano. "Relational university, learning and entrepreneurship ecosystems for sustainable tourism." Journal of Science and Technology Policy Management 10, no. 4 (October 16, 2019): 905–26. http://dx.doi.org/10.1108/jstpm-03-2018-0032.

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Purpose This paper aims to offer a conceptual model for the development of a teaching–learning experiential ecosystem which favors university entrepreneurship in the area of sustainable tourism and innovation acceleration. Design/methodology/approach The methodology is carried out in two phases. First, participatory action research in the context of the “Protocol and Event Organization” subject, included in the Tourism degree taught at the University of Alicante, analyzing a didactic experience as a case study. Second, primary qualitative and quantitative research. Findings Although at a small scale, it is shown that the relational approach for university management fosters entrepreneurship and generates social awareness regarding the sustainable tourism business. Research limitations/implications This is a local simulatory case on relational university and is based on only one subject. However, as a starting point, it has the following implications. Practical implications The authors contribute in a practical manner to model a learning and entrepreneurship ecosystem for configuring an education innovation agenda in the field of tourism, with the cooperation of all agents involved in this sector to accelerate novel entrepreneurships, as well as transitions, toward sustainability. Social implications The model is useful to accelerate new entrepreneurship and transitions toward sustainable regional development. Originality/value Students have led the process and have become quintessential relationatory agents, creating their own career opportunities on sustainable tourism, in addition to creating society–university links.

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Lee, Jeong-Woo, and Jun-Ho Oh. "Hybrid Learning of Mapping and its Jacobian in Multilayer Neural Networks." Neural Computation 9, no. 5 (July 1, 1997): 937–58. http://dx.doi.org/10.1162/neco.1997.9.5.937.

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There are some learning problems for which a priori information, such as the Jacobian of mapping, is available in addition to input-output examples. This kind of information can be beneficial in neural network learning if it can be embedded into the network. This article is concerned with the method for learning the mapping and available Jacobian simultaneously. The basic idea is to minimize the cost function, which is composed of the mapping error and the Jacobian error. Prior to developing the Jacobian learning rule, we develop an explicit and general method for computing the Jacobian of the neural network using the parameters computed in error backpropagation. Then the Jacobian learning rule is derived. The method is simpler and more general than tangent propagation (Simard, Victorri, Le Cun, & Denker, 1992). Through hybridization of the error backpropagation and the Jacobian learning, the hybrid learning algorithm is presented. The method shows good performance in accelerating the learning speed and improving generalization. And through computer experiments, it is shown that using the Jacobian synthesized from noise-corrupted data can accelerate learning speed.

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Snir, Sagi, and Ben Yohay. "Prokaryotic evolutionary mechanisms accelerate learning." Discrete Applied Mathematics 258 (April 2019): 222–34. http://dx.doi.org/10.1016/j.dam.2018.10.044.

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Lee, Young-Ah, and Tae-Choong Chung. "Function Approximation for accelerating learning speed in Reinforcement Learning." Journal of Korean Institute of Intelligent Systems 13, no. 6 (December 1, 2003): 635–42. http://dx.doi.org/10.5391/jkiis.2003.13.6.635.

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Price, B., and C. Boutilier. "Accelerating Reinforcement Learning through Implicit Imitation." Journal of Artificial Intelligence Research 19 (December 1, 2003): 569–629. http://dx.doi.org/10.1613/jair.898.

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Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

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Juan, Yongfei, Yongbing Dai, Yang Yang, and Jiao Zhang. "Accelerating materials discovery using machine learning." Journal of Materials Science & Technology 79 (July 2021): 178–90. http://dx.doi.org/10.1016/j.jmst.2020.12.010.

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Chun, Il Yong, and Jeffrey A. Fessler. "Convolutional Dictionary Learning: Acceleration and Convergence." IEEE Transactions on Image Processing 27, no. 4 (April 2018): 1697–712. http://dx.doi.org/10.1109/tip.2017.2761545.

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Politis, Diamanto, Jonas Gabrielsson, Nataliya Galan, and Solomon Akele Abebe. "Entrepreneurial learning in venture acceleration programs." Learning Organization 26, no. 6 (September 9, 2019): 588–603. http://dx.doi.org/10.1108/tlo-04-2018-0082.

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Purpose This study aims to better understand entrepreneurial learning in the context of venture acceleration programs. Design/methodology/approach A qualitative research strategy was used based on multiple in-depth interviews with 21 lead entrepreneurs complemented with participatory observations and secondary sources. The data were inductively analysed following the Gioia methodology (Gioia et al., 2012). Findings The authors build on experiential learning theory to generate a process-focussed model exploring the learning dynamics that venture acceleration programs can facilitate. In this model, the authors identify three catalysts that trigger processes of experiential learning and two contingencies that alleviate the effects of the catalysts on learning outcomes. The findings suggest that the potential of venture acceleration programs to be effective learning environments pends on the presence and quality of these catalysts and contingencies. Originality/value The findings provide novel insights on how venture acceleration programs trigger entrepreneurial learning, thereby offering a deeper understanding of the learning dynamics in this setting.

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Wang, Junxiong, Hongzhi Wang, Chenxu Zhao, Jianzhong Li, and Hong Gao. "Iteration acceleration for distributed learning systems." Parallel Computing 72 (February 2018): 29–41. http://dx.doi.org/10.1016/j.parco.2018.01.001.

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Yu, Haishan, and Jun Jiang. "Accelerating catalysts design by machine learning." Science Bulletin 65, no. 19 (October 2020): 1593–94. http://dx.doi.org/10.1016/j.scib.2020.06.004.

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Younger, Jon, René Sorensen, Christine Cleemann, Aaron Younger, Allan Freed, and Sanne Moller. "Accelerating strategic change through action learning." Strategic HR Review 12, no. 4 (June 14, 2013): 177–84. http://dx.doi.org/10.1108/shr-02-2013-0013.

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Jonathan Fainberg*, Rand Wilcox Vanden Berg, Gregory Chesnut, Taehyoung Lee, Jonathan Coleman, Timothy Donahue, Alvin Goh, et al. "MP83-01 ACCELERATING THE LEARNING CURVE." Journal of Urology 203 (April 2020): e1255. http://dx.doi.org/10.1097/ju.0000000000000975.01.

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Khailany, Brucek, Haoxing Ren, Steve Dai, Saad Godil, Ben Keller, Robert Kirby, Alicia Klinefelter, et al. "Accelerating Chip Design With Machine Learning." IEEE Micro 40, no. 6 (November 1, 2020): 23–32. http://dx.doi.org/10.1109/mm.2020.3026231.

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Bøhn, Eivind, Signe Moe, and Tor Arne Johansen. "Accelerating Reinforcement Learning with Suboptimal Guidance." IFAC-PapersOnLine 53, no. 2 (2020): 8090–96. http://dx.doi.org/10.1016/j.ifacol.2020.12.2278.

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Kartini, Justina Endang, and Bambang Ismanto. "MANAJEMEN PROGRAM AKSELERASI DI SMP PL DOMENICO SAVIO SEMARANG." Kelola: Jurnal Manajemen Pendidikan 2, no. 1 (June 7, 2015): 12. http://dx.doi.org/10.24246/j.jk.2015.v2.i1.p12-21.

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<p>This study aimed to describe the management acceleration program in terms of planning, organizing, actuating, and monitoring learning in PL Domenico Savio Junior High School. This research is a qualitative descriptive study. Subjects were principal, manager acceleration, acceleration secretary, and homeroom teacher. Results of research: Planning accelerated program in PL Domenico Savio Junior High School done by preparing teachers, finance, infrastructure, learners, curriculum, time, and high school. Learning plan covering the syllabus and lesson plan contains learning objectives, teaching materials, teaching methods, learning resources, and assessment of learning outcomes. Organization of human resources contained in the organizational structure of the school is equipped manager acceleration. Organizing accelerated learning includes the step before learning, implementation, and after learning. Teachers organize learning materials to learners’ learning time. Acceleration managers manage learning activities acceleration in school and outside of school. Actuating of accelerated program refers to the RPP and half of each subject program. Implementation of the learning stages: introduction, core activities, and cover. Monitoring the acceleration of learning is done directly by the students, parents of students, and managers acceleration. Learners and parents provide input on school. Manager acceleration control feasibility study. Principal using monitoring results into training materials for teachers. Accelerated learning assessment carried out by the teacher. Teacher evaluations are conducted by the principal and foundations. Reporting accelerated learning is done on the parents of learners.</p>

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Paenke, Ingo, Tadeusz J. Kawecki, and Bernhard Sendhoff. "The Influence of Learning on Evolution: A Mathematical Framework." Artificial Life 15, no. 2 (April 2009): 227–45. http://dx.doi.org/10.1162/artl.2009.15.2.15204.

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The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.

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Son, Jaewon, Yonghyuk Yoo, Khu-rai Kim, Youngjae Kim, Kwonyong Lee, and Sungyong Park. "A GPU Scheduling Framework to Accelerate Hyper-Parameter Optimization in Deep Learning Clusters." Electronics 10, no. 3 (February 2, 2021): 350. http://dx.doi.org/10.3390/electronics10030350.

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This paper proposes Hermes, a container-based preemptive GPU scheduling framework for accelerating hyper-parameter optimization in deep learning (DL) clusters. Hermes accelerates hyper-parameter optimization by time-sharing between DL jobs and prioritizing jobs with more promising hyper-parameter combinations. Hermes’s scheduling policy is grounded on the observation that good hyper-parameter combinations converge quickly in the early phases of training. By giving higher priority to fast-converging containers, Hermes’s GPU preemption mechanism can accelerate training. This enables users to find optimal hyper-parameters faster without losing the progress of a container. We have implemented Hermes over Kubernetes and compared its performance against existing scheduling frameworks. Experiments show that Hermes reduces the time for hyper-parameter optimization up to 4.04 times against previously proposed scheduling policies such as FIFO, round-robin (RR), and SLAQ, with minimal time-sharing overhead.

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Refonaa, J., Bandaru Suhas, B. V. S. Bhaskar, S. L. JanyShabu, S. Dhamodaran, Sardar Maran, Maria Anu, and M. Lakshmi. "Fall Detection and Daily Living Activity Recognition Logic Regression." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3520–25. http://dx.doi.org/10.1166/jctn.2020.9223.

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It is a must to bring the fall detection system in to use with the increasing number of elder people in the world, because the most of them tend live voluntarily and at risk of injuries. Falls are dangerous in a few cases and could even lead to deadly injuries. A very robust fall detection system must be built in order to counter this problem. Here, we establish fall detection and recognition of daily live behavior through machine learning system. In order to detect different types of activities, including the detection of falls and day to-day activities, We use 2 shared archives for the accelerating and lateral speed data during this development. Logistic regression is used to determine motions such as drop, walk, climb, sit, stand and lie bases on the accelerating data and data on angular velocities. More specifically, the triaxial acceleration average value is used to achieve fall detection accuracy.

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Kellman, Philip J., and Sally Krasne. "Accelerating expertise: Perceptual and adaptive learning technology in medical learning." Medical Teacher 40, no. 8 (August 3, 2018): 797–802. http://dx.doi.org/10.1080/0142159x.2018.1484897.

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Yu, X. H. "Acceleration of backpropagation learning using optimised learning rate and momentum." Electronics Letters 29, no. 14 (1993): 1288. http://dx.doi.org/10.1049/el:19930860.

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