Academic literature on the topic 'Adapted learning methods'
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Journal articles on the topic "Adapted learning methods"
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Özöğür Akyüz, Süreyya, Gürkan Üstünkar, and Gerhard Wilhelm Weber. "Adapted Infinite Kernel Learning by Multi-Local Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 04 (April 12, 2016): 1651004. http://dx.doi.org/10.1142/s0218001416510046.
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The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.
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Gazzola, Mattia, Babak Hejazialhosseini, and Petros Koumoutsakos. "Reinforcement Learning and Wavelet Adapted Vortex Methods for Simulations of Self-propelled Swimmers." SIAM Journal on Scientific Computing 36, no. 3 (January 2014): B622—B639. http://dx.doi.org/10.1137/130943078.
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Khalifa, Amal, and Anthony Guzman. "Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques." Symmetry 14, no. 7 (June 27, 2022): 1325. http://dx.doi.org/10.3390/sym14071325.
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Digital image steganography is the process of embedding information within a cover image in a secure, imperceptible, and recoverable way. This research extends a symmetry-adapted deep-learning approach to identify hidden patterns of images using two-dimensional convolutional neural networks (CNN). The proposed method (SteGuz) uses three CNNs to implement the different phases of the steganography process on digital image covers. SteGuz introduced a gain function, based on several image similarity metrics, to maximize the imperceptibility of the hiding process. Using 10 different pairs of cover-secret images, the performance of the proposed method was measured in terms of standard metrics such as peak signal to noise ratio (PSNR) and structured similarity index measurement (SSIM). The results showed that the proposed methodology outperformed the original method in terms of both imperceptibility and recoverability. In addition, when compared with some existing methods, SteGuz proved the outstanding performance of achieving a very high PSNR value while maintaining high accuracy of the extracted image.
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Albashish, Dheeb. "Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images." PeerJ Computer Science 8 (July 5, 2022): e1031. http://dx.doi.org/10.7717/peerj-cs.1031.
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Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to the limited sample sizes and heterogeneity in tumor presentation in medical images, CNN models suffer from training issues, including training from scratch, which leads to overfitting. Alternatively, a pre-trained neural network’s transfer learning (TL) is used to derive tumor knowledge from medical image datasets using CNN that were designed for non-medical activations, alleviating the need for large datasets. This study proposes two ensemble learning techniques: E-CNN (product rule) and E-CNN (majority voting). These techniques are based on the adaptation of the pretrained CNN models to classify colon cancer histopathology images into various classes. In these ensembles, the individuals are, initially, constructed by adapting pretrained DenseNet121, MobileNetV2, InceptionV3, and VGG16 models. The adaptation of these models is based on a block-wise fine-tuning policy, in which a set of dense and dropout layers of these pretrained models is joined to explore the variation in the histology images. Then, the models’ decisions are fused via product rule and majority voting aggregation methods. The proposed model was validated against the standard pretrained models and the most recent works on two publicly available benchmark colon histopathological image datasets: Stoean (357 images) and Kather colorectal histology (5,000 images). The results were 97.20% and 91.28% accurate, respectively. The achieved results outperformed the state-of-the-art studies and confirmed that the proposed E-CNNs could be extended to be used in various medical image applications.
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Ding, Alison, Jiacheng Wang, and Yingjie Hang. "Efficient Sensor Calibration Via Machine Learning-Based Resampling Methods." ECS Transactions 109, no. 15 (September 30, 2022): 3–15. http://dx.doi.org/10.1149/10915.0003ecst.
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A sensor’s performance relies on the calibration model functionally relating the sensor’s response (denoted as y) to the target analyte concentration (denoted as x). However, classic statistical methods are not able to adequately quantify the uncertainty of the x concentration estimates. In this work, a machine learning (ML)-based computational resampling method was adapted to address the uncertainty quantification issues, based on which a two-stage procedure was developed for efficient sampling and modeling of the calibration data obtained via laboratory experiments. The outputs of the procedure allow for not only a single-value estimate, but also a high-likelihood range for the underlying analyte concentration in an unknown sample being tested. The calibration procedure was applied on a simulated biosensor, and evaluated through large amount of validation data. The sensor simulator was developed from a paper-based lateral flow strip (PLFS) designed for traumatic brain injury (TBI) diagnosis.
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Wang, Yu, Mengru Sun, and Yifan Duan. "Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data." Computational and Mathematical Methods in Medicine 2021 (November 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/8008731.
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The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic methods in recent years. In this paper, machine learning methods are used to analyze metagenomic sequencing data of acne, i.e., all kinds of lipids in the face skin. Firstly, lipids data of the diseased skin (DS) samples and the healthy skin (HS) samples of acne patients and the normal control (NC) samples of healthy person are, respectively, analyzed by using principal component analysis (PCA) and kernel principal component analysis (KPCA). Then, the lipids which have main influence on each kind of sample are obtained. In addition, a multiset canonical correlation analysis (MCCA) is utilized to get lipids which can differentiate the face skins of the above three samples. The experimental results show the machine learning methods can effectively analyze metagenomic sequencing data of acne. According to the results, lipids which only influence one of the three samples or the lipids which simultaneously have different degree of influence on these three samples can be used as indicators to judge skin statuses.
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Kesuma, Dytchia Septi, Zakirman Zakirman, Ade Usra Berli, Mira Meilisa, and Chichi Rahayu. "Funscience Methods to Improve Student Activeness in Science Learning." EDUKATIF : JURNAL ILMU PENDIDIKAN 4, no. 1 (December 27, 2021): 530–38. http://dx.doi.org/10.31004/edukatif.v4i1.1759.
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The new challenge faced in carrying out IPA learning in elementary school, especially in SDN 01 Kubang Siamang Bunyi is to increase student activeness and involvement. The aim of this research is to improve student liveliness and engagement through Funscience methods in IPA learning. Fun science is a method by using simple experiments that are interestingly packaged and cause fun effects for students in IPA learning. This type of research is descriptive, involving student respondents in SDN 01 Kubang Siamang Bunyi Regency 50 cities of West Sumatra Province. Data collection instruments in the form of observation sheets and interviews. Data is analyzed descriptively. The results of the data analysis showed that the application of Funscience methods improved the activeness of students in IPA learning in SDN 01 Kubang Siamang Bunyi Kenagarian Kubang Regency 50 City. Funscience can be one of the alternative methods that can be applied in IPA learning to improve and improve the quality of learning. Funscience is a method of learning that has been adapted to the characteristics of students in elementary school.
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Mularsih, Heni, and Hartini. "The Effectiveness of Cyber Learning Methods to Improve Learning Motivation in Interior Design Courses." Journal of Computational and Theoretical Nanoscience 17, no. 2 (February 1, 2020): 1147–55. http://dx.doi.org/10.1166/jctn.2020.8779.
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The Interior Design courses emphasizing accuracy requires high learning motivation. One stimulus to grow motivation is by applying teaching methods that are in accordance with the characteristics of the course, namely cyber learning methods that also facilitate accuracy and efficiency. The purpose of this research is to improve learning motivation in interior design courses through cyber learning methods employing steps of project-based learning (PjBL). The study included quasiexperiments using two-group post test design. The instrument of learning motivation was adapted from McClelland’s motivational theory. The number of participants was 60 students divided in two groups. The first students group learned by using conventional methods (control group), while the second students group learned by using cyber methods (experimental group). The number of learning was 10 meetings. After completing the meetings, the two groups were measured based on the learning motivation. The results of the study indicate that cyber learning methods can increase student’s learning motivation in interior design courses. Result shows that the experimental group of students has higher learning motivation than students in control group who were leaved uninterfered with the conventional method—the lectures.
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Krissandi, Apri Damai Sagita. "THE DEVELOPMENT OF BOOK: APPROACHES, METHODS, AND LEARNING MODELS FOR LEARNING INDONESIAN IN PRIMARY SCHOOL." NATURALISTIC : Jurnal Kajian Penelitian Pendidikan dan Pembelajaran 2, no. 1 (October 26, 2017): 62–76. http://dx.doi.org/10.35568/naturalistic.v2i1.101.
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This study aims to develop book approach, method, and model of learning Indonesian. This type of research includes research development (Research and Development). This study aims to develop and produce a specific product and test the validity of the resulting product. The development model used is a development model adapted from Borg and Gall development research steps (1983: 775). The development procedure is through the following stages: (1) determining the study of competency standards and learning materials, (2) needs analysis, (3) producing books, (4) product validation, and (5) revision of the final product. From the results of the recapitulation score, obtained the following details: 1) the score of the media expert is 4.7 with very good criteria; 2) score of Indonesian lecturers 4.8 with very good criteria; and 3) the mean of student validation score is 4.9 with very good criteria. Therefore, it can be concluded that the book approach, method, and model of learning Indonesian in primary school get an average score of 4.8 with the category of "Very Good".
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Satrya, Wahyu Fadli, and Ji-Hoon Yun. "Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression." Sensors 23, no. 2 (January 4, 2023): 583. http://dx.doi.org/10.3390/s23020583.
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For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases.
Dissertations / Theses on the topic "Adapted learning methods"
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Gordon, Christopher John. "Encouraging the Development of Deeper Learning and Personal Teaching Efficacy: Effects of Modifying the Learning Environment in a Preservice Teacher Education Program." Thesis, The University of Sydney, 2000. http://hdl.handle.net/2123/511.
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Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.
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Gordon, Christopher John. "Encouraging the Development of Deeper Learning and Personal Teaching Efficacy: Effects of Modifying the Learning Environment in a Preservice Teacher Education Program." University of Sydney. Development and Learning, 2000. http://hdl.handle.net/2123/511.
Full textAbstract:
Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.
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Smith, Patrick Steven. "Learning to Adapt: Online Social Science Instruction in Higher Education." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/1089.
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Online learning is a rapidly growing phenomenon in post-secondary education. Institutions of higher learning have embraced online learning for its perceived merits, but without the consideration of how instructors deal with this different learning medium. Little is known of the extent to which different disciplines are suited to the online medium; this is pertinent to disciplines that rely on spontaneous in-person discussion. Furthermore, as colleges continue to invest heavily in online learning, instructors who only possess face-to-face teaching experience may begin teaching online. This poses a pedagogical challenge for instructors who are unfamiliar with the medium. This qualitative, in-depth interview study with ten social science instructors elucidates the process of transition from face-to-face teaching to online teaching. Through grounded analysis, a few key themes emerged. Respondents explain that teaching in the online classroom is qualitatively different from teaching in-person. The asynchronisity of the online classroom - which means students do not "meet", discuss, or learn at the same time - is a subtle yet significant difference between the two mediums. The asynchronous classroom means instructors relinquish control of when and where students will engage in study and discussion, and this requires students to have especially high self-regulatory skills. Respondents also explained that their online courses were several times larger than their in-person ones, with some courses allowing over twice as many students as an in-person course. Consequently, instructors must find new ways to approach teaching in the online medium. This pitfall of relying on old, obsolete methods in the online medium can be avoided if instructors are provided with the peer and pedagogical support of their professional peers, and access to teaching assistants to manage the greater time commitment of teaching online. In order to have a positive experience, online teachers must be willing to take on an intellectual challenge that may defy how they perceive themselves and their role in higher education. If instructors are open to a new intellectual challenge and possess the proper resources, they will become committed to teaching online and perceive the advantages of the medium to outweigh the disadvantages.
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Zelbienė, Kristina. "Lietuvių kalbos mokytojų požiūris į modifikuotų ir adaptuotų programų rengimą ir taikymą bendrojo lavinimo mokykloje." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_160048-08941.
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Darbe atlikta teorinė integruoto (inkliuzinio) ugdymo sampratos, probleminių klausimų, ugdymo individualizavimo funkcijų bendrojo lavinimo mokykloje analizė.
Iškelta hipotezė, kad modifikuotų ir adaptuotų programų rengimas bendrojo lavinimo mokyklose mokytojams mažai praktine patirtimi paremta sritis, kurią reikia tobulinti. Programų rengimo mechanizmas yra nebaigtas ir nuolatos tobulinamas.
Anketinės apklausos metodu buvo atliktas tyrimas, kurio tikslas - atskleisti ir įvertinti adaptuotų ir modifikuotų programų taikymo bendrojo lavinimo mokykloje efektyvumo veiksnius, atkreipti dėmesį į pačią problemą, t.y. galbūt svarbiau ne programa, o mokymo kokybė, ar nesureikšminama programa, dėmesį atitraukiant nuo ugdymo proceso kokybės. Gautų tyrimo rezultatų analizė atlikta taikant aprašomosios statistikos metodus: skaičiuotas procentinis pasiskirstymas, aritmetinis vidurkis, reitingai, atlikta koreliacinė analizė. Statistinėms hipotezėms tikrinti taikytas chi-kvadrato kriterijus.
Tyrime dalyvavo 111 bendrojo lavinimo mokyklų lietuvių kalbos ir pradinių klasių mokytojai.
Empirinėje dalyje nagrinėjamas modifikuotų ir adaptuotų programų sudarymas, mokymo metodų taikymas, atliktas specialiųjų ugdymosi poreikių turinčių mokinių vertinimas, aptariami organizaciniai adaptuotų bei modifikuotų lietuvių kalbos programų rengimo klausimai ir problemos.
Svarbiausios empirinio tyrimo išvados:
1. Pagrindiniai sunkumai, susiję su ugdymo turinio individualizavimu ir diferencijavimu: mokytojams... [toliau žr. visą tekstą]In the paper the theoretical analysis of integrated (inclusive) education and problem questions, as well as analysis of education individualization functions in the mainstream school are performed. Hypothesis is set, that preparation of modified and adapted curricula in the mainstream schools is the area little supported by the teachers’ practical experience which is to be improved. Curricula preparation mechanism is incomplete and undergoes improvement. With the means of questioning survey a research was performed, sought to disclose and to assess the efficiency factors of adapted and modified curricula application in the mainstream school, to pay attention at the same problem, i.e. probably teaching quality is more important that the curriculum, whether the curriculum isn’t given prominence, taking the attention away from the education process quality. Analysis of the results obtained was performed using the descriptive statistical methods: percentage distribution, arithmetical means, ratings were calculated, correlation analysis was done. To check the statistical hypotheses, chi-square criterion was employed. 111 teachers of Lithuanian language and primary forms from mainstream schools participated in the survey. In the empirical part modified and adapted curriculum compilation, application of teaching methods are analysed, evaluation of the pupils with special needs is performed, organization questions and problems of modified and adapted Lithuanian language... [to full text]
Books on the topic "Adapted learning methods"
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Huettig, Carol. Gross motor activities for young children with special needs: A supplement to: Auxter/Pfyer/Huettig, Principles and methods of adapted physical education and recreation. 9th ed. Bostan, Mass: McGraw Hill, 2001.
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Huettig, Carol. Gross motor activities for young children with special needs: A supplement to: Auxter/Pyfer/Huettig, Principles and methods of adapted physical education and recreation, 9th edition. Boston: McGraw-Hill, 2001.
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Jean, Pyfer, and Auxter David, eds. Gross motor activities for young children with special needs: A supplement to Auxter/Pyfer/Huettig Principles and methods of adapted physical education and recreation, tenth edition. New York, N.Y: McGraw-Hill, 2005.
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Federighi, Paolo, and Francesca Torlone, eds. SMOC-Soft Open Method of Coordination from Prevalet. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6453-246-2.
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Seven European Regional Governments, together with Earlall and universities and research centres, have undertaken to refine a first draft of a Joint Progress Report of Regions on Implementation of European Lifelong Learning Strategies in the perspective of the Europe 2020 Strategy. The purpose of this exercise is to demonstrate the usefulness and need to make an instrument available to European Regional Governments that helps to understand and evaluate the educational conditions of the regional population when compared with other regions. The Report shows how, over the decade, all the Regional Governments have undertaken to adapt training systems to the demographic dynamics characterised by the general ageing of the population and by migratory phenomena. Simultaneously the Report also shows the need for urgent improvements in some fields where the impact of policies is still too modest and where the risk of stagnation must be dealt with (for example, lifelong learning policies and NEET reduction).
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Federighi, Paolo, and Francesca Torlone, eds. SMOC – Sanfte offene Koordinierungsmethode – Soft Open Method of Coordination von Prevalet. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6453-263-9.
Full textAbstract:
Seven European Regional Governments, together with Earlall and universities and research centres, have undertaken to refine a first draft of a Joint Progress Report of Regions on Implementation of European Lifelong Learning Strategies in the perspective of the Europe 2020 Strategy. The purpose of this exercise is to demonstrate the usefulness and need to make an instrument available to European Regional Governments that helps to understand and evaluate the educational conditions of the regional population when compared with other regions. The Report shows how, over the decade, all the Regional Governments have undertaken to adapt training systems to the demographic dynamics characterised by the general ageing of the population and by migratory phenomena. Simultaneously the Report also shows the need for urgent improvements in some fields where the impact of policies is still too modest and where the risk of stagnation must be dealt with (for example, lifelong learning policies and NEET reduction).
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Nagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.
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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. This book examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, the book discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. The book presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
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Pyfer, Jean, Carol I. Huettig, Carol Huettig, and David Auxter. Gross Motor Activities for Small Children with Special Needs to accompany Principals and Methods of Adapted PE and Recreation. McGraw-Hill Humanities/Social Sciences/Languages, 2004.
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Klugman, Craig M., and Erin Gentry Lamb, eds. Research Methods in Health Humanities. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190918514.001.0001.
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Research Methods in Health Humanities surveys the diverse and unique research methods used by scholars in the growing transdisciplinary field of health humanities. Appropriate for advanced undergraduates but nuanced enough to engage more seasoned students and scholars, this volume is an essential teaching and reference tool for health humanities teachers and scholars. Health humanities is a field united by its commitment to social justice; it recognizes the importance of applying expertise to real-world concerns and of creating research that translates back in meaningful and useful ways to participants and communities. The chapters in this field-defining volume reflect these values through research approaches to examining the human aspects of health and healthcare that are critical, reflective, textual, contextual, qualitative, and quantitative. Divided into four sections, the volume demonstrates how to conduct research on texts, contexts, people, and programs. Readers will find research methods from traditional disciplines adapted to health humanities work, such as close reading of diverse texts, archival research, ethnography, interviews, and surveys. The book also features transdisciplinary methods unique to the health humanities, such as health and social justice studies, digital health humanities, and community dialogues. Each chapter provides learning objectives, step-by-step instructions, resources, and exercises, with illustrations of the method provided by the authors’ own research. An invaluable tool in learning, curricular development, and research design, this volume provides a grounding in the traditions of the humanities, fine arts, and social sciences for students considering healthcare careers and also provides useful tools of inquiry for everyone, as we are all future patients and future caregivers of a loved one.
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Hayes, Gillian R. Design, Action, and Practice. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733249.003.0010.
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Action research (AR) is an approach to research that involves engaging with a community to address some problem or challenge, and through this problem-solving, to develop scholarly knowledge. AR is not a method, nor even a suite of methods, but a perspective that makes use of a wide variety of methods. AR is explicitly democratic, collaborative, and interdisciplinary. It focuses on highly contextualized, localized solutions with a greater emphasis on transferability than generalizability. In other words, scholars and community partners work together to develop and learn from solutions that work in a single context; in addition, they collect data that will enable these solutions to be adapted or transferred to other contexts. Most importantly, AR claims that the intervention, the learning, and the doing and knowing cannot be disentangled.
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Ahn, F. Ahn's Practical and Easy Method of Learning the French Language, Tr. and Adapted to English Students. 1St Course. Creative Media Partners, LLC, 2018.
Find full textBook chapters on the topic "Adapted learning methods"
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Lee, Jeonghyun, Farahnaz Soleimani, and Stephen W. Harmon. "Reflecting on a Year of Emergency Remote Teaching." In Global Perspectives on Educational Innovations for Emergency Situations, 169–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99634-5_17.
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AbstractThis chapter reflects on lessons learned from a year of emergency remote teaching at a technology-focused university since the pandemic outbreak in March 2020. We focus on practical implications for supporting faculty in future emergency remote teaching situations based on an understanding of how faculty adapted to this novel phenomenon and the challenges they encountered during the rapid transition. Specifically, we examine faculty members’ perceptions and attitudes toward emergency remote teaching by taking a mixed-methods approach. We conducted a study using data collected from two anonymous online surveys administered in April 2020 and December 2020 as well as case study interviews conducted in May 2020. Our findings suggest that engaging students in interactive discussions or collaborative activities and assessing students’ learning were the areas in which faculty struggled the most persistently across both semesters. Yet, we observed between the two semesters an increasing trend in the proportion of faculty who felt their course was suitable for online instruction. We also observed a similar uptick in faculty who sought instructional resources from within their department and technology experts on campus. Our study findings capture creative and flexible strategies that our instructors have used to overcome these instructional challenges (e.g., re-configuring projects to handle logistical difficulties). We also identify varying needs and other important individual factors that might explain different transitioning experiences among faculty. The chapter concludes by discussing next steps to effectively support faculty members’ instructional practices and rebuild “the hybrid model” of education for the post-pandemic era.
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Sherrah, Jamie. "Learning to Adapt: A Method for Automatic Tuning of Algorithm Parameters." In Advanced Concepts for Intelligent Vision Systems, 414–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17688-3_39.
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Gerostathopoulos, Ilias, and Alexander auf der Straße. "Online Experiment-Driven Learning and Adaptation." In Model-Based Engineering of Collaborative Embedded Systems, 295–303. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62136-0_15.
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AbstractThis chapter presents an approach for the online optimization of collaborative embedded systems (CESs) and collaborative system groups (CSGs). Such systems have to adapt and optimize their behavior at runtime to increase their utilities and respond to runtime situations. We propose to model such systems as black boxes of their essential input parameters and outputs, and search efficiently in the space of input parameters for values that optimize (maximize or minimize) the system’s outputs. Our optimization approach consists of three phases and combines online (Bayesian) optimization with statistical guarantees stemming from the use of statistical methods such as factorial ANOVA, binomial testing, and t-tests in different phases. We have applied our approach in a smart cars testbed with the goal of optimizing the routing of cars by tuning the configuration of their parametric router at runtime.
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Mondaini, Gianluigi, and Marco Rosciani. "Adaptive Environments. New Spaces for Learning." In Makers at School, Educational Robotics and Innovative Learning Environments, 367–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77040-2_49.
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AbstractThe architecture of schools can no longer be as rigid as it has been in the past. It needs to be spatially open, stimulating and, through its physical qualities, able to react and adapt to those who live and work there to use it as an active educational tool. However, architecture by itself is not enough to define new spatial models for education or for our society and its ever more complex problems. Thus, a plurality of forms of disciplinary participation is necessary. Pedagogy, with its close relationship with architecture, and technology, with the innovations it brings to teaching methods, are also involved. The dynamism in communication processes and educational practices that arises from new technologies demands a review of how school environments are organized, along with flexible, multifunctional and adaptable solutions for them. The most interesting models are those in which the conventional classroom “breaks down” physically, in favor of open-space learning environments, and flexible spaces. Such layouts help promote skills acquisition, thanks to the synergy between the technological elements and the physical qualities of the spaces that make learning more engaging.
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Kloetzer, Laure, Jo Wells, Laura Seppänen, and Sarah Hean. "Mentoring in Practice: Rebuilding Dialogue with Mentees’ Stories." In Improving Interagency Collaboration, Innovation and Learning in Criminal Justice Systems, 165–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70661-6_7.
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AbstractThe voluntary and community sector (VCS) is a key player in the support of prisoners and ex-prisoners in the English and Welsh criminal justice system. Organisational learning and innovation is urgently required in this sector to adapt to the current political and economic environment. The chapter describes exploratory efforts to introduce participatory methods drawn from Change Laboratory Methods and Clinics of Activity within a local VCS organisation that would help (re)build dialogue between stakeholders with the aim of promoting organisational learning and innovation. The intervention comprised an ethnographic phase of observing the staff, interviews with 19 key stakeholders, and a final developmental workshop with the staff. The analysis of these data by the researcher (first author) provided insight into the experience of mentors working in the voluntary sector as well as providing a trigger for dialogue in a subsequent workshop that used these data to establish dialogue between staff. These served as dialogical artefacts, introducing micro-dramas in the form of selected user stories. These dialogical artefacts triggered diverse reactions and analyses by the various participants, highlighting different elements than those anticipated by the researcher. We discuss the different readings of our research data by the researcher and staff members, presenting these two contrasting perspectives, and the implications this has for workplace development methods.
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Lien, Yen-Chieh, Rongting Zhang, F. Maxwell Harper, Vanessa Murdock, and Chia-Jung Lee. "Leveraging Customer Reviews for E-commerce Query Generation." In Lecture Notes in Computer Science, 190–98. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99739-7_22.
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AbstractCustomer reviews are an effective source of information about what people deem important in products (e.g. “strong zipper” for tents). These crowd-created descriptors not only highlight key product attributes, but can also complement seller-provided product descriptions. Motivated by this, we propose to leverage customer reviews to generate queries pertinent to target products in an e-commerce setting. While there has been work on automatic query generation, it often relied on proprietary user search data to generate query-document training pairs for learning supervised models. We take a different view and focus on leveraging reviews without training on search logs, making reproduction more viable by the public. Our method adopts an ensemble of the statistical properties of review terms and a zero-shot neural model trained on adapted external corpus to synthesize queries. Compared to competitive baselines, we show that the generated queries based on our method both better align with actual customer queries and can benefit retrieval effectiveness.
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Marcarini, Mariagrazia Francesca. "Pedarchitecture: Which Learning Environments for the Personalisation of Teaching and Learning? An Educational Architecture for the Schools of the Future." In Teacher Transition into Innovative Learning Environments, 85–107. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7497-9_8.
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AbstractThis project investigates how to overcome traditional learning environment’s rigidity; those established practices that may hinder full use of what we might call new learning environments. It addresses how teachers adapt their teaching to changing learning environments, what impact new educational spaces have on teachers and students, how to organise students with different criteria, and how learning environments can be redesigned in old schools with limited investments. The research studies four schools: in Denmark, the Hellerup Folkeskole in Gentofte and the Ørestad Gymnasium in Copenhagen; in Italy, the Enrico Fermi High School in Mantua and IC3 Piersanti Mattarella secondary first grade in Modena. New learning environments are intended to enhance teacher collaboration and stimulate the exchange of new teaching methods, enabling learning personalisation. This is often facilitated by team teaching, which in this chapter is seen as a “bridge-culture” concept, offering a wider vision including structural and organisational details. The chapter discusses how this strategy lead to students improved learning skills, them taking on greater personal responsibility and displaying aptitude to study in different ways. In this sample of “architecture feeds pedagogy” schools, some key concepts are explored that might guide future learning environments design: readability, “semantic-topical”, flexibility, invisible pedagogy and affordances.
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Soui, Makram, Karthik Srinivasan, and Abdulaziz Albesher. "Intelligent Personalized E-Learning Platform using Machine Learning Algorithms." In Machine Learning Methods for Engineering Application Development, 110–26. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9879815079180122010011.
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Personalized learning is a teaching method that allows the content and course of online training to be adapted according to the individual profile of learners. The main task of adaptability is the selection of the most appropriate content for the student in accordance with his digital footprint. In this work, we build a machine learning model to recommend the appropriate learning resources according to the student profile. To this end, we use Sequential forward selection (SFS) as a feature selection technique with AdaBoost as a classifier. The obtained results prove the efficiency of the proposed model with 91.33% of accuracy rate and 91.43% of precision rate.
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Battal, Ali, Halil Kayaduman, and Hamza Polat. "The Use of Gamification Elements in an Online Introduction to Programming Course." In Shaping the Future of Online Learning, 284–300. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6513-4.ch015.
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This study aims to understand the different methods of badge transformation on students' achievement, satisfaction, learning enjoyment, and engagement. An explanatory mixed method study was adapted in this study. A counter-balanced experimental design with no control group was employed for the quantitative phase. Participants were randomly assigned to two equal groups and received the constant and variable-rate badge transformation method in different orders. The paired-sample t-test showed no significant mean difference in students' achievement, satisfaction, learning enjoyment, and engagement in using the constant-rate and variable method. For the qualitative phase, students were asked to explain their opinions about the applied methods. Qualitative findings supported the quantitative results. While some students favored the use of badges and different methods of badge transformation, some students were neither interested in the collection of badges nor their transformation.
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Mateus, Diana, Christian Wachinger, Selen Atasoy, Loren Schwarz, and Nassir Navab. "Learning Manifolds." In Machine Learning in Computer-Aided Diagnosis, 374–402. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0059-1.ch018.
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Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-points, manifold learning algorithms first approximate the low dimensional manifold where data lives with a graph; then, they find a non-linear map to embed this graph into a low dimensional space. Since the explicit pairwise relations and the neighborhood system can be designed according to the application, manifold learning methods are very flexible and allow easy incorporation of domain knowledge. The authors describe different assumptions and design elements that are crucial to building successful low dimensional data representations with manifold learning for a variety of applications. In particular, they discuss examples for visualization, clustering, classification, registration, and human-motion modeling.
Conference papers on the topic "Adapted learning methods"
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Mahtani, Kumar, Luis F. Beites, Jaime Rodríguez, and Carlos Veganzones. "ADAPTED HANDS-ON SELF-LEARNING METHODS THROUGH SIMULATION TO ENHANCE THE DISTANCE LEARNING EXPERIENCE IN ELECTRICAL MACHINES (ELECTRICAL ENGINEERING)." In 15th International Technology, Education and Development Conference. IATED, 2021. http://dx.doi.org/10.21125/inted.2021.0903.
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Machado, Mateus Tarcinalli, Thiago Alexandre Salgueiro Pardo, Evandro Eduardo Seron Ruiz, and Ariani Di Felippo. "Learning rules for automatic identification of implicit aspects in Portuguese." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/stil.2021.17787.
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This sentiment analysis work is focused on the task of identifying aspects, emphasizing the so-called implicit aspects, i.e., those that are not explicitly mentioned in the texts. For this, we analyzed frequency-based methods, adapted rules from the English language to Portuguese, and developed a method that learns new rules through corpus analysis.
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Guo, Yangyang, Liqiang Nie, Zhiyong Cheng, Feng Ji, Ji Zhang, and Alberto Del Bimbo. "AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/98.
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A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions based on the language shortcut. Some efforts have been devoted to overcoming this issue with delicate models. However, there is no research to address it from the view of the answer feature space learning, despite the fact that existing VQA methods all cast VQA as a classification task. Inspired by this, in this work, we attempt to tackle the language prior problem from the viewpoint of the feature space learning. An adapted margin cosine loss is designed to discriminate the frequent and the sparse answer feature space under each question type properly. In this way, the limited patterns within the language modality can be largely reduced to eliminate the language priors. We apply this loss function to several baseline models and evaluate its effectiveness on two VQA-CP benchmarks. Experimental results demonstrate that our proposed adapted margin cosine loss can enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.
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Milojevic, Hristina, Yan Jin, Ananya Patel, Perri Chastain-Howley, and Noah K. Brown. "An Adapted Ethnographic Approach to Social Cognition and Cognitive Apprenticeship in Design Learning Experience." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22405.
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Abstract Design is difficult to teach in traditional ways of lecturing and testing. One defined learning methodology that applies well to design education is project-based learning. In an attempt to better understand the patterns of project-based learning in different design-related programs, we studied three small groups of teachers and students at an innovative academy based out of Shanghai Institute of Visual Art, entitled De Tao Master’s Academy, and compared their education style to that of subjects in regular programs at Shanghai Institute of Visual Art. Our goal was to seek patterns of cognitive apprenticeship in our subjects’ education, and find out (a) if it’s more effective than the traditional approach, and (b) can modelling (i.e. direct replication of learned material) be excluded from a design curriculum. The information gathered through surveys, interviews and observation were segmented into six categories: (1) self-regulation, (2) creative thinking and thinking styles, (3) incorporation of cognitive apprenticeship model into teaching style, (4) teaching hours vs. self-learning, (5) individual vs. team work preference, and (6) learning environment and teaching resources. We found that self-regulation was uniformly low throughout the sample, but that De Tao curriculum aimed to increase it over the course of their programs. Most students preferred small teams, with less than 5 students to do assignments and projects with, instead of individually working or working in large teams. Curriculum and interviews indicated De Tao programs had a higher focus on teaching creative thinking and independence, which reflected on design self-efficacy scores of their students when compared with SIVA students. Learning spaces at De Tao were observed to be better, and their instruction constructed close to cognitive apprenticeship. Coaching, scaffolding, articulation and exploration were evident in the design education methods adopted at De Tao. The ethnographic findings were related into an evolved social cognitive design framework, which allowed us to preliminarily contextualize design learning influencers.
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Mekid, Samir. "Challenges and Methods in Better Teaching of Engineering." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62081.
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New advanced technologies and subjects related to high level research in engineering are expected to be bridged to undergraduate teaching at various levels. Hence, innovative teaching is required to address new challenges. A rapid overview of current teaching methods is presented in this paper highlighting related issues. Problem based learning and enquiry based learning are still under investigations to be adapted to most engineering courses. It is expected that students would be able to take control of their own learning with a certain freedom, or, given the opportunity to solve engineering problems without prior knowledge of the subject. Innovative teaching methods have to be designed and established to enable clear knowledge transfer to students with continuous assessment of the learning outcomes over regular periods of teaching (e.g. every semester). This paper discusses, also, a number of challenges encountered in teaching engineering with an example.
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Su, Guangxin, Weitong Chen, and Miao Xu. "Positive-Unlabeled Learning from Imbalanced Data." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/412.
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Positive-unlabeled (PU) learning deals with the binary classification problem when only positive (P) and unlabeled (U) data are available, without negative (N) data. Existing PU methods perform well on the balanced dataset. However, in real applications such as financial fraud detection or medical diagnosis, data are always imbalanced. It remains unclear whether existing PU methods can perform well on imbalanced data. In this paper, we explore this problem and propose a general learning objective for PU learning targeting specially at imbalanced data. By this general learning objective, state-of-the-art PU methods based on optimizing a consistent risk can be adapted to conquer the imbalance. We theoretically show that in expectation, optimizing our learning objective is equivalent to learning a classifier on the oversampled balanced data with both P and N data available, and further provide an estimation error bound. Finally, experimental results validate the effectiveness of our proposal compared to state-of-the-art PU methods.
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Xu, Yixing, Chang Xu, Chao Xu, and Dacheng Tao. "Multi-Positive and Unlabeled Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/444.
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The positive and unlabeled (PU) learning problem focuses on learning a classifier from positive and unlabeled data. Some methods have been developed to solve the PU learning problem. However, they are often limited in practical applications, since only binary classes are involved and cannot easily be adapted to multi-class data. Here we propose a one-step method that directly enables multi-class model to be trained using the given input multi-class data and that predicts the label based on the model decision. Specifically, we construct different convex loss functions for labeled and unlabeled data to learn a discriminant function F. The theoretical analysis on the generalization error bound shows that it is no worse than k√k times of the fully supervised multi-class classification methods when the size of the data in k classes is of the same order. Finally, our experimental results demonstrate the significance and effectiveness of the proposed algorithm in synthetic and real-world datasets.
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Zang, Chuanqi, Mingtao Pei, and Yu Kong. "Few-shot Human Motion Prediction via Learning Novel Motion Dynamics." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/118.
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Human motion prediction is a task where we anticipate future motion based on past observation. Previous approaches rely on the access to large datasets of skeleton data, and thus are difficult to be generalized to novel motion dynamics with limited training data. In our work, we propose a novel approach named Motion Prediction Network (MoPredNet) for few-short human motion prediction. MoPredNet can be adapted to predicting new motion dynamics using limited data, and it elegantly captures long-term dependency in motion dynamics. Specifically, MoPredNet dynamically selects the most informative poses in the streaming motion data as masked poses. In addition, MoPredNet improves its encoding capability of motion dynamics by adaptively learning spatio-temporal structure from the observed poses and masked poses. We also propose to adapt MoPredNet to novel motion dynamics based on accumulated motion experiences and limited novel motion dynamics data. Experimental results show that our method achieves better performance over state-of-the-art methods in motion prediction.
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Hu, Hao, Mohamed Siala, Emmanuel Hebrard, and Marie-José Huguet. "Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/163.
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Recently, several exact methods to compute decision trees have been introduced. On the one hand, these approaches can find optimal trees for various objective functions including total size, depth or accuracy on the training set and therefore. On the other hand, these methods are not yet widely used in practice and classic heuristics are often still the methods of choice. In this paper we show how the SAT model proposed by [Narodytska et.al 2018] can be lifted to a MaxSAT approach, making it much more practically relevant. In particular, it scales to much larger data sets; the objective function can easily be adapted to take into account combinations of size, depth and accuracy on the training set; and the fine-grained control of the objective function it offers makes it particularly well suited for boosting. Our experiments show promising results. In particular, we show that the prediction quality of our approach often exceeds state of the art heuristics. We also show that the MaxSAT formulation is well adapted for boosting using the well-known AdaBoost Algorithm.
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Li, Ruihuang, Changqing Zhang, Qinghua Hu, Pengfei Zhu, and Zheng Wang. "Flexible Multi-View Representation Learning for Subspace Clustering." 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.2019/404.
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In recent years, numerous multi-view subspace clustering methods have been proposed to exploit the complementary information from multiple views. Most of them perform data reconstruction within each single view, which makes the subspace representation unpromising and thus can not well identify the underlying relationships among data. In this paper, we propose to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction. The latent representation is flexibly constructed by enforcing it to be close to different views, which implicitly makes it more comprehensive and well-adapted to subspace clustering. With the introduction of kernel dependence measure, the latent representation can flexibly encode complementary information from different views and explore nonlinear, high-order correlations among these views. We employ the Alternating Direction Minimization (ADM) method to solve our problem. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.
Reports on the topic "Adapted learning methods"
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Thompson, Joseph. How WASH Programming has Adapted to the COVID-19 Pandemic. Institute of Development Studies (IDS), December 2020. http://dx.doi.org/10.19088/slh.2021.001.
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Since first appearing at the end of 2019, the novel coronavirus disease (COVID-19) has spread at a pace and scale not seen before. On 11 March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. A rapid response was called for, and actors across the globe worked quickly to develop sets of preventative measures to contain the disease. One mode of transmission identified early on in the crisis was via surfaces and objects (fomites) (Howard et al. 2020). To combat this, hand hygiene was put forward as a key preventative measure and heralded as ‘the first line of defence against the disease’ (World Bank 2020). What followed was an unprecedented global focus on handwashing with soap. Health messages on how germs spread, the critical times at which hands should be washed, and methods for correct handwashing were shared (Centers for Disease Control and Prevention 2020). Political leaders around the world promoted handwashing and urged people to adopt the practice to protect against the coronavirus. The primary and secondary impacts of COVID-19 have affected people and industries in a variety of different ways. For the WASH sector, the centring of handwashing in the pandemic response has led to a sudden spike in hygiene activity. This SLH Rapid Topic Review takes stock of some of the cross-cutting challenges the sector has been facing during this period and explores the adaptations that have been made in response. It then looks forwards, thinking through what lies ahead for the sector, and considers the learning priorities for the next steps.
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Thompson, Joseph. How WASH Programming has Adapted to the COVID-19 Pandemic. The Sanitation Learning Hub, Institute of Development Studies, December 2020. http://dx.doi.org/10.19088/slh.2021.0015.
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Since first appearing at the end of 2019, the novel coronavirus disease (COVID-19) has spread at a pace and scale not seen before. On 11 March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. A rapid response was called for, and actors across the globe worked quickly to develop sets of preventative measures to contain the disease. One mode of transmission identified early on in the crisis was via surfaces and objects (fomites) (Howard et al. 2020). To combat this, hand hygiene was put forward as a key preventative measure and heralded as ‘the first line of defence against the disease’ (World Bank 2020). What followed was an unprecedented global focus on handwashing with soap. Health messages on how germs spread, the critical times at which hands should be washed, and methods for correct handwashing were shared (Centers for Disease Control and Prevention 2020). Political leaders around the world promoted handwashing and urged people to adopt the practice to protect against the coronavirus. The primary and secondary impacts of COVID-19 have affected people and industries in a variety of different ways. For the WASH sector, the centring of handwashing in the pandemic response has led to a sudden spike in hygiene activity. This SLH Rapid Topic Review takes stock of some of the cross-cutting challenges the sector has been facing during this period and explores the adaptations that have been made in response. It then looks forwards, thinking through what lies ahead for the sector, and considers the learning priorities for the next steps.
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Brinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor-Mirghani. Adapting to Learn and Learning to Adapt: Practical Insights from International Development Projects. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.
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Adaptive programming and management principles focused on learning, experimentation, and evidence-based decision making are gaining traction with donor agencies and implementing partners in international development. Adaptation calls for using learning to inform adjustments during project implementation. This requires information gathering methods that promote reflection, learning, and adaption, beyond reporting on pre-specified data. A focus on adaptation changes traditional thinking about program cycle. It both erases the boundaries between design, implementation, and evaluation and reframes thinking to consider the complexity of development problems and nonlinear change pathways.Supportive management structures and processes are crucial for fostering adaptive management. Implementers and donors are experimenting with how procurement, contracting, work planning, and reporting can be modified to foster adaptive programming. Well-designed monitoring, evaluation, and learning systems can go beyond meeting accountability and reporting requirements to produce data and learning for evidence-based decision making and adaptive management. It is important to continue experimenting and learning to integrate adaptive programming and management into the operational policies and practices of donor agencies, country partners, and implementers. We need to devote ongoing effort to build the evidence base for the contributions of adaptive management to achieving international development results.
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Schmidt-Sane, Megan, Tabitha Hrynick, Erica Nelson, and Tom Barker. Mutual Learning for Policy Impact: Insights from CORE. Adapting research methods in the context of Covid-19. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/core.2021.008.
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On 25 November 2021, the CORE Knowledge Translation Services team at the Institute of Development Studies, UK, hosted an online clinic session to facilitate the sharing of experiences and mutual learning on how CORE projects have or can adapt their research activities in the context of the Covid-19 pandemic. The clinic was attended by 22 CORE members from 12 projects and featured contributions from two CORE projects: The Youth Question in Africa: Impact, Response and Protection Measures in the IGAD Region and A New Digital Deal for an Inclusive Post-Covid-19 Social Compact: Developing Digital Strategies for Social and Economic Reconstruction. This learning guide captures the practical insights and advice from the event, to help inform the practice of participants and other projects across the portfolio.
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Adebayo, Oliver, Joanna Aldoori, William Allum, Noel Aruparayil, Abdul Badran, Jasmine Winter Beatty, Sanchita Bhatia, et al. Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission. The Royal College of Surgeons of England, August 2022. http://dx.doi.org/10.1308/fos2.2022.
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Over the past 50 years the capability of technology to improve surgical care has been realised and while surgical trainees and trainers strive to deliver care and train; the technological ‘solutions’ market continues to expand. However, there remains no coordinated process to assess these technologies. The FOS:TEST Report aimed to (1) define the current, unmet needs in surgical training, (2) assess the current evidence-base of technologies that may be beneficial to training and map these onto both the patient and trainee pathway and (3) make recommendations on the development, assessment, and adoption of novel surgical technologies. The FOS:TEST Commission was formed by the Association of Surgeons in Training (ASiT), The Royal College of Surgeons of England (RCS England) Robotics and Digital Surgery Group and representatives from all trainee specialty associations. Two national datasets provided by Health Education England were used to identify unmet surgical training needs through qualitative analysis against pre-defined coding frameworks. These unmet needs were prioritised at two virtual consensus hackathons and mapped to the patient and trainee pathway and the capabilities in practice (CiPs) framework. The commission received more than 120 evidence submissions from surgeons in training, consultant surgeons and training leaders. Following peer review, 32 were selected that covered a range of innovations. Contributors also highlighted several important key considerations, including the changing pedagogy of surgical training, the ethics and challenges of big data and machine learning, sustainability, and health economics. This summates to 7 Key Recommendations and 51 concluding statements. The FOS:TEST Commission was borne out of what is a pivotal point in the digital transformation of surgical training. Academic expertise and collaboration will be required to evaluate efficacy of any novel training solution. However, this must be coupled with pragmatic assessments of feasibility and cost to ensure that any intervention is scalable for national implementation. Currently, there is no replacement for hands-on operating. However, for future UK and ROI surgeons to stay relevant in a global market, our training methods must adapt. The Future of Surgery: Technology Enhanced Surgical Training Report provides a blueprint for how this can be achieved.
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Quality Assurance in HECA Colleges During the COVID-19 Pandemic: A Mixed Methods Analysis through the lens of QQI’s Core Criteria. Higher Education College's Assocation (HECA), May 2022. http://dx.doi.org/10.22375/hqf.tos22.
Full textAbstract:
The National Forum’s 2020 Network and Discipline Fund was launched in November 2020 and focuses on Shared Solutions to Common Challenges. Its overarching aim is to support networks and discipline groups to work together to respond proactively to identified challenges in teaching and learning in higher education, including those that have come to light through the sudden move to online/remote teaching and learning. The National Forum’s 2020 Network and Discipline Fund, drawing on the Higher Education Colleges’ Association (HECA) members’ experience, provided the HECA Academic Quality Enhancement Forum(HAQEF) with opportunities to strengthen its networks and discipline group, support their efforts to enhance teaching and learning, and produce useful insights and resources into the application of quality assurance of blended and online learning. In the pandemic period, many colleges were forced to change from a face-to-face delivery model of education and training to an online format of delivery. This disruption meant that assumptions about and plans for how the courses were to be delivered and managed had to be rapidly reviewed. With government pronouncements advising all staff to work from home where possible, the majority of internal roles and processes of colleges were also affected. The quality assurance (QA) policies, processes and procedures that were developed by colleges had to be modified to reflect this change in circumstances. This document reports on the results of a mixed methods study of QA developments that came about as a result of the initial changes enforced by the pandemic and the move to working from home. It also looked at what was learned about the management of quality in this sector during this time. Data was collected by HAQEF via a survey of QA personnel from the member colleges of HECA and follow up interviews. The survey consisted of 11 questions, one for each Core Area of Quality, as set out in the QQI Core QA Guidelines (2016). The questions asked for reflections on each provider’s experience of managing this aspect of QA, what resources or instances of good practice they could share with practitioners and which stakeholders were involved in this adaptation. The interviews asked about the measures that were undertaken to adapt QA processes in the pandemic period and what challenges could be reported from this adaptation. The report is divided into 11 chapters, with each chapter centred on a discussion of one Core Area of Quality as set out in the QQI Core QA Guidelines. An overview of the core area is developed, followed by a commentary on common experiences of the group in this area. Most sections conclude with resources, volunteered by member colleges of HECA, that was rated as useful in how the cohort navigated the management of QA in this core area.