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1
Morin, Johanne. "Learning relational cliches with contextual generalization." Thesis, University of Ottawa (Canada), 1999. http://hdl.handle.net/10393/8773.
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Inductive logic programming (ILP) is concerned with the problem of inducing concepts represented as logic programs (or Horn clauses) from examples. Top-down inductive learners such as FOIL (Quinlan 1990; Cameron-Jones et al. 1993) learn Horn clauses adding one literal at a time using a hill-climbing search. These learners suffer from local plateaus, where the selection of a conjunction of literals, rather than a single literal, would improve the accuracy of the clause. The problem becomes the search for combinations of literals rather than just single literals. A mechanism to search efficiently through the space of combinations of literals is needed. The FOCL system (Pazzani et al. 1991) solved this problem by giving the concept learner hand-made "relational cliches" which are combinations of literals to consider while learning. The problem is that these cliches are hard to derive and often specific to a domain. So, it would be desirable to learn them automatically. As a part of this thesis, an inductive learner called CLUSE (Cliches Learned and USEd) has been developed that learns combinations of literals called relational cliches. The underlying idea is to learn cliches from examples of a concept and to use them with a hill-climbing learner to escape local plateaus. Cliches are learned from a concept in one domain and used to learn concepts within the same domain as well as across domains. Assuming that cliches are learned and used in the same domain, literals used to express different concepts overlap. Consequently cliches learned from one concept should provide appropriate lookahead to learn concepts in the same domain. On the other hand, these cliches probably have few literals in common with concepts across domains, hence the need for more general cliches. To solve this, CLUSE learns two kinds of cliches: Domain Dependent Cliches expressed as a conjunction of literals specific to a domain, and Domain Independent Cliches where literals have variable predicate symbols. CLUSE is a bottom-up inductive relational learner based on Relative Least General Generalization (RLGG). To remedy the inefficiency and the overgeneralization problems of RLGG, a modified version of RLGG has been developed that exploits the context in which LGG is applied. The modified RLGG is called Contextual Least General Generalization (CLGG). Empirical experiments with CLUSE reveal that cliches learned with CLUSE provide appropriate lookahead to escape local plateaus of a hill-climbing learner both within and across domains. For the purpose of the evaluation, FOIL has been extended to learn concepts with or without cliches. In two domains of application, cliches have proven to be useful. One domain is the real-life application defining structures for the finite element methods (FEM). The other domain is the synthetic domain of blocks, which offers a wide variety of problems (or concepts). Other domains of application such as drug design, text categorization, and detecting traffic problems are also discussed.
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Morin, Johanne. "Learning relational clichés with contextual generalization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0015/NQ46535.pdf.
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Martin, Susanna Marie. "Mobile contextual data for hands-on learning." Thesis, University of Bath, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607144.
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This thesis investigates whether the use of hand-held technology affects motivation and learning in science. An innovative mixed methods approach was used to provide new insights into an emerging area of research. First, two pilot observational studies were conducted, which aimed to establish how a school currently uses hand held dataloggers, and gain further insight into how learners respond to this technology. This was followed by a primarily quantitative experiment that was concerned with the role of data ownership and the impact of ‘seams’ on the transformation process of the collected data. The results indicated that a hands-on experience increased confidence among students in explaining their own data, as opposed to data collected by someone else. A third study was designed to compare how student motivation and learning were affected when carrying out the same inquiry task either with or without the support of dataloggers. The results revealed no difference in accuracy or motivation for learning. The final, fourth, study was a longitudinal study designed in collaboration with a secondary science teacher, comparing three conditions: the inclusion of cameras to support student reflection, the inclusion of both cameras and the use of dataloggers to support teaching, and a control condition where the lessons were not changed. This study found that inclusion of dataloggers into modules led to increased assessment scores, while the use of cameras indicated that students are adept at taking relevant photos, and did not suffer from an extensive novelty effect. The results highlighted the importance of using a range of methods and tools for teaching students. The thesis concludes with recommendations and future research ideas, including exploring how data is visualised and the role of physical context. Of key importance is that future work is conducted in collaboration with educators in the wild.
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Smyth, A. "Ageing and implicit learning : explorations in contextual cuing." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19559/.
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Research in cognitive ageing has found that while older adults show reductions in performance on standard explicit memory tasks, implicit memory performance remains relatively stable. Such findings are often used to support the popular dual-systems account of human learning and memory, which organizes these types of cognition into distinct implicit and explicit systems. In contrast to previous studies, we found that healthy older adults show learning impairments on an implicit contextual cuing task when compared to younger adults, in addition to expected poor performance on an explicit generation test. To examine the possibility that slower overall response speed may account for the implicit deficit, younger adults’ response times were artificially increased by altering the display properties so as to match those of older adults. Learning in younger participants remained intact under these conditions. Similarly, when display properties were altered to produce faster responses in older participants, their learning continued to be impaired. These results reveal that implicit processing is not immune to the effects of ageing, and that these deficits cannot be attributed solely to older adults’ slower overall response speed. In a further series of experiments using younger participants, we examined the claim that implicit knowledge is not accessible to awareness in contextual cuing. When the number of trials used in an explicit generation test was increased, we found that contextual cuing information was consciously retrievable. These results suggest that the shorter tests used previously were not statistically powerful enough to detect a true effect. Furthermore, when concurrent implicit and explicit tests were used, learning did not precede awareness. Collectively, these findings suggest that awareness may be a necessary concomitant of contextual cuing in older adults, and provide further evidence that learning and memory should not be divided on the basis of consciousness.
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Alnuaim, A. "Designing and evaluating a contextual mobile learning application to support situated learning." Thesis, University of the West of England, Bristol, 2015. http://eprints.uwe.ac.uk/25274/.
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This research emerged from seeking to identify ways of getting Human-Computer Interaction Design students into real world environments, similar to those in which they will eventually be designing, thus maximising their ability to identify opportunities for innovation. In helping students learn how to become proficient and innovative designers and developers, it is crucial that their ‘out of the classroom’ experience of the environments in which their designs will be used, augments and extends in-class learning. The aim of this research is to investigate firstly, a blended learning model for students in higher education using mobile technology for situated learning and, secondly, the process of designing a mobile learning app within this blended learning model. This app was designed, by the author, to support students in a design task and to develop their independent learning and critical thinking skills, as part of their Human-Computer Interaction coursework. The first stage in designing the system was to conduct a comprehensive contextual inquiry to understand specific student and staff needs in the envisaged scenario. In addition, this research explores the challenges in implementing and deploying such an app in the learning context. A number of evaluations were conducted to assess the design, usability and effectiveness of the app, which we have called sLearn. The results show an improvement in scores and quality of assessed work completed with the support of the sLearn app and a positive response from students regarding its usability and pedagogic utility. The promising results show that the app has helped students in developing critical thinking and independent learning skills. The research also considers the challenges of conducting an ecologically valid study of such interventions in a higher education setting. There were issues discovered in regards to the context of use such as usability of interface elements and feeling self-conscious in using the app in a public place. The model was tested with two other student cohorts: User Experience and Engineering students, to further investigate best practice in deploying mobile learning in higher education and examine the suitability of this learning model for different disciplines. These trials suggest that the model is indeed suitable and, the engineering study in particular has demonstrated that it has the potential to support the learning in-situ of students from non-computing disciplines.
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Channing, Jill. "I Will Never Use This: Contextual Teaching and Learning." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/4884.
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Educators can connect life to learning to transform the classroom experience using contextual teaching and learning strategies and assignments such as open educational resources, service learning projects, active learning labs, practical technological applications, and collaborative assignments.
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Moos, Daniel Charles. "Learning with hypermedia examining cognitive, motivational, and contextual factors /." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6750.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Human Development. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Johnson, Cynthia L. "A contextual approach to learning collaborative behavior via observation." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4943.
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This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other software agents and creating a behavior function for a single agent. The research described here combines current research in teamwork simulations and learning by observation to effectively train a multi-agent system in effective team behavior. The dissertation describes the background and work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described.<br>ID: 029809360; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 305-314).<br>Ph.D.<br>Doctorate<br>Electrical Engineering and Computer Science<br>Engineering and Computer Science
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Méndez, Daniel. "Learning from Hunger: A Communal Recipe in Contextual Theology." Digital Commons at Loyola Marymount University and Loyola Law School, 2013. https://digitalcommons.lmu.edu/etd/12.
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SIEBRA, Sandra de Albuquerque. "Contextual analysis of users interactions in collaborative learning environments." Universidade Federal de Pernambuco, 2007. https://repositorio.ufpe.br/handle/123456789/2026.
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Previous issue date: 2007<br>As interações realizadas através de Ambientes de Aprendizagem Colaborativa
suportados por Computador (AAC) são um dos aspectos visíveis mais importantes da
colaboração. A análise dessas interações pode dar suporte aos processos de reflexão e autoavaliação
dos estudantes e às atividades dos professores. Porém, para executar o processo de
análise, é importante descobrir e registrar o contexto onde cada interação ocorreu, a fim de
entender o verdadeiro significado das interações dos usuários. Entretanto, faltam métodos e
ferramentas para a Análise de Interações em AAC que considerem: (1) o contexto onde as
interações ocorreram; (2) a diferença de necessidades do ponto de vista do professor e do
estudante; e (3) a necessidade de informações históricas contextualizadas, para produzir
relatórios mais completos para estudantes e professores. Neste cenário, objetivando prover
suporte de boa qualidade para estudantes e professores, essa tese apresenta um Processo para
Análise de Interações baseado em Contexto, que estrutura as interações, levando em
consideração o contexto onde elas ocorreram. Depois disso, ele armazena as interações em um
repositório multidimensional chamado LIM (Memória de Interações de Aprendizagem) e,
finalmente, usa consultas analíticas para explorar e analisar as informações armazenadas sob
diferentes perspectivas (dimensões), de acordo com as necessidades dos usuários. Essa tese
também apresenta um Ambiente Analítico baseado em Contexto chamado SmartChat+: um
ambiente para discussões colaborativas de assuntos específicos que segue o Processo baseado
em Contexto definido. A experimentação inicial desse ambiente é detalhada e os resultados
dos experimentos são discutidos, indicando a viabilidade do processo proposto e seu potencial
para apoiar ao aprendizado
11
Current, Daniel C. "Language Learning Through Contextual Input in a Virtual Reality Enviroment." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341613840.
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Farrow, Damian Thomas, and mikewood@deakin edu au. "The effects of contextual interference on children learning forehand tennis groundstrokes." Deakin University, 1995. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20060727.112040.
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Previous research on contextual interference theory in controlled laboratory situations consistently illustrated that random practice was superior to blocked practice when learning motor skills. However, when considered in relation to physical education class settings the findings of the contextual interference experiments were not as uniform. Furthermore, the results of the contextual interference research were ambiguous when an open skill was used as the experimental task, with no definite trend evident. Random practice was found more effective for learning (del Rey, 1989) whereas French, Rink and Werner (1990) demonstrated blocked practice to be superior. In the present study, the influence of high and low contextual interference as practice schedules was investigated within an applied sports setting using an open sports skill as the experimental task. Two groups of boys and girls, 8-9 and 10-12 years of age, were taught a forehand tennis groundstroke using both their preferred and non-preferred hands over a ten week coaching and practice period. The findings showed that male subjects were significantly better at the experimental task than female subjects at the pre-test stage only. The result also demonstrated that the 10-12 year old subjects were significantly better than the 8-9 year olds at the task with the exception of the preferred hand at the post and retention test stage. The contextual interference effect was demonstrated in the retention phase on the preferred hand of the 10-12 year old subjects where the random practice group was significantly better than the blocked practice group in an applied sports setting. These findings were discussed in relation to the role of cognition in the learning of these motor skills and the influence of the subjects related background experiences.
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Olson, Clinton Leif. "Leveraging Contextual Relationships Between Objects for Localization." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2204.
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Object localization is currently an active area of research in computer vision. The object localization task is to identify all locations of an object class within an image by drawing a bounding box around objects that are instances of that class. Object locations are typically found by computing a classification score over a small window at multiple locations in the image, based on some chosen criteria, and choosing the highest scoring windows as the object bounding-boxes. Localization methods vary widely, but there is a growing trend towards methods that are able to make localization more accurate and efficient through the use of context. In this thesis, I investigate whether contextual relationships between related objects can be leveraged to improve localization efficiency through a reduction in the number of windows considered for each localization task. I implement a context-driven localization model and evaluate it against two models that do not use context between objects for comparison. My model constrains the search spaces for the target object location and window size. I show that context-driven methods substantially reduce the mean number of windows necessary for localizing a target object versus the two models not using context. The results presented here suggest that contextual relationships between objects in an image can be leveraged to significantly improve localization efficiency by reducing the number of windows required to find the target object.
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Gane, Brian D. "Can modular examples and contextual interference improve transfer?" Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11451.
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Two instructional design features hypothesized to affect problem solving performance, problem format and contextual interference, were investigated. Problem format was manipulated by altering the format of worked examples to demonstrate a molar or modular solution. Contextual interference was manipulated by randomizing the order in which problem categories were studied. Participants studied worked examples from 5 complex probability categories and solved 11 novel problems. The modular problem format reduced study time and the workload during study and increased performance on the subsequent test. Greater contextual interference increased study time but had no effect on workload or test performance. Additionally, a regression analysis demonstrated that mental workload partially mediated the effect of problem format on test performance. A separate regression analysis did not demonstrate that working memory capacity moderated the effect of problem format on mental workload.
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Svensson, Martin, and Oskar Pettersson. "Exploring the use of contextual metadata collected during ubiquitous learning activities." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2074.
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<p>Recent development in modern computing has led to a more diverse use of devices within the field of mobility. Many mobile devices of today can, for instance, surf the web and connect to wireless networks, thus gradually merging the wired Internet with the mobile Internet. As mobile devices by design usually have built-in means for creating rich media content, along with the ability to upload these to the Internet, these devices are potential contributors to the already overwhelming content collection residing on the World Wide Web. While interesting initiatives for structuring and filtering content on the World Wide Web exist – often based on various forms of metadata – a unified understanding of individual content is more or less restricted to technical metadata values, such as file size and file format. These kinds of metadata make it impossible to incorporate the purpose of the content when designing applications. Answers to questions such as "why was this content created?" or "in which context was the content created?" would allow for a more specified content filtering tailored to fit the end-users cause. In the opinion of the authors, this kind of understanding would be ideal for content created with mobile devices which purposely are brought into various environments. This is why we in this thesis have investigated in which way descriptions of contexts could be caught, structured and expressed as machine-readable semantics.</p><p>In order to limit the scope of our work we developed a system which mirrored the context of ubiquitous learning activities to a database. Whenever rich media content was created within these activities, the system associated that particular content to its context. The system was tested during live trials in order to gather reliable and “real” contextual data leading to the transition to semantics by generating Rich Document Format documents from the contents of the database. The outcome of our efforts was a fully-functional system able to capture contexts of pre-defined ubiquitous learning activities and transforming these into machine-readable semantics. We would like to believe that our contribution has some innovative aspects – one being that the system can output contexts of activities as semantics in real-time, allowing monitoring of activities as they are performed.</p>
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Holland, Lindsay Anne. "Initial Instruction in a Mathematics Classroom: Learning in a Contextual Setting." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-10292008-113230/.
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The purpose of this research was to investigate how the order of mathematical instruction with respect to using a context affects studentsâ performance in the classroom and their attitudes towards learning. The study examined two high school Algebra I classes and was implemented over three days. On Day 1 of the study, the experimental group received the implementation of learning in a contextual setting while the control group learned in a noncontextual setting. In the noncontextual setting, students learned about one and two step equations where a lecture style lesson was implemented. On Day 2 of the study, each of the two groups received the type of instruction the other group received on Day 1. The experimental group received the traditional approach method where the control group learned the mathematics in a contextual setting. The study determined that there is a difference in studentsâ academic performance when they learn in a contextual setting first and then learn the math in a traditional based approach as opposed to learning in a traditional setting first and then learn in a contextual setting. Studentsâ attitudes toward learning in a contextual setting without regards to order were more positive. Finally, the order of instruction with respect to using a context affects lower ranked studentsâ levels of performance in the classroom more than middle or higher ranked students, but not significantly more.
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Mendoza, Gretchen Marie. "Fostering trust in technical services through integrated, collaborative and contextual learning." Research Showcase @ CMU, 2011. http://repository.cmu.edu/theses/23.
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Trust is an intrinsic component of any loyal “consumer friendship” between customers and service providers, and is a by-product of shared understanding. Nowhere is the notion of trust more relevant than in technical service—such as professional legal practice, architecture, medical care and auto repair—where the primary commodities exchanged are specialized knowledge, equipment and skills. A common challenge in dialogue between expert providers and novice customers in this context is meaningful sharing of technical information. A successful exchange requires care in representation, language, attitude, delivery and timing. Furthermore, with communication breakdowns, trust falters, and business relationships run the risk of falling apart.
Rather than relying on simple transactional exchanges of information in service, a customer’s journey could be enriched by framing service touchpoints as individual opportunities for learning. Learning activities occur in everyday life via interactions with society, artifacts or programs, and often involve the pursuit of knowledge or skills without the structure of a formal curriculum. This study explores how learning might function as a channel for strengthening multi-faceted trust relations in service through integration into programs and artifacts.
In this project, an auto repair shop was investigated as a case study in technical service, given its long inglorious history of customer mistrust. Through exploration in the context of a local mechanic shop, prototypes for experiential and transformative service learning were implemented, tested, and re-shaped into a four-part framework designed to improve technical communications
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Heath, Florence. "Role of D1 receptor antagonism in contextual fear learning and memory." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/28516/.
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Understanding the modulation of contextual fear learning and memory by the neurotransmitter dopamine is important as it could lead to a greater understanding of the mechanisms underlying anxiety disorders. The effect of D1 receptor antagonism during the contextual fear learning and memory stages was investigated. In the first set of experiments the D1 receptor antagonist SCH 23390 (0.1mg/kg; i.p.) was administered systemically before or immediately after contextual fear conditioning to determine whether D1 receptors are involved in the acquisition and/or consolidation stages. This experiment was followed up by investigating the effects of SCH 23390 infusion into the dorsal hippocampus (5μg per side) or amygdala (2.5μg per side) on contextual fear acquisition. The second set of experiments investigated the involvement of systemic SCH 23390 in the reconsolidation, retrieval, destabilization and extinction of contextual fear. SCH 23390 was administered before or immediately after either a short reactivation or longer extinction session. In the destabilization experiment SCH 23390 was administered prior to reactivation and the NMDA receptor antagonist MK-801 (0.1mg/kg; i.p) immediately after to determine if SCH 23390 could rescue the amnesic effects of NMDA receptor antagonism. It was found that systemic and intra-hippocampal but not intra-amygdala SCH 23390 reduced freezing during memory retention testing, twenty four hours and seven days after conditioning. There was no effect of SCH 23390 when immediately given after conditioning. There was also no effect of SCH 23390 when given either before or after reactivation or extinction sessions. The destabilization experiment was inconclusive as MK-801 was not found to impair memory when administered after reactivation. In conclusion, D1 receptors were found to be involved in the acquisition of contextual fear, and this modulation was found to occur in the dorsal hippocampus but not the amygdala. D1 type receptors were not found to be involved in the consolidation, retrieval, reconsolidation or extinction of contextual fear.
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Scott-Toux, Deborah. "Constructivist model for career internships: Integrating contextual learning and critical thinking." CSUSB ScholarWorks, 2001. https://scholarworks.lib.csusb.edu/etd-project/2014.
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Pesman, Haki. "Method-approach Interaction: The Effects Of Learning Cycle Vs Traditional And Contextual Vs Non-contextual Instruction On 11th Grade Students." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614298/index.pdf.
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The main purpose of the study was to explore how learning cycle and traditional method as teaching methods contribute to the effect of contextual approach on 11th grade students&rsquo<br>achievement in &ldquo<br>impulse and momentum&rdquo<br>, and attitude towards physics. Therefore, a distinction between teaching approach (contextual vs. non-contextual) and teaching method (learning cycle vs. traditional method) was made and they were used as independent variables. Students&rsquo<br>gender was also used as an independent variable. Thus, the study was a 2x2x2 factorial design. The sample, drawn through the purposive sampling, included 226 students. Pretests and posttests were used for assessing students&rsquo<br>achievement in impulse and momentum, and attitude towards physics. Using Multivariate Analysis of Covariance (MANCOVA), the main effects of contextual approach, learning cycle, and student gender as well as the interaction effects among them were investigated. Consequently, (1) contextual approach was more effective in supporting students&rsquo<br>conceptual understanding of impulse and momentum, (2) learning cycle was as effective as the traditional method, (3) gender related difference in attitude towards physics in favor of males could not be removed through the treatments, (4) contextual approach worked better with the traditional method than the learning cycle for achievement and attitude, (5) males benefitted a little more from learning cycle while females benefitted a little more from traditional method in terms of conceptual and non-conceptual scores.
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Torcinovich, Alessandro <1992>. "Using Contextual Information In Weakly Supervised Learning: Toward the integration of contextual and deep learningapproaches, to address weakly supervised tasks." Doctoral thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/20596.
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Come l'attento lettore avrà dedotto dal titolo, questa tesi pone alcune basi empiriche, assieme ad altrettante considerazioni teoriche, verso la definizione di una metodologia finalizzata a migliorare task di weakly supervised learning. La metodologia genera supervisione addizionale sfruttando l'informazione contestuale proveniente dal confronto delle osservazioni in un dataset sotto molteplici ipotesi di etichettatura.
Il materiale di ricerca presentato, ruota principalmente attorno a due algoritmi. Nella prima parte, l'attenzione è rivolta a Graph Transduction Games (GTG), un algoritmo di label propagation basato su nozioni di Teoria dei Giochi. In particolare, questo documento descrive le interazioni sperimentate con GTG e dei deep feature extractor, per affrontare problemi di semi-supervised, domain adaptation e deep metric learning. La seconda parte è incentrata su Relaxation Labeling (ReLab), una famiglia di processi utilizzata per label disambiguation, fortemente connessa a GTG, sebbene sia motivata da un differente contesto teorico. Questo documento alcuni concetti preliminari di teoria e degli esperimenti pensati per investigare future applicazioni di ReLab nel contesto di semi-supervised semantic segmentation.
Il lavoro presentato di seguito può essere pensato come un punto iniziale per costituire una teoria di contextual weakly supervised learning.
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Zhang, Zheng. "Explorations in Word Embeddings : graph-based word embedding learning and cross-lingual contextual word embedding learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS369/document.
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Les plongements lexicaux sont un composant standard des architectures modernes de traitement automatique des langues (TAL). Chaque fois qu'une avancée est obtenue dans l'apprentissage de plongements lexicaux, la grande majorité des tâches de traitement automatique des langues, telles que l'étiquetage morphosyntaxique, la reconnaissance d'entités nommées, la recherche de réponses à des questions, ou l'inférence textuelle, peuvent en bénéficier. Ce travail explore la question de l'amélioration de la qualité de plongements lexicaux monolingues appris par des modèles prédictifs et celle de la mise en correspondance entre langues de plongements lexicaux contextuels créés par des modèles préentraînés de représentation de la langue comme ELMo ou BERT.Pour l'apprentissage de plongements lexicaux monolingues, je prends en compte des informations globales au corpus et génère une distribution de bruit différente pour l'échantillonnage d'exemples négatifs dans word2vec. Dans ce but, je précalcule des statistiques de cooccurrence entre mots avec corpus2graph, un paquet Python en source ouverte orienté vers les applications en TAL : il génère efficacement un graphe de cooccurrence à partir d'un grand corpus, et lui applique des algorithmes de graphes tels que les marches aléatoires. Pour la mise en correspondance translingue de plongements lexicaux, je relie les plongements lexicaux contextuels à des plongements de sens de mots. L'algorithme amélioré de création d'ancres que je propose étend également la portée des algorithmes de mise en correspondance de plongements lexicaux du cas non-contextuel au cas des plongements contextuels<br>Word embeddings are a standard component of modern natural language processing architectures. Every time there is a breakthrough in word embedding learning, the vast majority of natural language processing tasks, such as POS-tagging, named entity recognition (NER), question answering, natural language inference, can benefit from it. This work addresses the question of how to improve the quality of monolingual word embeddings learned by prediction-based models and how to map contextual word embeddings generated by pretrained language representation models like ELMo or BERT across different languages.For monolingual word embedding learning, I take into account global, corpus-level information and generate a different noise distribution for negative sampling in word2vec. In this purpose I pre-compute word co-occurrence statistics with corpus2graph, an open-source NLP-application-oriented Python package that I developed: it efficiently generates a word co-occurrence network from a large corpus, and applies to it network algorithms such as random walks. For cross-lingual contextual word embedding mapping, I link contextual word embeddings to word sense embeddings. The improved anchor generation algorithm that I propose also expands the scope of word embedding mapping algorithms from context independent to contextual word embeddings
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Akers, Julia B. "Confronting the Realities of Implementing Contextual Learning Ideas in a Biology Classroom." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/27024.
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The purpose of this study was to describe the implementation of contextual learning
practices in a biology class. Research contends that contextual learning classrooms are active
learning environments where students are involved in â hands-onâ team projects and the teacher
assumes a facilitator role. In this student-centered classroom, students take ownership and
responsibility for their own learning. This study examined these assertions and other factors that
emerged as the study developed. The research methods used were qualitative.
The subject for this study was a biology teacher with twenty-six years of experience who
implemented contextual learning practices in two of her biology classes in the 1997-98 school year.
As the teacher confronted contextual learning, we engaged in collaborative research that included
fourteen interviews transcribed verbatim for analysis, classroom observations and the teacherâ s
written reports.
Throughout the study, factors developed that adversely affected contextual learning
practices. These factors were discipline, curriculum, and administrative decisions over which the
teacher had no control. These are examined along with their consequences for implementing a
contextual classroom.
Successful practices that worked in the teacherâ s classroom were also determined and
included the teacherâ s â failure is not an optionâ policy, mandatory tutoring, behavior contracts,
high expectations and teamed projects. Besides contextual learning, a key component of the study
was the collaborative research process and its meaning to the subject, the researcher and future
researchers who attempt this collaborative approach.
The studyâ s conclusion indicate that scheduling, multiple repeaters, discipline and the state
Standards of Learning moved the teacher away from contextual learning practices to a more teacher-directed classroom.
Two recommendations of this study are that further research is needed to study how the
state Standards of Learning have affected instructional practices and the effect of administrative
decisions that influence the level of teacher success in the classroom.<br>Ed. D.
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Lucas, Yvan. "Credit card fraud detection using machine learning with integration of contextual knowledge." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI110.
Full textAbstract:
La détection de fraude par carte de crédit présente plusieurs caractéristiques qui en font une tâche difficile. Tout d'abord, les attributs décrivant une transaction ignorent les informations séquentielles. Deuxièmement, les comportements d'achat et les stratégies de fraude peuvent changer au fil du temps, rendant progressivement une fonction de décision apprise par un classifieur non pertinente. Nous avons effectué une analyse exploratoire afin de quantifier le dataset shift jour par jour et avons identifé des périodes calendaires qui ont des propriétés différentes au sein du jeu de données. La stratégie principale pour intégrer des informations séquentielles consiste à créer un ensemble d'attributs qui sont des statistiques descriptives obtenues en agrégeant les séquences de transactions des titulaires de carte. Nous avons utilisé cette méthode comme méthode de référence pour la détection des fraudes à la carte de crédit. Nous avons proposé une stratégie pour la création d'attributs basés sur des modèles de Markov cachés (HMM) caractérisant la transaction par différents points de vue afin d'intégrer un large spectre d'informations séquentielles au sein des transactions. En fait, nous modélisons les comportements authentiques et frauduleux des commerçants et des détenteurs de cartes selon deux caractéristiques univariées: la date et le montant des transactions. Notre approche à perspectives multiples basée sur des HMM permet un prétraitement automatisé des données pour modéliser les corrélations temporelles. Des expériences menées sur un vaste ensemble de données de transactions de cartes de crédit issu du monde réel (46 millions de transactions effectuées par des porteurs de carte belges entre mars et mai 2015) ont montré que la stratégie proposée pour le prétraitement des données basé sur les HMM permet de détecter davantage de transactions frauduleuses quand elle est combinée à la stratégie de prétraitement des données par aggrégations<br>The detection of credit card fraud has several features that make it a difficult task. First, attributes describing a transaction ignore sequential information. Secondly, purchasing behavior and fraud strategies can change over time, gradually making a decision function learned by an irrelevant classifier. We performed an exploratory analysis to quantify the day-by-day shift dataset and identified calendar periods that have different properties within the dataset. The main strategy for integrating sequential information is to create a set of attributes that are descriptive statistics obtained by aggregating cardholder transaction sequences. We used this method as a reference method for detecting credit card fraud. We have proposed a strategy for creating attributes based on Hidden Markov Models (HMMs) characterizing the transaction from different viewpoints in order to integrate a broad spectrum of sequential information within transactions. In fact, we model the authentic and fraudulent behaviors of merchants and cardholders according to two univariate characteristics: the date and the amount of transactions. Our multi-perspective approach based on HMM allows automated preprocessing of data to model temporal correlations. Experiments conducted on a large set of data from real-world credit card transactions (46 million transactions carried out by Belgian cardholders between March and May 2015) have shown that the proposed strategy for pre-processing data based on HMMs can detect more fraudulent transactions when combined with the Aggregate Data Pre-Processing strategy
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Arcand, Scott Andrew. "The Effectiveness of Contextual Learning on Physics Achievement in Career Technical Education." Thesis, Saint Mary's University of Minnesota, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284451.
Full textAbstract:
<p> The purpose of this casual-comparative study was to determine if students being taught the Minnesota Science Physics Standards via contextual learning methods in Project Lead the Way (PLTW) Principles of Engineering or the PLTW Aerospace Engineering courses, taught by a Career Technical Education (CTE) teacher, achieve at the same rate as students in a physics course taught by a science teacher. The PLTW courses only cover the standards taught in the first trimester of physics. The PLTW courses are two periods long for one trimester. Students who successfully pass the PLTW Principles of Engineering course or the PLTW Engineering Aerospace course earn one-half credit in physics and one-half elective credit. The instrument used to measure student achievement was the district common summative assessment for physics. The Common Summative Assessment scores were pulled from the data warehouse from the first trimester of the 2013-2014 school year. Implications of the research address concepts of contextual learning especially in the Career Technical Education space. The mean score for Physics students (30.916) and PLTW Principles of Engineering students (32.333) was not statistically significantly different. Students in PLTW Principles of Engineering achieved at the same rate as students in physics. Due to the low rate of students participating in the Common Summative Assessment in PTLW Aerospace (four out of seven students), there is not enough data to determine if there is a significant difference in the Physics A scores and PLTW Aerospace Engineering scores.</p><p>
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Ibhakewanlan, John-Okoria. "Contextual learning : education through inter-cultural dialogue of elite and indigenous-indigent." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/30614/.
Full textAbstract:
Universal access to education has been an urgent concern since the establishment of the United Nations’ Millennium Development Goals or MDG. While aiming at ‘Education for All’, the MDG did not specify what kind of education nor how that education would be delivered. Besides the emphasis on access, apparent in the various attempts to ensure provision of education for the world’s poor, there has been also focus on material resources - especially a reliance on foreign aid. This author argues that what is needed in the long-term is a localized or Culturally Responsive approach that includes a consideration of the question of justice – particularly the issue of socio-economic inequality. The study evaluates some historical attempts towards Cultural Responsiveness (CR) in education, highlighting the efforts to filter curriculum content and teaching strategies through students’ cultural frames of reference. It eventually questions this curriculum-centred approach. Should CR not rather address the problem of elitism inherited via colonial education? The elite and the indigent, the study suggests, have become of different cultures. Hence part of the task of CR in education needs to be conceptualised as an elite-indigent dialogue. The CR dialogue is indeed of culture but must be framed in the context of justice, presented in terms of the author’s religious worldview, which includes eco-justice. To gather data on an elite-indigent interaction, the study adopts a decolonized methodology, as well as a qualitative approach employing unstructured interviews and open-ended questionnaires. Based on an interpretive case study of the relationship between an elite school in Africa and its indigenous-indigent host community, the study explores an alternative CR approach through the philosophical lens of Constructivism and the Ignatian Pedagogical Paradigm (IPP). The result is a three-fold learning hypothesis termed Costheanthropic Learning.
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Sakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Full textAbstract:
Cette thèse s'intéresse au problème de l'apprentissage à partir d'interactions en utilisant le cadre du bandit contextuel hors ligne. En particulier, nous nous intéressons à deux sujets connexes : (1) l'apprentissage de politiques hors ligne avec des certificats de performance, et (2) l'apprentissage rapide et efficace de politiques, pour le problème de recommandation à grande échelle. Pour (1), nous tirons d'abord parti des résultats du cadre d'optimisation distributionnellement robuste pour construire des bornes asymptotiques, sensibles à la variance, qui permettent l'évaluation des performances des politiques. Ces bornes nous aident à obtenir de nouveaux objectifs d'apprentissage plus pratiques grâce à leur nature composite et à leur calibrage simple. Nous analysons ensuite le problème d'un point de vue PAC-Bayésien et fournissons des bornes, plus étroites, sur les performances des politiques. Nos résultats motivent de nouvelles stratégies, qui offrent des certificats de performance sur nos politiques avant de les déployer en ligne. Les stratégies nouvellement dérivées s'appuient sur des objectifs d'apprentissage composites qui ne nécessitent pas de réglage supplémentaire. Pour (2), nous proposons d'abord un modèle bayésien hiérarchique, qui combine différents signaux, pour estimer efficacement la qualité de la recommandation. Nous fournissons les outils computationnels appropriés pour adapter l'inférence aux problèmes à grande échelle et démontrons empiriquement les avantages de l'approche dans plusieurs scénarios. Nous abordons ensuite la question de l'accélération des approches communes d'optimisation des politiques, en nous concentrant particulièrement sur les problèmes de recommandation avec des catalogues de millions de produits. Nous dérivons des méthodes d'optimisation, basées sur de nouvelles approximations du gradient calculées en temps logarithmique par rapport à la taille du catalogue. Notre approche améliore le temps linéaire des méthodes courantes de calcul de gradient, et permet un apprentissage rapide sans nuire à la qualité des politiques obtenues<br>This thesis presents contributions to the problem of learning from logged interactions using the offline contextual bandit framework. We are interested in two related topics: (1) offline policy learning with performance certificates, and (2) fast and efficient policy learning applied to large scale, real world recommendation. For (1), we first leverage results from the distributionally robust optimisation framework to construct asymptotic, variance-sensitive bounds to evaluate policies' performances. These bounds lead to new, more practical learning objectives thanks to their composite nature and straightforward calibration. We then analyse the problem from the PAC-Bayesian perspective, and provide tighter, non-asymptotic bounds on the performance of policies. Our results motivate new strategies, that offer performance certificates before deploying the policies online. The newly derived strategies rely on composite learning objectives that do not require additional tuning. For (2), we first propose a hierarchical Bayesian model, that combines different signals, to efficiently estimate the quality of recommendation. We provide proper computational tools to scale the inference to real world problems, and demonstrate empirically the benefits of the approach in multiple scenarios. We then address the question of accelerating common policy optimisation approaches, particularly focusing on recommendation problems with catalogues of millions of items. We derive optimisation routines, based on new gradient approximations, computed in logarithmic time with respect to the catalogue size. Our approach improves on common, linear time gradient computations, yielding fast optimisation with no loss on the quality of the learned policies
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Strutynskiy, Maksym. "A concept of an intent-based contextual chat-bot with capabilities for continual learning." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99102.
Full textAbstract:
Chat-bots are computer programs designed to conduct textual or audible conversations with a single user. The job of a chat-bot is to be able to find the best response for any request the user issues. The best response is considered to answer the question and contain relevant information while following grammatical and lexical rules. Modern chat-bots often have trouble accomplishing all these tasks. State-of-the-art approaches, such as deep learning, and large datasets help chat-bots tackle this problem better. While there is a number of different approaches that can be applied for different kind of bots, datasets of suitable size are not always available. In this work, we introduce and evaluate a method of expanding the size of datasets. This will allow chat-bots, in combination with a good learning algorithm, to achieve higher precision while handling their tasks. The expansion method uses the continual learning approach that allows the bot to expand its own dataset while holding conversations with its users. In this work we test continual learning with IBM Watson Assistant chat-bot as well as a custom case study chat-bot implementation. We conduct the testing using a smaller and a larger datasets to find out if continual learning stays effective as the dataset size increases. The results show that the more conversations the chat-bot holds, the better it gets at guessing the intent of the user. They also show that continual learning works well for larger and smaller datasets, but the effect depends on the specifics of the chat-bot implementation. While continual learning makes good results better, it also turns bad results into worse ones, thus the chat-bot should be manually calibrated should the precision of the original results, measured before the expansion, decrease.
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Bettadapura, Vinay Kumar. "Leveraging contextual cues for dynamic scene understanding." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54834.
Full textAbstract:
Environments with people are complex, with many activities and events that need to be represented and explained. The goal of scene understanding is to either determine what objects and people are doing in such complex and dynamic environments, or to know the overall happenings, such as the highlights of the scene. The context within which the activities and events unfold provides key insights that cannot be derived by studying the activities and events alone. \emph{In this thesis, we show that this rich contextual information can be successfully leveraged, along with the video data, to support dynamic scene understanding}. We categorize and study four different types of contextual cues: (1) spatio-temporal context, (2) egocentric context, (3) geographic context, and (4) environmental context, and show that they improve dynamic scene understanding tasks across several different application domains.
We start by presenting data-driven techniques to enrich spatio-temporal context by augmenting Bag-of-Words models with temporal, local and global causality information and show that this improves activity recognition, anomaly detection and scene assessment from videos. Next, we leverage the egocentric context derived from sensor data captured from first-person point-of-view devices to perform field-of-view localization in order to understand the user's focus of attention. We demonstrate single and multi-user field-of-view localization in both indoor and outdoor environments with applications in augmented reality, event understanding and studying social interactions. Next, we look at how geographic context can be leveraged to make challenging ``in-the-wild" object recognition tasks more tractable using the problem of food recognition in restaurants as a case-study. Finally, we study the environmental context obtained from dynamic scenes such as sporting events, which take place in responsive environments such as stadiums and gymnasiums, and show that it can be successfully used to address the challenging task of automatically generating basketball highlights. We perform comprehensive user-studies on 25 full-length NCAA games and demonstrate the effectiveness of environmental context in producing highlights that are comparable to the highlights produced by ESPN.
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TERRAGNI, SILVIA. "Modeling Relational and Contextual Information into Topic Models and their Evaluation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365002.
Full textAbstract:
La conoscenza testuale è uno dei pilastri principali della nostra società. Infatti, la conoscenza umana è spesso trasmessa attraverso le parole. Dall'invenzione della scrittura, gli uomini hanno raccontato e descritto la loro esistenza con parole su pezzi di carta. Questa quantità di conoscenza si accumula fino a ciò che l'intera civiltà ha raccolto in più di 5000 anni. Gli storici e gli scienziati sociali e politici cercano modi per capire meglio questa vasta quantità di conoscenza collettiva che non può essere esplorata manualmente.
A questo scopo, i ricercatori di machine learning, statistica e linguistica computazionale hanno sviluppato i topic model, una suite di algoritmi che mirano ad annotare grandi archivi di documenti con informazioni tematiche. La popolarità di questi modelli è dovuta al fatto che sono non supervisionati e che sono interpretabili. I topic model analizzano e riassumono i temi principali, anche detti topic, di grandi collezioni di documenti, presentando le informazioni in una forma compatta e comprensibile.
La maggior parte dei topic model si concentrano solo sulle parole codificate nei documenti. Tuttavia, informazioni aggiuntive possono essere introdotte nei topic model per migliorare le loro prestazioni. Infatti, in molti casi del mondo reale, raramente abbiamo solo i semplici testi da analizzare. Invece, abbiamo informazioni aggiuntive o metadati relativi ai documenti, ad esempio, l'autore del documento, la data, collegamenti ipertestuali ad altri documenti, un insieme di hashtag, menzioni o etichette. Possiamo usare queste informazioni precedenti per aiutare un topic model a scoprire topic di qualità. Per esempio, sapere che un documento cita un altro documento aumenta la nostra fiducia che i documenti parlano degli stessi argomenti.Inoltre, i topic model spesso ignorano l'ordine delle parole e le informazioni contestuali, rendendo difficile dedurre gli topic coerenti e significativi.
Un altro problema nel campo è legato agli iperparametri usati per durante il processo di learning dei topic model. Gli iperparametri sono spesso fissati e pochi ricercatori hanno cercato di studiare il loro impatto sui risultati.
In questa tesi, ci proponiamo di affrontare i problemi menzionati. Introduciamo nuove famiglie di topic model per ottenere prestazioni migliori. Esploriamo anche le questioni relative all'ottimizzazione degli iperparametri progettando e sviluppando un nuovo strumento per fornire ai ricercatori delle linee guida migliori su come usare un topic model.<br>Textual knowledge is one of the main pillars of our society. Indeed, human knowledge is often passed along using words. Since the invention of writing, humans have narrated and described their existence with words over pieces of papers. This amount of knowledge builds up to what the entire civilization has collected over more than 5'000 years. Historians and social and political scientists look for ways to understand better this vast amount of collective knowledge that cannot be manually explored.
To this end, researchers from machine learning, statistics and computational linguistic have developed topic models, a suite of algorithms that aim to annotate large archives of documents with thematic information. The popularity of these models is due to the fact that they are unsupervised and that they are interpretable. Topic models analyze and summarize the main themes, or topics, of large collections of documents, presenting the information in a compact and understandable form.
Most topic models focus only on the words encoded in the documents. However, additional information can be introduced into topic models to improve their performance. In fact, in many real-world cases, we seldom have only the mere texts to analyze. Instead, we have additional information or metadata related to the documents, e.g., the document's author, the date, hyperlinks to other documents, a set of hashtags, mentions or labels. We can use this prior information to help a topic model discover better topics. For example, knowing that a document cites another document increases our confidence that the documents talk about the same topics. Also, topic models often ignore word order and contextual information, making it difficult to infer high-quality topics.
Another problem in the field is related to the hyperparameters used to train the topics models. The hyperparameters are often fixed in experimental settings and few researchers have tried to study their impact on the results.
In this thesis, we aim to tackle the mentioned problems. We introduce novel families of topic models to obtain better performance. We also explore the issues related to hyperparameter optimization by designing and developing a novel tool to supply researchers with better guidelines on how to train a topic model.
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Arnol, Damien. "Statistical methods for the analysis of contextual gene expression data." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/290262.
Full textAbstract:
Technological advances have enabled profiling gene expression variability, both at the RNA and the protein level, with ever increasing throughput. In addition, miniaturisation has enabled quantifying gene expression from small volumes of the input material and most recently at the level of single cells. Increasingly these technologies also preserve context information, such as assaying tissues with high spatial resolution. A second example of contextual information is multi-omics protocols, for example to assay gene expression and DNA methylation from the same cells or samples. Although such contextual gene expression datasets are increasingly available for both popu- lation and single-cell variation studies, methods for their analysis are not established. In this thesis, we propose two modelling approaches for the analysis of gene expression variation in specific biological contexts. The first contribution of this thesis is a statistical method for analysing single cell expression data in a spatial context. Our method identifies the sources of gene expression variability by decomposing it into different components, each attributable to a different source. These sources include aspects of spatial variation such as cell-cell interactions. In applications to data across different technologies, we show that cell-cell interactions are indeed a major determinant of the expression level of specific genes with a relevant link to their function. The second contribution is a latent variable model for the unsupervised analysis of gene expression data, while accounting for structured prior knowledge on experimental context. The proposed method enables the joint analysis of gene expression data and other omics data profiled in the same samples, and the model can be used to account for the grouping structure of samples, e.g. samples from individuals with different clinical covariates or from distinct experimental batches. Our model constitutes a principled framework to compare the molecular identities of these distinct groups.
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Benkirane, Fatima Ezzahra. "Integration of contextual knowledge in deep Learning modeling for vision-based scene analysis." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCA002.
Full textAbstract:
La vision par ordinateur a connu une évolution importante, passant des méthodes traditionnelles aux modèles d'apprentissage profond. L’un des principaux objectifs des tâches de vision par ordinateur est d’émuler la perception humaine. En effet, le processus classique effectué par les modèles d’apprentissage profond dépend entièrement des caractéristiques visuelles, reflétant simplement la manière dont les humains perçoivent visuellement leur environnement. Cependant, pour que les humains comprennent l’environnement qui les entoure, leur raisonnement dépend non seulement de leurs capacités visuelles, mais aussi de leurs connaissances pré-acquises. Combler cette différence entre la perception humaine et celle des machines est essentielle afin de parvenir à un raisonnement similaire à celui des humains. Dans cette thèse, nous proposons de nouvelles approches pour améliorer les performances des modèles d’apprentissage profond en intégrant les systèmes basés sur les connaissances dans les réseaux de neuronaux profonds. L'objectif est d’aider ces réseaux à prendre les bonnes décisions en exploitant à la fois les caractéristiques visuelles et les connaissances pour émuler l’analyse visuelle de l’être humain. Ces méthodologies impliquent deux axes principaux. Premièrement, définir la représentation des connaissances pour incorporer des informations utiles à une tâche spécifique de vision. Deuxièmement, examiner comment intégrer ces connaissances dans les réseaux de neurones pour améliorer leurs performances. La première contribution porte sur l'estimation de la profondeur monoculaire. En effet, les humains sont capables d'estimer leur distance par rapport aux objets perçus, même en n’utilisant qu’un seul œil, et ceci en se basant sur les indices monoculaires. Nous proposons d'intégrer ces indices au sein des réseaux de neurones comme un raisonnement similaire à celui des humains pour l'estimation de la profondeur. À cette fin, nous suggérons d'exploiter un modèle ontologique pour représenter l'environnement comme un ensemble de concepts liés par des relations sémantiques. Les informations sur les indices monoculaires sont extraites grâce à un raisonnement effectué sur l'ontologie proposée et sont transférées dans les réseaux de neurones. Le deuxième travail porte sur la tâche de segmentation panoptique qui vise à identifier toutes les instances d’objets capturées dans une image. Nous proposons une approche qui combine les avantages des réseaux de neurones avec des connaissances sur les relations spatiales entre les objets. Nous avons choisi ce type de connaissances car elles peuvent fournir des indices utiles pour résoudre les ambiguïtés et distinguer entre les instances d'objets similaires. Plus précisément, nous proposons une stratégie d'entraînement qui intègre les connaissances dans le processus d'optimisation des réseaux de neurones. L’approche comprend un processus d'extraction et de représentation des connaissances sur les relations spatiales, qui sont incorporées dans l’entraînement sous forme d'une fonction de perte. Afin de valider l'efficacité des approches proposées, nous avons choisi l'environnement urbain et les véhicules autonomes comme principale cas d’application. Ce domaine est particulièrement intéressant car il s'agit d'un axe de recherche novateur en développement continu, avec des implications significatives pour la sécurité et la mobilité des humains. En conclusion, nous avons étudié diverses approches pour représenter les connaissances et les intégrer aux réseaux de neurones. Ces approches valident que l’utilisation combinée de méthodes basées sur les connaissances et celles basées sur les données conduit de manière constante à des résultats améliorés. Le défi principal réside toujours dans le choix des connaissances pertinentes pour chaque tâche, leur représentation et leur intégration de la manière la plus optimale dans l'architecture des réseaus de neurones profonds<br>Computer vision has made an important evolution starting from traditional methods to advanced Deep Learning (DL) models. One of the goals of computer vision tasks is to effectively emulate human perception. The classical process of DL models is completely dependent on visual features, which only reflects how humans visually perceive their surroundings. However, for humans to comprehensively understand their environment, their reasoning not only depends on what they see but also on their pre-acquired knowledge. Addressing this gap is essential as achieving human-like reasoning requires a seamless combination of data-driven and knowledge-driven methods. In this thesis, we propose new approaches to improve the performance of DL models by integrating Knowledge-Based Systems (KBS) within Deep Neural Networks (DNNs). The goal is to empower these networks to make informed decisions by leveraging both visual features and knowledge to emulate human-like visual analysis. These methodologies involve two main axes. First, define the representation of KBS to incorporate useful information for a specific computer vision task. Second, investigate how to integrate this knowledge into DNNs to enhance their performance. To do so, we worked on two main contributions. The first work focuses on monocular depth estimation. Considering humans as an example, they can estimate their distance with respect to seen objects, even using just one eye, based on what is called monocular cues. Our contribution involves integrating these monocular cues as human-like reasoning for monocular depth estimation within DNNs. For this purpose, we investigate the possibility of directly integrating geometric and semantic information into the monocular depth estimation process. We suggest using an ontology model in a DL context to represent the environment as a structured set of concepts linked with semantic relationships. Monocular cues information is extracted through reasoning performed on the proposed ontology and is fed together with the RGB image in a multi-stream way into the DNNs. Our approach is validated and evaluated on widespread benchmark datasets. The second work focuses on panoptic segmentation task that aims to identify and analyze all objects captured in an image. More precisely, we propose a new informed deep learning approach that combines the strengths of DNNs with some additional knowledge about spatial relationships between objects. We have chosen spatial relationships knowledge for this task because it can provide useful cues for resolving ambiguities, distinguishing between overlapping or similar object instances, and capturing the holistic structure of the scene. More precisely, we propose a novel training methodology that integrates knowledge directly into the DNNs optimization process. Our approach includes a process for extracting and representing spatial relationships knowledge, which is incorporated into the training using a specially designed loss function. The performance of the proposed method was also evaluated on various challenging datasets. To validate the effectiveness of the proposed approaches for combining KBS and DNNs regarding different methodologies, we have chosen the urban environment and autonomous vehicles as our main use case application. This domain is particularly interesting because it is a challenging and novel field in continuous development, with significant implications for the safety, comfort and mobility of humans. As a conclusion, the proposed approaches validate that the integration of knowledge-driven and data-driven methods consistently leads to improved results. Integration improves the learning process for DNNs and enhances results of computer vision tasks, providing more accurate predictions. The challenge always lies in choosing the relevant knowledge for each task, representing it in the best structure to leverage meaningful information, and integrating it most optimally into the DNN architecture
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Johansson, Joakim. "Acquiring low-frequency English vocabulary by contextual guessing amongst Swedish learners of English playing The Infectious Madness of Doctor Dekker." Thesis, Linnéuniversitetet, Institutionen för språk (SPR), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-89390.
Full textAbstract:
Video and computer game users are frequently stated as possessing a higher proficiency in English. In this study, 3 Swedish upper secondary school students from different programs played The Infectious Madness of Doctor Dekker, a lovecraftian murder mystery game. The aim was to examine the possible effects on their English vocabulary acquisition through their ability of assuming words' meaning from the context presented by the game. The words used in the study were low-frequency words which were gathered from the game and then cross-referenced with the Corpus of Contemporary American English(COCA). The 80 least frequent words were selected. Participants were tasked with translating, explaining or using the words in sentences after completing a game chapter. The study found that the game had in general had a positive effect on the participants’ vocabulary store. However, the test scores varied greatly between participants; 1 participant acquired 1 word while another participant acquired 18. This, combined with the small sample size, meant it was difficult to say definitively how effective the game had been at expanding the participants' vocabulary store.
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Lowry, Katherine Danielle. "Age and Context Dependency in Causal Learning." TopSCHOLAR®, 2015. http://digitalcommons.wku.edu/theses/1538.
Full textAbstract:
The ability to make associations between causal cues and outcomes is an important adaptive trait that allows us to properly prepare for an upcoming event. Encoding context is a type of associative processing; thus, context is also an important aspect of acquiring causal relationships. Context gives us additional information about how two events are related and allows us to be flexible in how we respond to causal cues. Research indicates that older adults exhibit an associative deficit as well as a deficit in contextual processing; therefore, it seems likely that these deficits are responsible for the deficit in older adults’ causal learning. The purpose of the current study was to more directly test how associative deficits related to older adults’ contextual processing affect their causal learning. Based on past research, it was hypothesized that older adults would be less likely than younger adults to acquire and use contextual information in causal learning. A causal learning scenario from Boddez, Baeyens, Hermans, and Beckers (2011) was used to test the hypothesis that older adults show deficits in contextual processing in a causal learning scenario. This task examined contextual processing using blocking and extinction. Participants went through eight blocks of trials in which they were exposed to various cues and outcomes. They provided expectancy ratings that indicated how likely they believed an outcome was to occur, and these ratings were used to assess age differences in use of contextual information in a causal learning scenario. As expected, both younger and older adults demonstrated blocking in that they assigned higher causal value to a previously trained target cue (A+) than to another cue (X) that was only presented in compound with cue A later in the task (i.e., AX+). Additionally, when tested in the context where the association was originally learned following extinction training (i.e., A-), the causal value of cue A decreased for all groups, even if extinction training took place in a different context. However, ratings for cue A decreased even more for younger adults whose extinction training took place in a different context when tested in their extinction context.
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Lie, Kin-pou, and 李健豹. "The hierarchical nature of acquisition of visual specificity in spatial contextual cueing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43223965.
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Lie, Kin-pou. "The hierarchical nature of acquisition of visual specificity in spatial contextual cueing." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43223965.
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Siedlecki, Madeleine, and Alexander Lundström. "Teachers’ Beliefs Regarding Vocabulary Learning." Thesis, Malmö högskola, Fakulteten för lärande och samhälle (LS), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-31611.
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This degree project aims to investigate how teachers negotiate their teaching practices and what determines their choice of working methods when it comes to teaching vocabulary. Since we are interested in teachers’ thoughts and opinions regarding vocabulary teaching, a selection of English teachers were interviewed. Our theoretical background is rooted in Nation’s (2001) theories on what it means to know a word, Schmitt’s (2000, 2007) theories on how vocabulary is acquired and on Lundahl’s (2012, 2014) perspectives on traditions in the English classroom. Additionally we used research articles for our analysis of the results. The results from our findings indicate that vocabulary is being taught in both a revised traditional- and a non-traditional way. Our results also show that education and contextual factors influence how teachers teach vocabulary. From our results, we can conclude that our informants teach vocabulary for communicative purposes, but that there is a considerable difference in their approach to vocabulary learning. In addition, we can also conclude that the informants all expressed the need for further professional development in English, and that there is a need for more research on how contextual factors affect teachers in their work.
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Burton, Lindsay Julia. "Community-based early learning in Solomon Islands : cultural and contextual dilemmas influencing program sustainability." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:b9c96049-ea5d-47e3-b74c-951cd22bb090.
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The Solomon Islands (SI), a small developing nation in the South Pacific, demonstrates an emergent community-based kindergarten model with the potential to promote context and culture relevant early learning and development. SI early childhood education (ECE) particularly rose in prominence with a 2008 national policy enactment requiring all children to attend three years of kindergarten as prerequisite for primary school entry. However, these ECE programs remain severely challenged by faltering community support. Internationally, many ECE programs dramatically resemble a universalized Western-based model, with a decidedly specific discourse for “high quality” programs and practices for children ages 0-8. Often these uncritical international transfers of Euro-American ideologies promote restricted policies and practices. This has resulted in a self-perpetuating set of practices and values, which arguably prevent recognition of, and efforts to reinvent, more culturally-relevant, sustainable programs for the Majority World. Based on the Kahua region (est. pop. 4,500) of Makira-Ulawa Province, this collaborative, ethnographically-inspired, case study explores how community characteristics have affected the cultural and contextual sustainability of community-based ECE in remote villages. The study traces historical and cultural influences to present-day SI ECE. Subsequently, it explores the re-imagined SI approach to formal ECE program design, remaining challenges preventing these programs from being sustained by communities, and potential community-wide transformations arising from these initiatives. To achieve this, the study collaborated with stakeholders from all levels of SI society through extensive participant-observations, interviews, and participatory focus groups. Findings aspire to enlighten regional sustainable developments and resilient behaviors relating to ECE. Key research findings suggest five overarching principles influencing kindergarten sustainability: presence of “champion” for the ECE vision; community ownership-taking, awareness-building, and cooperation-maintenance; and program cultural/contextual sensitivity and relevance. These elements were found to be strongly linked with an intergenerational cultural decay in the Kahua region, as conceptualized through a model of Cyclically-Sustained Kindergarten Mediocrity.
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Kochli, Daniel. "The amygdala is critical for trace, delay, and contextual fear conditioning." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1406560508.
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Bawden, Rachel. "Going beyond the sentence : Contextual Machine Translation of Dialogue." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS524/document.
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Les systèmes de traduction automatique (TA) ont fait des progrès considérables ces dernières années. La majorité d'entre eux reposent pourtant sur l'hypothèse que les phrases peuvent être traduites indépendamment les unes des autres. Ces modèles de traduction ne s'appuient que sur les informations contenues dans la phrase à traduire. Ils n'ont accès ni aux informations présentes dans les phrases environnantes ni aux informations que pourrait fournir le contexte dans lequel ces phrases ont été produites. La TA contextuelle a pour objectif de dépasser cette limitation en explorant différentes méthodes d'intégration du contexte extra-phrastique dans le processus de traduction. Les phrases environnantes (contexte linguistique) et le contexte de production des énoncés (contexte extra-linguistique) peuvent fournir des informations cruciales pour la traduction, notamment pour la prise en compte des phénomènes discursifs et des mécanismes référentiels. La prise en compte du contexte est toutefois un défi pour la traduction automatique. Évaluer la capacité de telles stratégies à prendre réellement en compte le contexte et à améliorer ainsi la qualité de la traduction est également un problème délicat, les métriques d'évaluation usuelles étant pour cela inadaptées voire trompeuses. Dans cette thèse, nous proposons plusieurs stratégies pour intégrer le contexte, tant linguistique qu'extra-linguistique, dans le processus de traduction. Nos expériences s'appuient sur des méthodes d'évaluation et des jeux de données que nous avons développés spécifiquement à cette fin. Nous explorons différents types de stratégies: les stratégies par pré-traitement, où l'on utilise le contexte pour désambiguïser les données fournies en entrée aux modèles ; les stratégies par post-traitement, où l'on utilise le contexte pour modifier la sortie d'un modèle non-contextuel, et les stratégies où l'on exploite le contexte pendant la traduction proprement dite. Nous nous penchons sur de multiples phénomènes contextuels, et notamment sur la traduction des pronoms anaphoriques, la désambiguïsation lexicale, la cohésion lexicale et l'adaptation à des informations extra-linguistiques telles que l'âge ou le genre du locuteur. Nos expériences, qui relèvent pour certaines de la TA statistique et pour d'autres de la TA neuronale, concernent principalement la traduction de l'anglais vers le français, avec un intérêt particulier pour la traduction de dialogues spontanés<br>While huge progress has been made in machine translation (MT) in recent years, the majority of MT systems still rely on the assumption that sentences can be translated in isolation. The result is that these MT models only have access to context within the current sentence; context from other sentences in the same text and information relevant to the scenario in which they are produced remain out of reach. The aim of contextual MT is to overcome this limitation by providing ways of integrating extra-sentential context into the translation process. Context, concerning the other sentences in the text (linguistic context) and the scenario in which the text is produced (extra-linguistic context), is important for a variety of cases, such as discourse-level and other referential phenomena. Successfully taking context into account in translation is challenging. Evaluating such strategies on their capacity to exploit context is also a challenge, standard evaluation metrics being inadequate and even misleading when it comes to assessing such improvement in contextual MT. In this thesis, we propose a range of strategies to integrate both extra-linguistic and linguistic context into the translation process. We accompany our experiments with specifically designed evaluation methods, including new test sets and corpora. Our contextual strategies include pre-processing strategies designed to disambiguate the data on which MT models are trained, post-processing strategies to integrate context by post-editing MT outputs and strategies in which context is exploited during translation proper. We cover a range of different context-dependent phenomena, including anaphoric pronoun translation, lexical disambiguation, lexical cohesion and adaptation to properties of the scenario such as speaker gender and age. Our experiments for both phrase-based statistical MT and neural MT are applied in particular to the translation of English to French and focus specifically on the translation of informal written dialogues
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Poulet, Sarah. "Contextual Cueing : apprentissage des régularités spatiales de l’environnement au cours de la recherche visuelle et accès conscient aux connaissances." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCC027.
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Face à la surcharge informationnelle inhérente à la complexité du monde visuel, pour pallier la limitation de ses capacités de traitement et ajuster au mieux son comportement, l’être humain est capable d’apprendre et d’exploiter des régularités présentes dans son environnement visuel. S’il est complexe, le monde visuel n’en est effectivement pas moins hautement structuré et redondant, le contexte dans lequel s’inscrivent les objets constituant dès lors une source de prédictibilité profitable aux observateur·rice·s. Depuis une vingtaine d’années, le paradigme de Contextual Cueing (CC) offre un outil expérimental pour étudier, en laboratoire, comment il est ainsi possible d’apprendre des régularités spatiales existant dans la localisation relative de différents objets, pour faciliter les comportements de recherche visuelle. Le CC alimentant une littérature particulièrement abondante et prolifique, la première partie de cette thèse fait état de l’ensemble des travaux préexistants sur le paradigme. Au travers de cette revue de questions, sont principalement discutées la nature implicite du phénomène, la caractérisation des connaissances acquises et de leur effet facilitateur, ainsi que la transposabilité et la généralisation du CC spatial à d’autres régularités ou environnements. Dans une seconde partie, nous présentons trois études expérimentales menées à partir de ce paradigme. La première d’entre elles montre que dans des scènes naturelles, l’acquisition de connaissances implicites tend à précéder l’exploitation consciente des régularités classiquement attachée au CC dans ce type d’environnement. Dans des contextes artificiels et abstraits (i.e., configurations de lettres), si le CC ne paraît pas avant tout dépendre de la bonne mise en œuvre de processus explicites et conscients, notre deuxième étude rapporte que son intégrité est menacée au cours du vieillissement normal. Enfin, notre troisième étude explore les possibilités d’apprentissage en situation d’expositions très brèves aux régularités (i.e., 50ms), et suggère que ces dernières peuvent effectivement être extraites et exploitées alors même que les environnements de recherche ne sont pas (physiquement) disponibles suffisamment longtemps pour permettre à la recherche d’être complétée<br>While deeply complex, the visual world is highly structured and redundant. Through experience, human beings can thus learn regularities present in the visual world, and exploit them to deal with information overload and facilitate behaviors. For the past twenty years, the Contextual Cueing (CC) paradigm has provided an experimental tool to study, in the laboratory, how spatial regularities can be learned to expedite visual search. The first part of this dissertation reviews the existing literature related to this paradigm. It mainly discusses the implicit nature of CC, the characterization of the acquired knowledge and its facilitating effect, as well as the generalization of spatial CC to other regularities and environments. In a second part, three experimental studies using the CC paradigm are presented. The first one shows that, in natural scenes, the acquisition of implicit knowledge tends to precede the conscious exploitation of regularities that classically accompanies CC in this type of environment. In artificial scenes (i.e., letter configurations), even if CC does not seem to primarily depend on the proper implementation of explicit and conscious processes, our second study reports that its integrity is threatened during normal ageing. Finally, our third study investigates whether CC can emerge from very brief exposures of regularities (i.e., 50ms), and suggests that regularities can actually be extracted and exploited even though search displays are not (physically) available long enough to complete the search
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Mast, Douglas Wayne. "The Effects of Learning and Reading Strategies Upon Spanish Vocabulary Acquisition Amongst Middle School Students." Diss., Temple University Libraries, 2008. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/6058.
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Spanish<br>Ph.D.<br>This study is based on an investigation of the effect of contextual and non-contextual vocabulary strategies carried out among three different sections of middle school students in the researcher's own beginning level Spanish classroom. Although all students participated in the same textbook related classroom activities, students in Group 1 received explicit instruction on contextual (wrote novel sentences, used a bilingual dictionary and morphological analysis) and non-contextually based vocabulary strategies (cognitive and semantic processing); students in Group 2 used bilingual dictionaries, analyzed base words and affixes, and created novel sentences with the words; students in Group 3 did not receive explicit instruction on vocabulary strategies, but were encouraged to use whatever methods they chose. The vocabulary used included words obtained from student textbooks, and Spanish word families. Primary data sources included quiz and test scores, an open-ended questionnaire, and a vocabulary learning dialogue journal. Data analysis consisted of (a) statistical assessment of quiz and test data, and (b) qualitative assessment of an open-ended questionnaire that evaluated student test-taking strategies, and assessment of student self-report data gathered from the dialogue journals. Results of testing instruments indicated that students in the contextually based strategy group had improved in their test taking scores in a more notable manner than the other two groups, and that students in the full-strategy group had achieved notable gains on the vocabulary quizzes. Qualitative analysis of the questionnaire showed the main strategy used by all participants on the vocabulary tests to be Spanish base word analysis. Assessment of the journals indicated a preference for rote and rehearsal strategies over cognitive and semantic processing among students in Group 1. The study suggests a combination of contextually based strategies entailing a focus on L2 base words and affixes may improve student vocabulary test taking scores.<br>Temple University--Theses
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Wang, Yu-Xiang. "New Paradigms and Optimality Guarantees in Statistical Learning and Estimation." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1113.
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Machine learning (ML) has become one of the most powerful classes of tools for artificial intelligence, personalized web services and data science problems across fields. Within the field of machine learning itself, there had been quite a number of paradigm shifts caused by the explosion of data size, computing power, modeling tools, and the new ways people collect, share, and make use of data sets. Data privacy, for instance, was much less of a problem before the availability of personal information online that could be used to identify users in anonymized data sets. Images, videos, as well as observations generated over a social networks, often have highly localized structures, that cannot be captured by standard nonparametric models. Moreover, the “common task framework” that is adopted by many sub- disciplines of AI has made it possible for many people to collaboratively and repeated work on the same data set, leading to implicit overfitting on public benchmarks. In addition, data collected in many internet services, e.g., web search and targeted ads, are not iid, but rather feedbacks specific to the deployed algorithm. This thesis presents technical contributions under a number of new mathematical frameworks that are designed to partially address these new paradigms. • Firstly, we consider the problem of statistical learning with privacy constraints. Under Vapnik’s general learning setting and the formalism of differential privacy (DP), we establish simple conditions that characterizes the private learnability, which reveals a mixture of positive and negative insight. We then identify generic methods that reuses existing randomness to effectively solve private learning in practice; and discuss weaker notions of privacy that allows for more favorable privacy-utility tradeoff. • Secondly, we develop a few generalizations of trend filtering, a locally-adaptive nonparametric regression technique that is minimax in 1D, to the multivariate setting and to graphs. We also study specific instances of the problems, e.g., total variation denoising on d-dimensional grids more closely and the results reveal interesting statistical computational trade-offs. • Thirdly, we investigate two problems in sequential interactive learning: a) off- policy evaluation in contextual bandits, that aims to use data collected from one algorithm to evaluate the performance of a different algorithm; b) the problem of adaptive data analysis, that uses randomization to prevent adversarial data analysts from a form of “p-hacking” through multiple steps of sequential data access. In the above problems, we will provide not only performance guarantees of algorithms but also certain notions of optimality. Whenever applicable, careful empirical studies on synthetic and real data are also included.
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Gardner, Martin John. "The impact of individual and contextual factors upon experiential learning team events : a case study." Thesis, University of Leicester, 2014. http://hdl.handle.net/2381/28751.
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Whilst much focus in recent years has been given to the learning process involved in Experiential Learning (ELT) and related Action Learning (AL) theories, very little academic study has been given to the contextual elements that surround them. Using the practical setting of an experiential learning team event, this thesis considers the academic ambiguities relating to such contextual elements, and examines the possible impact these can have on the overall development experience. Experiential learning team events are primarily designed to provide a series of action centred challenges which can support both individual and team development. The research firstly considers ELT together with AL and their relationship with related theories including: humanist, team, personality, cognitive and social. From this broad-base review, four contextual factors emerge which were considered suitable for further study: sponsor role, subcultures, facilitation and personality profiling. The empirical study focused on a three day residential experiential learning team event, with six teams of eight managers taking part. The managers were all from one company, a petrochemical organisation employing 3000 people, based in Abu Dhabi, UAE. Data was collected from a series of questionnaires, observations and focus group sessions. Findings identified that the sponsor role can prove to be a strong influence, particularly through attendance of events and supporting follow up actions. Subcultures proved to have a positive effect, which was in contrast to the more negative impacts highlighted by the majority of academics. Using individual personality profiling to provide a balanced representation across teams proved insignificant. Comparing teams with a balance of personality types with those of a more unbalanced composition revealed that individuals can adapt their personality preference to match contextual requirements. The role of the facilitator and the style they adopted in supporting the event proved to be a strong influence. The empowering style was seen to be particularly effective in supporting the principles of ELT and AL. Given the findings of this study, the implications clearly indicate that for future ELT and AL research: the sponsor role can prove a strong contributory factor to the learning experience and should not be ignored; the diversity of subcultures can be used in a constructive way given common purpose and values are in place; the facilitator role can prove a positive influence provided the style of support is in keeping with the principles of ELT and AL for individual empowerment.
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Sarker, Chandrama. "Automated detection of flooded areas using machine learning methods." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211481/1/Chandrama_Sarker_Thesis.pdf.
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This thesis provides an analytical study to develop a generalised classification method for faster detection of flooded areas from multispectral remote sensing images. The method is based on the neighbouring spectral information to obtain flood probability information from images. The project also develops an optimization algorithm for refining the probability measures for pixels occluded by cloud cover or cloud shadows using ancillary elevation information of the Australian landscapes. This project investigated different flooding events that occurred in different parts of Queensland and northern New South Wales.
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Chau, Yat Kwong. "What predicts workplace self-paced e-learning outcomes? : an exploratory study of motivation, self-regulated learning characteristics, and organisational contextual factors." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/51641/.
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Organisations today are investing significant amounts of time, money, and resources on workplace self-paced e-learning, yet employees seem to be having problems even getting these e-learning courses completed, bringing into question the true value of workplace self-paced e-learning. In an attempt to improve understanding of factors contributing to success in workplace self-paced e-learning, this study investigated how employee learners’ motivation, self-regulated learning, and organisational contextual factors affected outcomes in workplace self-paced e-learning. A quantitative study was conducted to investigate the research questions. Participants of the study were 119 employees enrolled in workplace self-paced e-learning courses provided by Hong Kong organisations. Data were collected using online questionnaires and analysed using the partial least squares structural equation modelling technique. Findings revealed significant relationships between learners’ motivation, self-regulated learning, organisational contextual factors, and training outcomes in workplace self-paced e-learning. Motivation to learn, time management, metacognitive self-regulation, perceived choice, workload, and organisational support were found to positively correlate with training outcomes as expressed in terms of course completion rate, learner satisfaction, and perceived learning performance in workplace self-paced e-learning. Findings also revealed learners’ autonomy in learning participation, level of workload (negative), and supervisor support (negative) moderate the relationship between learners’ time management strategy use and completion rate of workplace self-paced e-learning courses. Unfortunately, the results failed to support the expected relationship between supervisor support and training outcomes. The significance of the findings is discussed, along with implications for researchers and practitioners, limitations of the current study, and opportunities for future research.
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Down, Catherine, and not supplied. "Situated learning: perceptions of training practitioners on the transfer of competence across workplace contexts." RMIT University. Education, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080109.094404.
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Down, Catherine Mary, and jack keating@rmit edu au. "Situated learning and polycontextual boundary crossing: Practitioners' perceptions of the transfer of competence across different work contexts." RMIT University. Philosophy, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20060911.130320.
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This research focused on the question, How do practitioners understand the transfer of competence (that is, what do they know and can do) accross different workplace contexts and how does it influence their practice? The research investigates the experiences and perceptions of 108 workers, who have changed jobs or whose jobs have changed, as to how they were able to adapt what they knew and could do at the time. The research is phenomological, using a methodology designed to collect and analyse data from the participants without decontextualising it. The methodology is customised and contextualised and uses activity theory, Engestrom's theory of expansive learning, grounded theory and discourse analysis to interrogate the research question. The collection of data occurred over a period of five years and was in two stages, with the second stage validating and building on the first stage. Minimally structured interviews and a questionnare were the main data collec tion tools used. Some descriptive statistics have been used but the research is qualitative in intent. The research draws on current theoretical positions of learning, transfer, experimental learning, workplace learning, activity theory, qualitative research and reflection on experience. The thesis has been written to foreground the voices of the participants and the insights their experience brings to the research. The research addresses a current gap in research work, carried out in Australia or overseas, which focuses on the transfer of competence across workplaces. The outcomes provide new perspectives on the ways in which practitioners understand transfer and integrate these interpretations into their generalisation without decontextualisation, and thus makes a contribution to our collective knowledge and understanding. The outcomes of the research are a metaphoric framework to guide the transfer of competence over different work contexts; a record of the application of new understandings of transfer as a sequence of consequential transitions (Beach 1999); generalisations derived from the embedding of contexts (Van Oers 1998); and an innovative research methodology. In addition, the participants have provided their perspectives on the preperation of, and on-going support for, people entering or crossing workplace contexts, and the consequential, necessary changes to institutional learning.
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Hwang, Gyu-Young. "An examination of the impact of introducing greater contextual interference during practice on learning to golf putt." Texas A&M University, 2003. http://hdl.handle.net/1969.1/1608.
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The skill of putting in golf contributes approximately 40 percent to ones total score making it an important skill to master in golf. One of the critical means of improving putting skill is through practice. The purpose of this study was to: (a) investigate if different practice schedules with different degrees of contextual interference (CI) influenced the participants immediate and long-term putting performance, (b) examine if performance changes were associated with concomitant changes in specific kinematic parameters, and (c) assess the cognitions of the participants during various stages of the practice of the putting skill.
Twenty-four undergraduate students were randomly assigned to either a blocked or random practice schedule. On Day One each participant practiced putting to three targets (4 ft, 8 ft, and 12 ft distance) for a total of 108 trials (36 trials to each target). On Day Two 30 trials of retention (10 trials to each target) and 10 transfer trials (10 ft distance) were performed. To obtain a kinematic description of the putting action, an OPTOTRAKTM 3020 camera system recorded the 3D movement of the putter. Participants cognitions were analyzed from stimulated recall interview data. Random practice participants exhibited poorer putting performance during acquisition compared to their blocked practice counterparts but showed superior performance in retention and transfer tests. While the blocked practice participants had significantly lower variability in the amplitude in the x-dimension for backswing, impact velocity, and putter position at impact (z-dimension) during practice, the random practice participants showed significantly lower variability in the amplitude of the x-dimension for the backswing and downswing, impact velocity, and putter position at impact during the retention and transfer phases.
Content analysis of interview data yielded three emergent categories: participant focus, self-evaluation of performance, and benefits of practice. The participants provided evidence of active thought processes during the putting task while receiving little instruction. The blocked group focused more on accuracy while the random group was more focused on judging distance. The lack of recognition about the z-dimension has potential implications for how instruction and feedback might be employed during the learning process.
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Santos, Jefferson John dos. "Efeitos da interferência contextual na aprendizagem de habilidades motoras em função de diferentes testes de retenção." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/39/39132/tde-27032012-082959/.
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O objetivo do presente estudo foi investigar o efeito da interferência contextual na aprendizagem de habilidades motoras em função de diferentes testes de retenção. Quatro diferentes intervalos de retenção foram empregados: (10m) 10 minutos (24h) 24 horas, (07d) sete dias, e (30d) 30 dias após a fase de aquisição. Cento e quarenta e quatro estudantes universitários, homens (n=88) e mulheres (n=56), com idade média de 21,9 anos (±3,2), todos voluntários e sem experiência com a tarefa, foram divididos em dois grupos de prática variada: aleatória e blocos. A tarefa consistiu em arremessar dardos de salão em um alvo circular com a mão dominante, a fim de acertar o seu centro. Participantes realizaram a tarefa em três distâncias diferentes do alvo (2,00m, 2,60m e 3,20m). O experimento consistiu de duas fases: aquisição (90 tentativas) e retenção (18 tentativas). O desempenho foi analisado em termos de magnitude (somatória de pontos) e variabilidade (coeficiente de variação dos pontos). Os resultados mostraram que todos os grupos melhoraram o desempenho na fase de aquisição e o mantiveram na fase de retenção. Além disso, concernente ao objetivo deste estudo, nenhuma diferença entre os grupos foi verificada no teste de retenção<br>The objective of the present study was to investigate the contextual interference effect in the motor skills learning in function of different retention tests. Four different retention intervals were employed: (10m) ten minutes, (24h) twenty-four hours, (07d) seven days, and (30d) thirty days after the acquisition phase. One hundred and forty-four college students, men (n=88) and women (n=56), mean age 21.9 years (±3.2), all volunteers and without experience with the task, were divided into two groups of varied practice: random and blocks. The ask consisted of throwing darts at a circular target (dartboard), with the dominant hand, in order to hit its center. Participants performed the task at three different target distances (2,00m, 2,60m, and 3,20m). The experiment consisted of two phases: acquisition (90 trials) and retention (18 trials). Performance was analyzed by magnitude (sum of points) and variability (coefficient of variation of points). Results showed that all groups improved the performance in the acquisition phase, and that it was kept in the retention test. Moreover, concerned to the aim of this study, no difference between the groups was found in the retention test