Dissertations / Theses: '3P model of learning'

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Jones, Catherine Toni, and n/a. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning." Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030304.092316.

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The aim of this research programme was to examine the 3P model of learning (Biggs, 1987a, 1999). The first stage necessarily involved an examination of the Study Process Questionnaire (SPQ) (Biggs, 1987a), an instrument developed to measure the process component of the model. The structure of the SPQ was examined utilising exploratory and confirmatory factor analysis of undergraduate responses (n= 260). The results indicated the higher-order factor structure of deep-achieving and surface-achieving-motive provided the most reliability and a better model fit than either the subscales or scales of the SPQ. The construct validity of the two constructs deep and surface was assessed next using a multitrait-multimethod matrix (MTMM) constructed from the three measures of the self-report questionnaire, interview ratings and written assessments from first-year students (n = 50). The results indicated good convergent validity between the deep scale of the SPQ and the interview ratings on the deep scale, between the deep scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The results indicated good convergent validity between the surface scale on the SPQ and the interview ratings on the surface scale, but not between the surface scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The discriminant validity between deep and surface was good for the SPQ, but not for either the interview or the written assessment. The findings indicate the deep and surface scales of the SPQ adequately measure the underlying deep and surface constructs. The retest reliability of the SPQ was then examined utilising Spearmans Rho to assess the rank-order correlations with a sample of third-year students (n=87). Over a period of three months there were significant correlations for the surface motive, surface strategy, deep strategy, achieving motive and achieving strategy subscales of the SPQ, suggesting good reliability for these subscales. The results at the scale level of the SPQ result in similar conclusions. There was a moderate significant correlation for the surface, deep and achieving scales of the SPQ, suggesting the scales have good reliability over a period of three months. There was also a moderate significant correlation for the surface-achieving-motive and deep-achieving scales over a period of three months. The stability of SPQ scores was also assessed utilising a series of one-way repeated measures MANOVAs with a sample of third-year undergraduates (n = 64). The results suggest some change occurs in self-reported use of approaches to learning between the first and third-years of an undergraduate degree programme. The role of the teaching-learning environment was next examined. Utilising a within-subjects design, undergraduate students (n=48) concurrently enrolled in traditional (viz. lecture and tutorial) and non-traditional (viz. workshops and group projects) subjects completed the SPQ to describe their approaches to learning in each subject. A series of 2x2 repeated measures MANOVAs were undertaken. The results indicated students were likely to change their approach to learning based on their perceptions of the learning environment (traditional or non-traditional subject). However, those students identified as predominantly surface learners significantly increased their deep scale scores in the non-traditional subject when compared to deep learners. The next study examined a range of personality (locus of control, sensing function, thinking function, intelligence) and demographic variables (age, gender, year of study) to assess which were good predictors of deep and surface approaches to learning. A series of regression analyses identified age, sensing function and locus of control as significant predictors of the surface, surface-achieving-motive, and deep approaches to learning. Locus of control was found to be a significant predictor of the deep-achieving approach to learning. The final study examined the 3P model of learning. Based on the results of earlier studies in the research programme the situational component of the presage factors was not included. The model was examined using structural equation modelling (n= 394). Two initial models were tested using both the three (deep, surface, achieving) and two (surface-achieving-motive and deep-achieving) process factor models. The three process factor model provided the better model fit. The results suggest deep and surface approaches to learning do not mediate between personal characteristics and learning outcomes (i.e. GPA). The results of this series of studies suggest the need for further research into the SPQ and the 3P model of learning. The implications of the research programme are also discussed.

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Jones, Catherine Toni. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366357.

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The aim of this research programme was to examine the 3P model of learning (Biggs, 1987a, 1999). The first stage necessarily involved an examination of the Study Process Questionnaire (SPQ) (Biggs, 1987a), an instrument developed to measure the process component of the model. The structure of the SPQ was examined utilising exploratory and confirmatory factor analysis of undergraduate responses (n= 260). The results indicated the higher-order factor structure of deep-achieving and surface-achieving-motive provided the most reliability and a better model fit than either the subscales or scales of the SPQ. The construct validity of the two constructs deep and surface was assessed next using a multitrait-multimethod matrix (MTMM) constructed from the three measures of the self-report questionnaire, interview ratings and written assessments from first-year students (n = 50). The results indicated good convergent validity between the deep scale of the SPQ and the interview ratings on the deep scale, between the deep scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The results indicated good convergent validity between the surface scale on the SPQ and the interview ratings on the surface scale, but not between the surface scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The discriminant validity between deep and surface was good for the SPQ, but not for either the interview or the written assessment. The findings indicate the deep and surface scales of the SPQ adequately measure the underlying deep and surface constructs. The retest reliability of the SPQ was then examined utilising Spearman’s Rho to assess the rank-order correlations with a sample of third-year students (n=87). Over a period of three months there were significant correlations for the surface motive, surface strategy, deep strategy, achieving motive and achieving strategy subscales of the SPQ, suggesting good reliability for these subscales. The results at the scale level of the SPQ result in similar conclusions. There was a moderate significant correlation for the surface, deep and achieving scales of the SPQ, suggesting the scales have good reliability over a period of three months. There was also a moderate significant correlation for the surface-achieving-motive and deep-achieving scales over a period of three months. The stability of SPQ scores was also assessed utilising a series of one-way repeated measures MANOVA’s with a sample of third-year undergraduates (n = 64). The results suggest some change occurs in self-reported use of approaches to learning between the first and third-years of an undergraduate degree programme. The role of the teaching-learning environment was next examined. Utilising a within-subjects design, undergraduate students (n=48) concurrently enrolled in traditional (viz. lecture and tutorial) and non-traditional (viz. workshops and group projects) subjects completed the SPQ to describe their approaches to learning in each subject. A series of 2x2 repeated measures MANOVA’s were undertaken. The results indicated students were likely to change their approach to learning based on their perceptions of the learning environment (traditional or non-traditional subject). However, those students identified as predominantly surface learners significantly increased their deep scale scores in the non-traditional subject when compared to deep learners. The next study examined a range of personality (locus of control, sensing function, thinking function, intelligence) and demographic variables (age, gender, year of study) to assess which were good predictors of deep and surface approaches to learning. A series of regression analyses identified age, sensing function and locus of control as significant predictors of the surface, surface-achieving-motive, and deep approaches to learning. Locus of control was found to be a significant predictor of the deep-achieving approach to learning. The final study examined the 3P model of learning. Based on the results of earlier studies in the research programme the situational component of the presage factors was not included. The model was examined using structural equation modelling (n= 394). Two initial models were tested using both the three (deep, surface, achieving) and two (surface-achieving-motive and deep-achieving) process factor models. The three process factor model provided the better model fit. The results suggest deep and surface approaches to learning do not mediate between personal characteristics and learning outcomes (i.e. GPA). The results of this series of studies suggest the need for further research into the SPQ and the 3P model of learning. The implications of the research programme are also discussed.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
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Tai, Chunming. "Undergraduate business and management students' experiences of being involved in assessment." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/9456.

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This study aimed to explore university undergraduates’ experiences of student involvement in assessment (SIA). Based on Biggs’ 3P model of student learning, this study focused on students’ experiences prior to SIA, during SIA and after SIA in three Business and Management modules. Applying this framework, different practices of involving students in assessment (peer assessment, self assessment or self designed assessment) were studied from the perspectives of the students concerned. Unlike other studies that normally test to what extent the designed outcomes of SIA have been met, the goal of this research was to reveal the inside picture of how students were coping with those SIA tasks and their learning. This picture was outlined from students’ perceptions of SIA, the main factors that might influence students’ engagement with SIA, and students’ reflections on SIA practice in the particular module. This study adopted mixed research methods with sequential explorative design. It employed the ETLA (Environment of Teaching, Learning and Assessment) questionnaire and follow up semi-structured interviews. There were in total 251 valid questionnaire responses from students and 18 valid student interviews. The data were collected from three undergraduate Business and Management degree modules in which different strategies were used to involve students in assessment. The three innovative modules were all from Scottish universities in which assessment practices were being re-engineered by involving students in assessment. Two of the modules had participated in the REAP (Re-engineering Assessment Practice) project. However, they were different from each other in terms of the way in which they involved students in assessment and the level or extent of student involvement in assessment that was entailed. The report and analysis of the findings has taken three main forms. First, the module context including the teaching, learning and assessment environment and student learning approaches and satisfactions in the particular module were compared and analysed using the questionnaire data. The results showed a strong association between the elements in the teaching and learning environment and student learning approaches. They also indicated that the quality of teaching, feedback and learning support played significant roles in the quality of student learning. Secondly, an analysis of the interview data was undertaken to examine why and how students would learn differently in different module contexts with different SIA practices, and how students were coping with their learning in the SIA tasks concerned. In addressing these questions, students’ previous experiences in SIA, and knowledge about SIA, peers’ influence, teachers’ support and training for SIA, interaction between and among students and teachers, the clarity of the module objectives and requirements and learning resources were found to be the major factors that might influence students’ engagement in the SIA. Additionally, the salient learning benefits and challenges of SIA as perceived by students were explored. Thirdly, based on the preceding findings, the analysis of each module aimed to further consider in what way the three modules differed from each other with respect to SIA practices, and how students responded in the three different module contexts in terms of their engagement with SIA. These three forms of analysis made it possible to gain a rich understanding of students’ experiences of SIA that could also feed into a consideration of what kind of support the students might need in order to better engage them into the SIA and better prepare them for life-long learning.

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Draper, Fiona J. "Development of a Student-Centred Evaluation Framework for Environmental Vocational Education and Training Courses. Development and validation of a Student-Centred Evaluation Framework for Environmental Vocational Education and Training Courses derived from Biggs' 3P Model and Kirkpatrick's Four Levels Evaluation Model." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5496.

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Individuals and organisations need to do much more if sustainable development is to be achieved. Appropriate environmental vocational education and training (EVET) is essential for current decision makers. Crucial decisions need to be made before the present generation of school and college students achieve significant positions of authority. An increasing range of EVET courses and course providers are available within the UK. However, availability is not synonymous with suitability for either the attendee and/or his/her (future) employer. Previous research indicates that, as a component of lifelong learning, EVET courses should and the methods used to evaluate them should be student-centred. This thesis describes the development and validation of a new studentcentred evaluation framework. Preliminary literature reviews identified six fundamental issues which needed to be addressed. Existing academically productive evaluation models were examined and critically appraised in the context of these problems. The output from this process was used to develop a bespoke research methodology. Empirical research on four commercial EVET programmes revealed distinct personal, teaching and work-based presage factors which influenced course attendance, individual learning and subsequent organisational learning. Modified versions of Biggs' 3P model and Kirkpatrick's Four level Evaluation Model were shown to provide an effective student-centred evaluation framework for EVET courses. Additional critical elements pertaining course utility and the student's long(er) term ii retention of knowledge/skill were derived from previous research by Alliger et al (1997). Work-based presage factors and the student¿s return on expectation were added as a direct consequence of this research. The resultant new framework, the Presage-Product Evaluation Framework, was positively received during an independent validation. This confirmed inter alia that the framework should also be capable of adaption for use with other VET courses. Recommendations for additional research focus on the need to demonstrate this through further empirical studies.

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Draper, Fiona Jane. "Development of a student-centred evaluation framework for environmental vocational education and training courses : development and validation of a student-centred evaluation framework for environmental vocational education and training courses derived from Biggs' 3P Model and Kirkpatrick's Four Levels Evaluation Model." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5496.

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Individuals and organisations need to do much more if sustainable development is to be achieved. Appropriate environmental vocational education and training (EVET) is essential for current decision makers. Crucial decisions need to be made before the present generation of school and college students achieve significant positions of authority. An increasing range of EVET courses and course providers are available within the UK. However, availability is not synonymous with suitability for either the attendee and/or his/her (future) employer. Previous research indicates that, as a component of lifelong learning, EVET courses should and the methods used to evaluate them should be student-centred. This thesis describes the development and validation of a new studentcentred evaluation framework. Preliminary literature reviews identified six fundamental issues which needed to be addressed. Existing academically productive evaluation models were examined and critically appraised in the context of these problems. The output from this process was used to develop a bespoke research methodology. Empirical research on four commercial EVET programmes revealed distinct personal, teaching and work-based presage factors which influenced course attendance, individual learning and subsequent organisational learning. Modified versions of Biggs¿ 3P model and Kirkpatrick¿s Four level Evaluation Model were shown to provide an effective student-centred evaluation framework for EVET courses. Additional critical elements pertaining course utility and the student¿s long(er) term ii retention of knowledge/skill were derived from previous research by Alliger et al (1997). Work-based presage factors and the student¿s return on expectation were added as a direct consequence of this research. The resultant new framework, the Presage-Product Evaluation Framework, was positively received during an independent validation. This confirmed inter alia that the framework should also be capable of adaption for use with other VET courses. Recommendations for additional research focus on the need to demonstrate this through further empirical studies.

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Huang, Mei-hui. "Factors influencing self-directed learning readiness amongst Taiwanese nursing students." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/20709/1/Mei-hui_Huang_Thesis.pdf.

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Rapid scientific and technological advances in health care mean that nurses need to keep learning and engage in professional education so that they can continue to provide safe and quality care. Education programs which prepare nurses for practice as a registered nurse have a vital role to play in ensuring that graduates are self-directed in responding to the need for ongoing learning throughout their professional career. In many countries, improving students’ readiness for self-directed learning has thus gained increasing recognition as being an important goal of nursing education programs. This level of interest in developing self-directedness in learning is evident in many policy documents and research in Taiwan. The aim of this study was to investigate factors influencing self-directed learning readiness amongst Taiwanese nursing students. A conceptual framework adopted from Biggs’s ‘3P model of teaching and learning’ was constructed to guide this study’s investigation. This study employed a two-staged mixed-method design to obtain a better understanding of Taiwanese students’ experience of SDL in undergraduate nursing programs. Stage one of the present study was a qualitative approach using semi-structured interview to explore students’ experiences with learning activities which they perceived to be self-directed in their undergraduate programs. Eight students were interviewed. Findings from this stage reveal that participants perceived a shift in teaching and learning styles between their previous nursing programs and the university. The more frequent use of student-directed learning activities, in which students were encouraged to be active and to take responsibility for their learning tasks, was one of the changes in teaching and learning approaches perceived by participants. Participants further suggested a number of factors that influenced the outcomes of these learning activities, including teacher-student interaction, facilitation process and learning resources. Stage two of this study used a quantitative approach consisting of two phases: instrument pilot testing and a cross-sectional survey. In the first phase, the instruments were translated into Chinese through a rigorous translation process and tested with a convenience sample of nursing students in Taiwan. Results indicated the translated instruments were reliable and stable. The second phase, a cross-sectional survey, was conducted to examine the conceptual framework of this study. A total of 369 undergraduate nursing students completed the questionnaire. Results of data analysis provides support for the conceptual framework proposed for this study, suggesting that students’ achievement goals and their perceptions of the learning environment significantly influence their adoption of learning approaches and the development of SDL readiness. Based on the results, this study provides practical implications that nurse educators may adopt to enhance students’ SDL readiness. This study also provides theoretical implications and recommendations for future research. It is envisaged that these recommendations may help future researchers focus their research design and further understandings of how to help students develop their ability to become self-directed learners.

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Huang, Mei-hui. "Factors influencing self-directed learning readiness amongst Taiwanese nursing students." Queensland University of Technology, 2008. http://eprints.qut.edu.au/20709/.

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Rapid scientific and technological advances in health care mean that nurses need to keep learning and engage in professional education so that they can continue to provide safe and quality care. Education programs which prepare nurses for practice as a registered nurse have a vital role to play in ensuring that graduates are self-directed in responding to the need for ongoing learning throughout their professional career. In many countries, improving students’ readiness for self-directed learning has thus gained increasing recognition as being an important goal of nursing education programs. This level of interest in developing self-directedness in learning is evident in many policy documents and research in Taiwan. The aim of this study was to investigate factors influencing self-directed learning readiness amongst Taiwanese nursing students. A conceptual framework adopted from Biggs’s ‘3P model of teaching and learning’ was constructed to guide this study’s investigation. This study employed a two-staged mixed-method design to obtain a better understanding of Taiwanese students’ experience of SDL in undergraduate nursing programs. Stage one of the present study was a qualitative approach using semi-structured interview to explore students’ experiences with learning activities which they perceived to be self-directed in their undergraduate programs. Eight students were interviewed. Findings from this stage reveal that participants perceived a shift in teaching and learning styles between their previous nursing programs and the university. The more frequent use of student-directed learning activities, in which students were encouraged to be active and to take responsibility for their learning tasks, was one of the changes in teaching and learning approaches perceived by participants. Participants further suggested a number of factors that influenced the outcomes of these learning activities, including teacher-student interaction, facilitation process and learning resources. Stage two of this study used a quantitative approach consisting of two phases: instrument pilot testing and a cross-sectional survey. In the first phase, the instruments were translated into Chinese through a rigorous translation process and tested with a convenience sample of nursing students in Taiwan. Results indicated the translated instruments were reliable and stable. The second phase, a cross-sectional survey, was conducted to examine the conceptual framework of this study. A total of 369 undergraduate nursing students completed the questionnaire. Results of data analysis provides support for the conceptual framework proposed for this study, suggesting that students’ achievement goals and their perceptions of the learning environment significantly influence their adoption of learning approaches and the development of SDL readiness. Based on the results, this study provides practical implications that nurse educators may adopt to enhance students’ SDL readiness. This study also provides theoretical implications and recommendations for future research. It is envisaged that these recommendations may help future researchers focus their research design and further understandings of how to help students develop their ability to become self-directed learners.

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Mahadevan, Shankar. "A Learning Object Model For Electronic Learning." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34060.

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Digital libraries are fast expanding into the role of independent educational entities that aspire not only to complementing traditional classroom teaching, but also allow open electronic learning for distance and continued education. These multifaceted roles can be realized only if the course content and the related content management system are versatile enough to be captured into any individual's learning needs. Many studies have defined a concept of "learning object" to address the issues and needs. But in attempting to solve the problem, the definitions have emphasized some aspects of the digital library while leaving the other issues to be solved later. Thus, the whole system dynamics is either weak or too cumbersome to navigate. As a part of this masters work, firstly the current model of pedagogical endowment was investigated. In order to accommodate the digital nature of education, a new modern profile of learning is proposed that allows modular yet efficient transfer of knowledge from the teacher to the pupil. The thesis then proposes a comprehensive learning object (LO) model, along with the associated system model, that will allow complete and flexible integration of content into the modern digital library profile. The process will be user-centric (both for knowledge developers and learners) as well as metadata-centric. It is scalable and interoperable with legacy and existing content databases and display systems. This thesis covers how the LO model is integrated into the core of the library's content development, discovery, and delivery process. The results of the experiment in terms of ease-of-use, flow-control, and feasibility of the model are documented. A beta-version of these concepts has been successfully tested with volunteers and implemented as a part of the Digital Library Network for Engineering and Technology (DLNET) project.
Master of Science

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GHADIRZADEH, ALI. "LEARNING A VISUALFORWARD MODEL." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142034.

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Abstract Internal forward models are aimed to provide the system with the prediction of changes in sensory observations as the consequent of its own actions. For the special case where the sensed information is in the form of the camera images, the model is called visual forward model. Images are one of the richest resources of data and the ability to predict the sensory camera images, enables the robots to do more autonomous and intelligent tasks. Most of actions performed by robots lead to outcomes which are appearing in the vision system. Therefor the capability to predict these outcomes in the form of images helps the robot to execute better long- term plans. That is why the visual forward models are of particular importance. The main challenges regarding the construction of the visual forward models are the high amount of image data to be predicted and the degrees of freedom of the robot's action which causes the complexities to grow rapidly. In this work, we have investigated dierent methods to construct the visual forward models for a robotic camera head setup. The forward model explores the contin- gencies between the movements in the robot's neck and eye joints and the resulting changes in the camera images. Four dierent methods to construct the visual for- ward models are introduced and implemented. Learning of the forward models in these methods is based on linear interpolation, radial basis function networks or Gaussian processes given the correspondences between the successive frames ex- tracted by the use of SURF descriptors or constructions of so-called cumulator units. To examine the performance of the proposed methods, two dierent types of experiments are designed with the dierence that in the rst experiment, depth information is not relevant while in the second one it is. Our experimental results show the success of the introduced methods in the construction of the visual forward models also provide the weak and strong aspects of each method.

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Dorfler, Viktor. "Model of learning ability." Thesis, University of Strathclyde, 2005. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=9341.

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Gawande, Nitin A. "Modeling microbiological and chemical processes in municipal solid waste bioreactor development and applications of a three-phase numerical model BIOKEMOD-3P /." Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002659.

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Gomes, Herman M. "Model learning in iconic vision." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/323.

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Generally, object recognition research falls into three main categories: (a) geometric, symbolic or structure based recognition, which is usually associated with CAD-based vision and 3-D object recognition; (b) property, vector or feature based recognition, involving techniques that vary from specific feature vectors, multiple filtering to global descriptors for shape, texture and colour; and (c) iconic or image based recognition, which either complies with the traditional sensor architecture of an uniform array of sampling units, or uses alternative representations. An example is the log-polar image, which is inspired by the human visual system and besides requiring less pixels, has some useful mathematical properties. The context of this thesis is a combination of the above categories in the sense that it investigates the area of iconic based recognition using image features and geometric relationships. It expands an existing vision system that operates by fixating at interesting regions in a scene, extracting a number of raw primal sketch features from a log-polar image and matching new regions to previously seen ones. Primal sketch features like edges, bars, blobs and ends are believed to take part of early visual processes in humans providing cues for an attention mechanism and more compact representations for the image data. In an earlier work, logic operators were defined to extract these features, but the results were not satisfactory. This thesis initially investigates the question of whether or not primal sketch features could be learned from log-polar images, and gives an affirmative answer. The feature extraction process was implemented using a neural network which learns examples of features in a window of receptive fields of the log-polar image. An architecture designed to encode the feature’s class, position, orientation and contrast has been proposed and tested. Success depended on the incorporation of a function that normalises the feature’s orientation and a PCA pre-processing module to produce better separation in the feature space. A strategy that combines synthetic and real features is used for the learning process. This thesis also provides an answer to the important, but so far not well explored, question of how to learn relationships from sets of iconic object models obtained from a set of images. An iconic model is defined as a set of regions, or object instances, that are similar to each other, organised into a geometric model specified by the relative scales, orientations, positions and similarity scores for each pair of image regions. Similarities are measured with a cross-correlation metric and relative scales and orientations are obtained from the best matched translational variants generated in the log-polar space. A solution to the structure learning problem is presented in terms of a graph based representation and algorithm. Vertices represent instances of an image neighbourhood found in the scenes. An edge in the graph represents a relationship between two neighbourhoods. Intra and inter model relationships are inferred by means of the cliques found in the graph, which leads to rigid geometric models inferred from the image evidence.

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Haussamer, Nicolai Haussamer. "Model Calibration with Machine Learning." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29451.

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This dissertation focuses on the application of neural networks to financial model calibration. It provides an introduction to the mathematics of basic neural networks and training algorithms. Two simplified experiments based on the Black-Scholes and constant elasticity of variance models are used to demonstrate the potential usefulness of neural networks in calibration. In addition, the main experiment features the calibration of the Heston model using model-generated data. In the experiment, we show that the calibrated model parameters reprice a set of options to a mean relative implied volatility error of less than one per cent. The limitations and shortcomings of neural networks in model calibration are also investigated and discussed.

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Layne, Jeffery Ray. "Fuzzy model reference learning control." Connect to resource, 1992. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1159541293.

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Ene, Gloria Unoma. "A learning 'learning' model for optimised construction workforce development." Thesis, University of Central Lancashire, 2017. http://clok.uclan.ac.uk/20919/.

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Integrating learning and work has become important for several reasons. The recognition that the key resources for wealth creation, knowledge and ideas are embedded in human capital. Furthermore, fast-paced advances in knowledge, technology, and access to information ensure that capabilities rapidly become obsolete. Continuous learning and workplace learning have therefore become essential. These developments have highlighted the pivotal role of learning in individual career development and organisational performance and the construction industry needs to address these issues. The construction industry, however, continues to report skill gaps suggesting that construction businesses need to consider creative ways to deliver skill-enhancing opportunities for their workforce. The challenge is global but has added significance for African emerging economies considering their developmental needs. Integrating workforce learning and development key practices into construction business was therefore the crux of this research which was aimed at developing a conceptual learning model that will enable construction firms to optimise performance in line with their business goals. Given the complexity of the construction domain and the need to allow integration of diverse processes, perceptions, experiences, practices and interactions, a pragmatic philosophical lens was employed allowing for a mixed methods research approach. A social constructionist ontology and a largely interpretivist stance was adopted. Surveys and case studies were conducted employing questionnaires, interviews and focus group discussions for data collection. Data analysis methods used were relative importance, correlational and constant comparative analyses. The research investigated the two main elements of learning systems the learner and the learning environment. The learner aspect found that emotional and social attributes were significantly associated with the performance of intermediate construction skills while key workforce practices emerged from the learning environment studies. These findings were integrated to develop the construction learning and development optimising model (CLEARDO). The research was limited to Nigeria because of its current focal position in the African economy.

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Berragan, Elizabeth Anne. "Learning nursing through simulation : towards an expansive model of learning." Thesis, University of the West of England, Bristol, 2013. http://eprints.uwe.ac.uk/20107/.

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This thesis explores the impact of simulation upon learning for undergraduate nursing students. A brief history of the evolution of pre-registration nurse education and the development of simulation for nursing provide background and context to the study. The conceptual frameworks used for this study draw upon the work of Benner and Sutphen (2007) and Engeström (1994). Benner and Sutphen’s work highlights the complex nature of situated knowledge in practice disciplines such as nursing. They suggest that knowledge must be constantly integrated within the curriculum through pedagogies of interpretation, formation, contextualisation and performance. These pedagogies present a framework, which enhances the understanding of the impact of simulation upon student learning. Engeström’s work on activity theory, recognises the links between learning and the environment of work and highlights the possibilities for learning to inspire change, innovation and the creation of new ideas. His notion of expansive learning offers nurse education a way of reconceptualising the learning that occurs during simulation. Together these frameworks present an opportunity for nurse education to articulate and theorise the learning inherent in simulation activities. Conducted as a small-scale narrative case study, this study tells the unique stories of a small number of undergraduate nursing students, nurse mentors and nurse educators and explores their experiences of learning through simulation. The nurse educators viewed simulation as a means of helping students to learn to be nurses, whilst, the nurse mentors suggested that simulation helped them to determine nursing potential. The students’ narratives revealed that they approached simulation learning in different ways resulting in a range of outcomes: those who were successfully becoming nurses, those who were struggling or working hard to become nurses and those who were not becoming nurses. A theoretical analysis of learning through simulation offers a means of conceptualizing and establishing different perspectives for understanding the learning described by the participants and offers new possibilities towards an expansive approach to learning nursing. The study concludes by examining what this interpretation of learning might mean for nurse education, nursing research and nursing practice.

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Saitas-Zarkias, Konstantinos. "Insights into Model-Agnostic Meta-Learning on Reinforcement Learning Tasks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290903.

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Meta-learning has been gaining traction in the Deep Learning field as an approach to build models that are able to efficiently adapt to new tasks after deployment. Contrary to conventional Machine Learning approaches, which are trained on a specific task (e.g image classification on a set of labels), meta-learning methods are meta-trained across multiple tasks (e.g image classification across multiple sets of labels). Their end objective is to learn how to solve unseen tasks with just a few samples. One of the most renowned methods of the field is Model-Agnostic Meta-Learning (MAML). The objective of this thesis is to supplement the latest relevant research with novel observations regarding the capabilities, limitations and network dynamics of MAML. For this end, experiments were performed on the meta-reinforcement learning benchmark Meta-World. Additionally, a comparison with a recent variation of MAML, called Almost No Inner Loop (ANIL) was conducted, providing insights on the changes of the network’s representation during adaptation (meta-testing). The results of this study indicate that MAML is able to outperform the baselines on the challenging Meta-World benchmark but shows little signs actual ”rapid learning” during meta-testing thus supporting the hypothesis that it reuses features learnt during meta-training.
Meta-Learning har fått dragkraft inom Deep Learning fältet som ett tillvägagångssätt för att bygga modeller som effektivt kan anpassa sig till nya uppgifter efter distribution. I motsats till konventionella maskininlärnings metoder som är tränade för en specifik uppgift (t.ex. bild klassificering på en uppsättning klasser), så metatränas meta-learning metoder över flera uppgifter (t.ex. bild klassificering över flera uppsättningar av klasser). Deras slutmål är att lära sig att lösa osedda uppgifter med bara några få prover. En av de mest kända metoderna inom området är Model-Agnostic Meta-Learning (MAML). Syftet med denna avhandling är att komplettera den senaste relevanta forskningen med nya observationer avseende MAML: s kapacitet, begränsningar och nätverksdynamik. För detta ändamål utfördes experiment på metaförstärkningslärande riktmärke Meta-World. Dessutom gjordes en jämförelse med en ny variant av MAML, kallad Almost No Inner Loop (ANIL), som gav insikter om förändringarna i nätverkets representation under anpassning (metatestning). Resultaten av denna studie indikerar att MAML kan överträffa baslinjerna för det utmanande Meta-Worldriktmärket men visar små tecken på faktisk ”snabb inlärning” under metatestning, vilket stödjer hypotesen att den återanvänder funktioner som den lärt sig under metaträning.

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Brock, David E. "Group therapy : an interpersonal learning model." Thesis, University of Surrey, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329423.

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Doshi, Finale (Finale P. ). "Efficient model learning for dialog management." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40325.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 118-122).
Partially Observable Markov Decision Processes (POMDPs) have succeeded in many planning domains because they can optimally trade between actions that will increase an agent's knowledge about its environment and actions that will increase an agent's reward. However, POMDPs are defined with a large number of parameters which are difficult to specify from domain knowledge, and gathering enough data to specify the parameters a priori may be expensive. This work develops several efficient algorithms for learning the POMDP parameters online and demonstrates them on dialog manager for a robotic wheelchair. In particular, we show how a combination of specialized queries ("meta-actions") can enable us to create a robust dialog manager that avoids the pitfalls in other POMDP-learning approaches. The dialog manager's ability to reason about its uncertainty -- and take advantage of low-risk opportunities to reduce that uncertainty -- leads to more robust policy learning.
by Final Doshi.
S.M.

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Bizonova, Zuzana. "Model driven e-learning platform integration." Evry, Télécom & Management SudParis, 2008. http://www.theses.fr/2008TELE0011.

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Le travail présenté ici s’intéresse à la problématique de l’interopérabilité entre systèmes de téléenseignement (e-learning) ; en particulier en ce qui concerne la question de la réutilisation des didacticiels (coursewares), sujets d’investissements considérables de la part des auteurs. En effet, le succès du paradigme du téléenseignement a été tel qu’il a donné lieu au développement de nombreuses plateformes toujours plus complexes et puissantes, mais reposant sur des architectures diverses, aux interfaces variées et incompatibles. Le résultat est une isolation croissante des applications (les systèmes de téléenseignement) et des données (les didacticiels), au détriment de la pérennité des investissements effectués dans ces derniers. L’approche retenue a utilisé le Model Driven Architecture (MDA) comme base conceptuelle. La stratégie, résumée à grands traits, a été la suivante : - Un travail approfondi sur les architectures des systèmes cibles a permis de dégager les principes architecturaux de ces diverses plateformes ; - L’étape suivante impliquait de disposer d’une modélisation générique (Platform Independent Model ou PIM du MDA) des systèmes cibles, de façon à pouvoir instancier un tel modèle en modèles plus concrets (Platform Specific Model ou PSM, puis implémentation). Une réflexion théorique a permis de conjecturer, puis de mettre au point une méthode originale intégrée au MDA, introduisant une étape de rétro-conception. L’application de cette méthode sur les systèmes cibles a résulté en l’obtention d’un modèle générique PIM généralisant l’ensemble des systèmes cibles en un seul modèle indépendant des plateformes. - Le modèle PIM généralisé a finalement permis de spécifier et d’implémenter un démonstrateur logiciel capable de migrer automatiquement des données (didacticiels) entre les plateformes cibles
In the recent years, e-learning gained popularity among educational institutions as well as enterprises. As the result of that many commercial or open-source Learning Management Systems (LMS) were developed to manage online courses. However, while the usage of these systems gained recognition and acceptance amongst institutions, a new category of problems arose that needs to be solved: because of multiplicity of platforms and approaches used for various systems implementation, it became increasingly difficult to exchange pieces of information among those systems. Applications and their data become isolated - a clear economical concern for the future of these technologies. The present study describes a method, based on Model Driven Architecture (MDA), for integrating approaches of candidate LMS systems into a generalized architectural framework. The framework makes use of standards for description of data and metadata like learning materials (IEEE LOM, IEEE PAPI), student information (IMS LIP) or learning design (IMS LD). This platform-independent framework can be used for automatic migration of data between various e-learning platforms
Počas posledných desiatich rokov si e-learning získal popularitu medzi vzdelávacími inštitúciami po celom svete. Výsledkom tohto trendu bola tvorba mnohých Learning Management Systémov na správu e-learningových kurzov. Mnohé z týchto systémov získavajú stále väčší počet používateľov avšak začínajú sa objavovať nové problémy spojené s ich používaním. Množstvo rôznorodých systémov a platforiem spôsobuje problémy pri zdieľaní dát medzi nimi. Tieto aplikácie a ich dáta ostávajú navzájom medzi sebou v izolácii, čo však môže spôsobiť vážne ekonomické problémy a ohroziť budúcnosť týchto technológií. Pre lepšie pochopenie, ide tu o zdieľanie takých dát ako sú vzdelávacie materiály či záznamy a výsledky jednotlivých študentov. Vytvorenie kvalitného materiálu je časovo aj myšlienkovo náročný process. Ak nie je možné zdieľať tieto dáta medzi systémami, znamená to, že je problematické ich znovuvyužitie v inej platforme. V tejto situácii je potrebné opakovane vytvárať rovnaké druhy informácií pre rozličné systémy. Táto štúdia opisuje metódu založenú na Modelovo orientovanej architektúre (MDA), ktorá integruje prístupy rozličných pozorovaných LMS systémov do zovšeobecného architektonického rámca, ktorý využíva štandardy pre popis dát a metadát ako napríklad IEEE LOM, IMS QTI či IEEE PAPI alebo IMS LD. Tento platformovo nezávislý rámec nám umožní automaticke zdieľanie rozličných druhov dát medzi e-learningovými platformami

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Zhao, Yajing. "Chaotic Model Prediction with Machine Learning." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.

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Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and propose the task of predicting future states of the chaotic system from limited observations. We explore two directions, answering two distinct fundamental questions of the system based on how informed we are about the underlying model. When we know the data is generated by the Lorenz System with unknown parameters, our task becomes parameter estimation (a white-box problem), or the ``inverse'' problem. When we know nothing about the underlying model (a black-box problem), our task becomes sequence prediction. We propose two algorithms for the white-box problem: Markov-Chain-Monte-Carlo (MCMC) and a Multi-Layer-Perceptron (MLP). Specially, we propose to use the Metropolis-Hastings (MH) algorithm with an additional random walk to avoid the sampler being trapped into local energy wells. The MH algorithm achieves moderate success in predicting the $\rho$ value from the data, but fails at the other two parameters. Our simple MLP model is able to attain high accuracy in terms of the $l_2$ distance between the prediction and ground truth for $\rho$ as well, but also fails to converge satisfactorily for the remaining parameters. We use a Recurrent Neural Network (RNN) to tackle the black-box problem. We implement and experiment with several RNN architectures including Elman RNN, LSTM, and GRU and demonstrate the relative strengths and weaknesses of each of these methods. Our results demonstrate the promising role of machine learning and modern statistical data science methods in the study of chaotic dynamic systems. The code for all of our experiments can be found on \url{https://github.com/Yajing-Zhao/}

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Noelle, David Charles. "A connectionist model of instructed learning /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9811797.

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Griffiths, Michael Edward. "Improving the asynchronous video learning model /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3518.pdf.

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Griffiths, Michael E. "Improving the Asynchronous Video Learning Model." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2048.

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Online education is popular from a consumer perspective, but there are elements of face-to-face instruction and assessment that are difficult to reproduce online (Bassoppo-Moyo 2006). The difficulty of reproducing valued elements of a face-to-face setting leads to concerns regarding the overall quality of the online learning experience. Videoconferencing is one technology that has been used to incorporate elements of a face-to-face environment. However, videoconferencing over the Internet is fraught with technical difficulties and live discussions remove one of the main benefits of distance education: time flexibility. A more recent development has been to use asynchronous video as a communications method in online courses. Griffiths and Graham (2009) described several pilots using asynchronous video in online courses at Brigham Young University. Asynchronous video conveys the verbal and nonverbal signals necessary for immediacy and social presence and retains the time flexibility benefit of distance education. Following the pilot studies, a prototype design theory titled the Asynchronous Video Learning Model (AVLM) was created for the use of asynchronous video in online courses. A study was designed to study a practical implementation of AVLM. The major purpose of the study was to observe and analyze the practical experiences of participants and improve the AVLM model. A class named IPT286 (Using Instructional Technology in Teaching) taught by the department of IP&T at BYU was redesigned to be an online class using AVLM. Data were gathered during the semester and then analyzed according to the methods described in this study. Results showed that many of the principles of the AVLM model were successfully implemented and led to positive experiences. Some elements of the model were not adequately implemented which led to some negative experiences. In addition, experiences led to new elements being added to the model. The study also revealed some interesting principles related to general learning theory. The data consistently revealed the importance of relationships in the learning process. Relationships between students and the instructor were shown to influence the student learning experience, and therefore the personality and style of the instructor impacted overall student learning to some degree.

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Nitesh, Varma Rudraraju Nitesh, and Boyanapally Varun Varun. "Data Quality Model for Machine Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18498.

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Context: - Machine learning is a part of artificial intelligence, this area is now continuously growing day by day. Most internet related services such as Social media service, Email Spam, E-commerce sites, Search engines are now using machine learning. The Quality of machine learning output relies on the input data, so the input data is crucial for machine learning and good quality of input data can give a better outcome to the machine learning system. In order to achieve quality data, a data scientist can use a data quality model on data of machine learning. Data quality model can help data scientists to monitor and control the input data of machine learning. But there is no considerable amount of research done on data quality attributes and data quality model for machine learning. Objectives: - The primary objectives of this paper are to find and understand the state-of-art and state-of-practice on data quality attributes for machine learning, and to develop a data quality model for machine learning in collaboration with data scientists. Methods: - This paper mainly consists of two studies: - 1) Conducted a literature review in the different database in order to identify literature on data quality attributes and data quality model for machine learning. 2) An in-depth interview study was conducted to allow a better understanding and verifying of data quality attributes that we identified from our literature review study, this process is carried out with the collaboration of data scientists from multiple locations. Totally of 15 interviews were performed and based on the results we proposed a data quality model based on these interviewees perspective. Result: - We identified 16 data quality attributes as important from our study which is based on the perspective of experienced data scientists who were interviewed in this study. With these selected data quality attributes, we proposed a data quality model with which quality of data for machine learning can be monitored and improved by data scientists, and effects of these data quality attributes on machine learning have also been stated. Conclusion: - This study signifies the importance of quality of data, for which we proposed a data quality model for machine learning based on the industrial experiences of a data scientist. This research gap is a benefit to all machine learning practitioners and data scientists who intended to identify quality data for machine learning. In order to prove that data quality attributes in the data quality model are important, a further experiment can be conducted, which is proposed in future work.

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Chen, Yang. "Improving student model for individualized learning." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066655/document.

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Les Environnements Informatiques pour l'Apprentissage Humain ont été utilisés pour améliorer l'apprentissage humain. Ils visent à accroître la performance des élèves en fournissant un enseignement individualisé. Il a été reconnu que l'apprentissage individualisé est plus efficace que l'apprentissage classique. L'utilisation de modèles d'étudiants pour capturer les connaissances des élèves sous-tend l'apprentissage individualisé. Différents modèles d'étudiants ont été proposés. Toutefois, une partie des informations de diagnostic issues du comportement des élèves est généralement ignorée par ces modèles. En outre, pour individualiser les parcours d'apprentissage des élèves, les modèles d'étudiants devraient capturer les structures préalables de compétences. Toutefois, l'acquisition de structures de compétences nécessite beaucoup d'efforts d'ingénierie de la connaissance. Nous améliorons les modèles d'étudiants pour l'apprentissage individualisé selon deux aspects. D'une part, afin d'améliorer la capacité de diagnostic d'un modèle de l'élève, nous introduisons les motifs d'erreur d'étudiants. Pour traiter le bruit dans les données de performance des élèves, nous étendons un modèle probabiliste en y intégrant les réponses erronées. Les résultats montrent que la fonction de diagnostic permet d'améliorer la précision de la prédiction des modèles d'étudiant. D'autre part, nous cherchons à découvrir des structures de compétences préalables à partir des données de performance de l'élève. C'est une tâche difficile, car les connaissances des élèves constituent une variable latente. Nous proposons une méthode en deux phases. Notre procédé est validé en l'appliquant à des données
Computer-based educational environments, like Intelligent Tutoring Systems (ITSs), have been used to enhance human learning. These environments aim at increasing student achievement by providing individualized instructions. It has been recognized that individualized learning is more effective than the conventional learning. Student models which are used to capture student knowledge underlie the individualized learning. In recent decades, various competing student models have been proposed. However, some diagnostic information in student behaviors is usually ignored by these models. Furthermore, to individualize learning paths, student models should capture prerequisite structures of fine-grained skills. However, acquiring skill structures requires much knowledge engineering effort. We improve student models for individualized learning with respect to the two aspects. On one hand, in order to improve the diagnostic ability of a student model, we introduce the diagnostic feature—student error patterns. To deal with the noise in student performance data, we extend a sound probabilistic model to incorporate erroneous responses. The results show that the diagnostic feature improves the prediction accuracy of student models. On the other hand, we target on discovering prerequisite structures of skills from student performance data. It is a challenging task, since student knowledge of a skill is a latent variable. We propose a two-phase method to discover skill structure from noisy observations. Our method is validated on simulated data and real data. In addition, we verify that prerequisite structures of skills can improve the accuracy of a student model

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Grappin, Edwin. "Model Averaging in Large Scale Learning." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLG001/document.

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Les travaux de cette thèse explorent les propriétés de procédures d'estimation par agrégation appliquées aux problèmes de régressions en grande dimension. Les estimateurs par agrégation à poids exponentiels bénéficient de résultats théoriques optimaux sous une approche PAC-Bayésienne. Cependant, le comportement théorique de l'agrégat avec extit{prior} de Laplace n'est guère connu. Ce dernier est l'analogue du Lasso dans le cadre pseudo-bayésien. Le Chapitre 2 explicite une borne du risque de prédiction de cet estimateur. Le Chapitre 3 prouve qu'une méthode de simulation s'appuyant sur un processus de Langevin Monte Carlo permet de choisir explicitement le nombre d'itérations nécessaire pour garantir une qualité d'approximation souhaitée. Le Chapitre 4 introduit des variantes du Lasso pour améliorer les performances de prédiction dans des contextes partiellement labélisés
This thesis explores properties of estimations procedures related to aggregation in the problem of high-dimensional regression in a sparse setting. The exponentially weighted aggregate (EWA) is well studied in the literature. It benefits from strong results in fixed and random designs with a PAC-Bayesian approach. However, little is known about the properties of the EWA with Laplace prior. Chapter 2 analyses the statistical behaviour of the prediction loss of the EWA with Laplace prior in the fixed design setting. Sharp oracle inequalities which generalize the properties of the Lasso to a larger family of estimators are established. These results also bridge the gap from the Lasso to the Bayesian Lasso. Chapter 3 introduces an adjusted Langevin Monte Carlo sampling method that approximates the EWA with Laplace prior in an explicit finite number of iterations for any targeted accuracy. Chapter 4 explores the statisctical behaviour of adjusted versions of the Lasso for the transductive and semi-supervised learning task in the random design setting

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Wang, Jiahao. "Vehicular Traffic Flow Prediction Model Using Machine Learning-Based Model." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42288.

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Intelligent Transportation Systems (ITS) have attracted an increasing amount of attention in recent years. Thanks to the fast development of vehicular computing hardware, vehicular sensors and citywide infrastructures, many impressive applications have been proposed under the topic of ITS, such as Vehicular Cloud (VC), intelligent traffic controls, etc. These applications can bring us a safer, more efficient, and also more enjoyable transportation environment. However, an accurate and efficient traffic flow prediction system is needed to achieve these applications, which creates an opportunity for applications under ITS to deal with the possible road situation in advance. To achieve better traffic flow prediction performance, many prediction methods have been proposed, such as mathematical modeling methods, parametric methods, and non-parametric methods. It is always one of the hot topics about how to implement an efficient, robust and accurate vehicular traffic prediction system. With the help of Machine Learning-based (ML) methods, especially Deep Learning-based (DL) methods, the accuracy of the prediction model is increased. However, we also noticed that there are still many open challenges under ML-based vehicular traffic prediction model real-world implementation. Firstly, the time consumption for DL model training is relatively huge compared to parametric models, such as ARIMA, SARIMA, etc. Second, it is still a hot topic for the road traffic prediction that how to capture the special relationship between road detectors, which is affected by the geographic correlation, as well as the time change. The last but not the least, it is important for us to implement the prediction system in the real world; meanwhile, we should find a way to make use of the advanced technology applied in ITS to improve the prediction system itself. In our work, we focus on improving the features of the prediction model, which can be helpful for implementing the model in the real word. Firstly, we introduced an optimization strategy for ML-based models' training process, in order to reduce the time cost in this process. Secondly, We provide a new hybrid deep learning model by using GCN and the deep aggregation structure (i.e., the sequence to sequence structure) of the GRU. Meanwhile, in order to solve the real-world prediction problem, i.e., the online prediction task, we provide a new online prediction strategy by using refinement learning. In order to further improve the model's accuracy and efficiency when applied to ITS, we provide a parallel training strategy by using the benefits of the vehicular cloud structure.

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Nguyen, Tri. "Learning tensions : a multilevel model of organisational learning : an empirical study." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/425925/.

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There is a growing recognition that the study of Organisational learning needs to be considered across three levels of analysis: individual, group, and organisational levels (March, 1991; Nonaka and Takeuchi, 1995; Crossan et al., 1999; 2011). Given the potential of multilevel research to extend the boundaries of the understanding of the field, this thesis aims to address how organisations learn as a multilevel system. The answers to the research inquiry were drawn from both theoretical works and by conducting an empirical investigation. To assist the investigation of the OL phenomenon in multilevel settings, a multilevel model of OL was proposed. The model provides analytical foci by specifying the learning tensions at the individual, group, and organisational levels. The model was employed in a case study of a Vietnamese public organisation, which had successfully undergone a business transformation. Through the contributions of this thesis, the author hopes to spark more interest in multilevel research of OL.

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Worrall, Lisa Jayne Rosalind. "Model of metacognition in lifelong e-learning." Thesis, University of Salford, 2005. http://usir.salford.ac.uk/26967/.

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Metacognition can be defined as “…thinking about thinking” or “…beliefs about beliefs” (Antaki and Lewis, 1986). The research aim of this thesis was to: 1) Discuss the philosophical foundations of knowledge, cognition and metacognition. 2) Put forward example learning and e-learning theories and models and discuss these with reference to Reeves’ (1997) original model of WWW based learning. 3) Provide a ‘focus beam’ of analysis of metacognition and lifelong e-learning. 4) Analyse the extended literature review and evaluate and discuss its potential contributions (and limitations) with reference to an extended and adapted version of Reeves’ (1997) model. 5) Analyse the empirical data and evaluate and discuss its potential contributions (and limitations) with reference an extended and adapted version of Reeves’ (1997) model. The ADAPT project consisted of forty learners, twenty six male and fourteen female, aged between eighteen and sixty years. The Sitec Training Ltd and Women’s Action Forum (WAF) subjects consisted of nine learners, of which four were male and five were female, aged between the ages of eighteen and sixty. The work of this thesis was built upon a research process of the literature and empirical data gathered from the ADAPT project (first) that highlighted the potential importance of metacognition within lifelong e-learning. This led to the additional empirical research from Sitec Training Ltd (second) and the Women’s Action Forum (WAF) (third) and an extended literature review. As a result of these works, the contribution of this thesis has put forward an extended and adapted version of Reeves’ (1997) model that attempts to re-address the current absence of lifelong, cyclical and flexible aspects of metacognitive processes within lifelong e-learning. This thesis has also put forward a skeletal practical model for the delivery of lifelong distance learning.

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Alexander, Miranda Abhilash. "Spectral factor model for time series learning." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209812.

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Today's computerized processes generate

massive amounts of streaming data.

In many applications, data is collected for modeling the processes. The process model is hoped to drive objectives such as decision support, data visualization, business intelligence, automation and control, pattern recognition and classification, etc. However, we face significant challenges in data-driven modeling of processes. Apart from the errors, outliers and noise in the data measurements, the main challenge is due to a large dimensionality, which is the number of variables each data sample measures. The samples often form a long temporal sequence called a multivariate time series where any one sample is influenced by the others.

We wish to build a model that will ensure robust generation, reviewing, and representation of new multivariate time series that are consistent with the underlying process.

In this thesis, we adopt a modeling framework to extract characteristics from multivariate time series that correspond to dynamic variation-covariation common to the measured variables across all the samples. Those characteristics of a multivariate time series are named its 'commonalities' and a suitable measure for them is defined. What makes the multivariate time series model versatile is the assumption regarding the existence of a latent time series of known or presumed characteristics and much lower dimensionality than the measured time series; the result is the well-known 'dynamic factor model'.

Original variants of existing methods for estimating the dynamic factor model are developed: The estimation is performed using the frequency-domain equivalent of the dynamic factor model named the 'spectral factor model'. To estimate the spectral factor model, ideas are sought from the asymptotic theory of spectral estimates. This theory is used to attain a probabilistic formulation, which provides maximum likelihood estimates for the spectral factor model parameters. Then, maximum likelihood parameters are developed with all the analysis entirely in the spectral-domain such that the dynamically transformed latent time series inherits the commonalities maximally.

The main contribution of this thesis is a learning framework using the spectral factor model. We term learning as the ability of a computational model of a process to robustly characterize the data the process generates for purposes of pattern matching, classification and prediction. Hence, the spectral factor model could be claimed to have learned a multivariate time series if the latent time series when dynamically transformed extracts the commonalities reliably and maximally. The spectral factor model will be used for mainly two multivariate time series learning applications: First, real-world streaming datasets obtained from various processes are to be classified; in this exercise, human brain magnetoencephalography signals obtained during various cognitive and physical tasks are classified. Second, the commonalities are put to test by asking for reliable prediction of a multivariate time series given its past evolution; share prices in a portfolio are forecasted as part of this challenge.

For both spectral factor modeling and learning, an analytical solution as well as an iterative solution are developed. While the analytical solution is based on low-rank approximation of the spectral density function, the iterative solution is based on the expectation-maximization algorithm. For the human brain signal classification exercise, a strategy for comparing similarities between the commonalities for various classes of multivariate time series processes is developed. For the share price prediction problem, a vector autoregressive model whose parameters are enriched with the maximum likelihood commonalities is designed. In both these learning problems, the spectral factor model gives commendable performance with respect to competing approaches.

Les processus informatisés actuels génèrent des quantités massives de flux de données. Dans nombre d'applications, ces flux de données sont collectées en vue de modéliser les processus. Les modèles de processus obtenus ont pour but la réalisation d'objectifs tels que l'aide à la décision, la visualisation de données, l'informatique décisionnelle, l'automatisation et le contrôle, la reconnaissance de formes et la classification, etc. La modélisation de processus sur la base de données implique cependant de faire face à d’importants défis. Outre les erreurs, les données aberrantes et le bruit, le principal défi provient de la large dimensionnalité, i.e. du nombre de variables dans chaque échantillon de données mesurées. Les échantillons forment souvent une longue séquence temporelle appelée série temporelle multivariée, où chaque échantillon est influencé par les autres. Notre objectif est de construire un modèle robuste qui garantisse la génération, la révision et la représentation de nouvelles séries temporelles multivariées cohérentes avec le processus sous-jacent.

Dans cette thèse, nous adoptons un cadre de modélisation capable d’extraire, à partir de séries temporelles multivariées, des caractéristiques correspondant à des variations - covariations dynamiques communes aux variables mesurées dans tous les échantillons. Ces caractéristiques sont appelées «points communs» et une mesure qui leur est appropriée est définie. Ce qui rend le modèle de séries temporelles multivariées polyvalent est l'hypothèse relative à l'existence de séries temporelles latentes de caractéristiques connues ou présumées et de dimensionnalité beaucoup plus faible que les séries temporelles mesurées; le résultat est le bien connu «modèle factoriel dynamique». Des variantes originales de méthodes existantes pour estimer le modèle factoriel dynamique sont développées :l'estimation est réalisée en utilisant l'équivalent du modèle factoriel dynamique au niveau du domaine de fréquence, désigné comme le «modèle factoriel spectral». Pour estimer le modèle factoriel spectral, nous nous basons sur des idées relatives à la théorie des estimations spectrales. Cette théorie est utilisée pour aboutir à une formulation probabiliste, qui fournit des estimations de probabilité maximale pour les paramètres du modèle factoriel spectral. Des paramètres de probabilité maximale sont alors développés, en plaçant notre analyse entièrement dans le domaine spectral, de façon à ce que les séries temporelles latentes transformées dynamiquement héritent au maximum des points communs.

La principale contribution de cette thèse consiste en un cadre d'apprentissage utilisant le modèle factoriel spectral. Nous désignons par apprentissage la capacité d'un modèle de processus à caractériser de façon robuste les données générées par le processus à des fins de filtrage par motif, classification et prédiction. Dans ce contexte, le modèle factoriel spectral est considéré comme ayant appris une série temporelle multivariée si la série temporelle latente, une fois dynamiquement transformée, permet d'extraire les points communs de façon fiable et maximale. Le modèle factoriel spectral sera utilisé principalement pour deux applications d'apprentissage de séries multivariées :en premier lieu, des ensembles de données sous forme de flux venant de différents processus du monde réel doivent être classifiés; lors de cet exercice, la classification porte sur des signaux magnétoencéphalographiques obtenus chez l'homme au cours de différentes tâches physiques et cognitives; en second lieu, les points communs obtenus sont testés en demandant une prédiction fiable d'une série temporelle multivariée étant donnée l'évolution passée; les prix d'un portefeuille d'actions sont prédits dans le cadre de ce défi.

À la fois pour la modélisation et pour l'apprentissage factoriel spectral, une solution analytique aussi bien qu'une solution itérative sont développées. Tandis que la solution analytique est basée sur une approximation de rang inférieur de la fonction de densité spectrale, la solution itérative est basée, quant à elle, sur l'algorithme de maximisation des attentes. Pour l'exercice de classification des signaux magnétoencéphalographiques humains, une stratégie de comparaison des similitudes entre les points communs des différentes classes de processus de séries temporelles multivariées est développée. Pour le problème de prédiction des prix des actions, un modèle vectoriel autorégressif dont les paramètres sont enrichis avec les points communs de probabilité maximale est conçu. Dans ces deux problèmes d’apprentissage, le modèle factoriel spectral atteint des performances louables en regard d’approches concurrentes.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Jasso, Hector. "A reinforcement learning model of gaze following." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3259369.

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Thesis (Ph. D.)--University of California, San Diego, 2007.
Title from first page of PDF file (viewed June 22, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 104-116).

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Cora, Vlad M. "Model-based active learning in hierarchical policies." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/737.

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Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games. Game designers find it very natural to adopt a divide-and-conquer philosophy of specifying hierarchical policies, where decision modules can be constructed somewhat independently. The process of choosing the parameters of these modules manually is typically lengthy and tedious. The hierarchical reinforcement learning (HRL) field has produced elegant ways of decomposing policies and value functions using semi-Markov decision processes. However, there is still a lack of demonstrations in larger nonlinear systems with discrete and continuous variables. To narrow this gap between industrial practices and academic ideas, we address the problem of designing efficient algorithms to facilitate the deployment of HRL ideas in more realistic settings. In particular, we propose Bayesian active learning methods to learn the relevant aspects of either policies or value functions by focusing on the most relevant parts of the parameter and state spaces respectively. To demonstrate the scalability of our solution, we have applied it to The Open Racing Car Simulator (TORCS), a 3D game engine that implements complex vehicle dynamics. The environment is a large topological map roughly based on downtown Vancouver, British Columbia. Higher level abstract tasks are also learned in this process using a model-based extension of the MAXQ algorithm. Our solution demonstrates how HRL can be scaled to large applications with complex, discrete and continuous non-linear dynamics.

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Schmidt, Mark. "Graphical model structure learning using L₁-regularization." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27277.

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This work looks at fitting probabilistic graphical models to data when the structure is not known. The main tool to do this is L₁-regularization and the more general group L₁-regularization. We describe limited-memory quasi-Newton methods to solve optimization problems with these types of regularizers, and we examine learning directed acyclic graphical models with L₁-regularization, learning undirected graphical models with group L₁-regularization, and learning hierarchical log-linear models with overlapping group L₁-regularization.

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Landelius, Tomas. "Reinforcement Learning and Distributed Local Model Synthesis." Doctoral thesis, Linköpings universitet, Bildbehandling, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54348.

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Reinforcement learning is a general and powerful way to formulate complex learning problems and acquire good system behaviour. The goal of a reinforcement learning system is to maximize a long term sum of instantaneous rewards provided by a teacher. In its extremum form, reinforcement learning only requires that the teacher can provide a measure of success. This formulation does not require a training set with correct responses, and allows the system to become better than its teacher. In reinforcement learning much of the burden is moved from the teacher to the training algorithm. The exact and general algorithms that exist for these problems are based on dynamic programming (DP), and have a computational complexity that grows exponentially with the dimensionality of the state space. These algorithms can only be applied to real world problems if an efficient encoding of the state space can be found. To cope with these problems, heuristic algorithms and function approximation need to be incorporated. In this thesis it is argued that local models have the potential to help solving problems in high-dimensional spaces and that global models have not. This is motivated with the biasvariance dilemma, which is resolved with the assumption that the system is constrained to live on a low-dimensional manifold in the space of inputs and outputs. This observation leads to the introduction of bias in terms of continuity and locality. A linear approximation of the system dynamics and a quadratic function describing the long term reward are suggested to constitute a suitable local model. For problems involving one such model, i.e. linear quadratic regulation problems, novel convergence proofs for heuristic DP algorithms are presented. This is one of few available convergence proofs for reinforcement learning in continuous state spaces. Reinforcement learning is closely related to optimal control, where local models are commonly used. Relations to present methods are investigated, e.g. adaptive control, gain scheduling, fuzzy control, and jump linear systems. Ideas from these areas are compiled in a synergistic way to produce a new algorithm for heuristic dynamic programming where function parameters and locality, expressed as model applicability, are learned on-line. Both top-down and bottom-up versions are presented. The emerging local models and their applicability need to be memorized by the learning system. The binary tree is put forward as a suitable data structure for on-line storage and retrieval of these functions.

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Kubilinskienė, Svetlana. "Extended metadata model for digital learning resources." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120402_093822-17816.

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The key aim of using information technology (IT) in learning is to increase the learning quality and efficiency, to facilitate a learner’s and a teacher’s work. We can distinguish two main directions of IT application in training: (1) if applying IT we strive to improve the traditional methods; (2) if new methods are developed that are applicable only if IT is used. In both cases, sharing the good experience of teachers and mastering of the learning methods are of great importance. The research work is meant for solving the problems of using methodological resources and learning methods, which arise due to insufficiency of information in the metadata repositories of learning object (LO). The main models of LO metadata standards, used to describe digital learning resources in a formal way, have been analyzed and compared. Scientific and practical principles for applied model design of LO metadata standards have been explored. The content LO developing models that ensure LO compatibility have been considered. The empirical research performed enabled us to determine a further trend of research and influenced the creation of an extended LO metadata applied model. The design process of a metadata applied model consists of the following phases: 1) determination of sets of metadata elements that describe methodological resources and learning method objects, 2) composition of controlled vocabularies, aimed at description of metadata elements, with a view to ensure the compatibility... [to full text]
Pagrindinis informacinių technologijų (IT) naudojimo mokymuisi tikslas – didinti mokymosi kokybę ir efektyvumą, lengvinti besimokančiojo ir mokytojo darbą. Galima išskirti dvi IT taikymo ugdymui kryptis: 1) kai naudojant IT siekiama gerinti tradicinius metodus 2) kai sukuriami nauji metodai, kuriuos taikyti įmanoma tik naudojant IT. Abiem atvejais svarbus mokytojų gerosios patirties dalijimasis, mokymosi metodų įvaldymas. Disertacinis darbas skirtas metodinių išteklių ir mokymosi metodų naudojimo problemoms, kylančioms dėl informacijos nepakankamumo mokymosi objektų (MO) metaduomenų saugyklose, spręsti. Išanalizuoti ir palyginti pagrindiniai MO metaduomenų standartų modeliai, naudojami skaitmeniniams mokymosi ištekliams formaliuoju būdu aprašyti. Ištirti MO metaduomenų standartų taikymo modelių sudarymo moksliniai ir praktiniai principai. Išanalizuoti turinio MO kūrimo modeliai, kurie užtikrina MO suderinamumą. Atliktas empirinis tyrimas leido nustatyti tolesnę tyrimo kryptį ir turėjo įtakos išplėsto MO metaduomenų taikomojo modelio kūrimui. Metaduomenų taikomojo modelio projektavimo procesą sudaro šie etapai: 1) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų elementų aibių išskyrimas; 2) valdomųjų žodynų, skirtų metaduomenų elementams aprašyti, formavimas siekiant užtikrinti metaduomenų suderinamumą; 3) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų lyginamoji analizė; 4) išplėsto MO metaduomenų modelio išbaigimas ir diegimas... [toliau žr. visą tekstą]

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Makris, Dimitrios. "Learning an activity-based semantic scene model." Thesis, City University London, 2004. http://eprints.kingston.ac.uk/7781/.

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Mohd, Alwi Najwa Hayaati. "E-learning stakeholders information security vulnerability model." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7387.

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The motivation to conduct this research has come from awareness that the Internet exposes the e-learning environment to information security threats and vulnerabilities. Information security management as practised as a top down approach in many organisations tend to detach of people’s responsibility in ensuring the security of e-learning. Literature has pointed out that people’s behaviour required to be addressed to control the information security threats. This research proposes an ISM human behaviour model for e-learning provider in public universities in Malaysia. With socio technical reflection, this model aims to improve the implementation and management of information security in e-learning taking consideration of the user perspective. This research consists of four phases, the Planning phase, Data Collection and Analysis Phase, Model development Phase and Discussion and Conclusion Phase. A pilot study highlighted data confidentiality difficulties and pointed to data collection by using existing public from multiple sources. Six multi-method studies were conducted to generate the dimensions for the model development. Review from expert confirmed the research findings and validated the practicality of addressing people behaviours in information security management. This research contributes to better understanding of the people complexity in information security. The research suggests that the culture view of individual is significant in preparing information security management. This model makes clear the influence of people towards security threats and vulnerabilities. This approach can guide on what can be done to improve the stakeholder’s participation and responsibilities on securing e-learning. This research is also extending the existing knowledge of information security and e-learning fields by analytically focussing on the intersection of both fields. New knowledge about the security in e-learning environment from the users’ perspective is derived.

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Tudevdagva, Uranchimeg. "Structure Oriented Evaluation Model for E-Learning." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-146901.

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Volume 14 of publication series EINGEBETTETE, SELBSTORGANISIERENDE SYSTEME is devoted to the structure oriented evaluation of e-learning. For future knowledge society, beside creation of intelligent technologies, adapted methods of knowledge transfer are required. In this context e-learning becomes a key technology for development of any education system. E-learning is a complex process into which many different groups with specific tasks and roles are included. The dynamics of an e-learning process requires adjusted quality management. For that corresponding evaluation methods are needed. In the present work, Dr.Tudevdagva develops a new evaluation approach for e-learning. The advantage of her method is that in contrast to linear evaluation methods no weight factors are needed and the logical goal structure of an elearning process can be involved into evaluation. Based on general measure theory structure oriented score calculation rules are derived. The so obtained score function satisfies the same calculation rules as they are known from normalised measures. In statistical generalisation, these rules allow the structure oriented calculation of empirical evaluation scores based on checklist data. By these scores the quality can be described by which an e-learning has reached its total goal. Moreover, a consistent evaluation of embedded partial processes of an e-learning becomes possibly. The presented score calculation rules are part of a eight step evaluation model which is illustrated by pilot samples. U. Tudevdagva’s structure oriented evaluation model (SURE model) is by its embedding into the general measure theory quite universal applicable. In similar manner, an evaluation of efficiency of administration or organisation processes becomes possible.

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Follett, Stephen James. "A computational model of learning in Go." Thesis, University of South Wales, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343412.

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Rizzi, Raymundo Caroline. "SAFEL : a Situation-Aware Fear Learning model." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/65705/.

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This thesis proposes a novel and robust online adaptation mechanism for threat prediction and prevention capable of taking into consideration complex contextual and temporal information in its internal learning processes. The proposed mechanism is a hybrid cognitive computational model named SAFEL (Situation-Aware FEar Learning), which integrates machine learning algorithms with concepts of situation-awareness from expert systems to simulate both the cued and contextual fear-conditioning phenomena. SAFEL is inspired by well-known neuroscience findings on the brain's mechanisms of fear learning and memory to provide autonomous robots with the ability to predict undesirable or threatening situations to themselves. SAFEL's ultimate goal is to allow autonomous robots to perceive intricate elements and relationships in their environment, learn with experience through autonomous environmental exploration, and adapt at execution time to environmental changes and threats. SAFEL consists of a hybrid architecture composed of three modules, each based on a different approach and inspired by a different region (or function) of the brain involved in fear learning. These modules are: the Amygdala Module (AM), the Hippocampus Module (HM) and the Working Memory Module (WMM). The AM learns and detects environmental threats while the HM makes sense of the robot's context. The WMM is responsible for combining and associating the two types of information processed by the AM and HM. More specifically, the AM simulates the cued conditioning phenomenon by creating associations between co-occurring aversive and neutral environmental stimuli. The AM represents the kernel of emotional appraisal and threat detection in SAFEL's architecture. The HM, in turn, handles environmental information at a higher level of abstraction and complexity than the AM, which depicts the robot's situation as a whole. The information managed by the HM embeds in a unified representation the temporal interactions of multiple stimuli in the environment. Finally, the WMM simulates the contextual conditioning phenomenon by creating associations between the contextual memory formed in the HM and the emotional memory formed in the AM, thus giving emotional meaning to the contextual information acquired in past experiences. Ultimately, any previously experienced pattern of contextual information triggers the retrieval of that stored contextual memory and its emotional meaning from the WMM, warning the robot that an undesirable situation is likely to happen in the near future. The main contribution of this work as compared to the state of the art is a domain-independent mechanism for online learning and adaptation that combines a fear-learning model with the concept of temporal context and is focused on real-world applications for autonomous robotics. SAFEL successfully integrates a symbolic rule-based paradigm for situation management with machine learning algorithms for memorizing and predicting environmental threats to the robot based on complex temporal context. SAFEL has been evaluated in several experiments, which analysed the performance of each module separately. Ultimately, we conducted a comprehensive case study in the robot soccer scenario to evaluate the collective work of all modules as a whole. This case study also analyses to which extent the emotional feedback of SAFEL can improve the intelligent behaviour of a robot in a practical real-world situation, where adaptive skills and fast/flexible decision-making are crucial.

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Gilja, Vikash. "Learning and applying model-based visual context." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33139.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 53).
I believe that context's ability to reduce the ambiguity of an input signal makes it a vital constraint for understanding the real world. I specifically examine the role of context in vision and how a model-based approach can aid visual search and recognition. Through the implementation of a system capable of learning visual context models from an image database, I demonstrate the utility of the model-based approach. The system is capable of learning models for "water-horizon scenes" and "suburban street scenes" from a database of 745 images.
by Vikash Gilja.
M.Eng.

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Weintraub, Ben Julian. "Learning control applied to a model helicopter." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/49921.

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Eser, Ercan. "Criminality-oriented terrorist learning : an interactive model." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/38811/.

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This thesis, focusing on the reasons beyond immediate terrorist and criminal events, studies ‘how’ and ‘why’ terrorist organizations (TOs) and organized crime groups (OCGs) act, react and evolve. It adopts a ‘criminality oriented approach’ that puts discrete pieces of terrorism under a microscopic examination and explains terrorist learning of criminality: how tacit knowledge required for terror tactics and organized crime is processed and saved in the secret domains of TOs and OCGs and how the knowledge is accessed and learned by other illegal organizations. Using Akers’ social learning theory, it explains that TOs and OCGs influence each other through a hybrid network structure and they learn non-traditional activities that require knowledge, skills and techniques (organized crime for TOs and terrorism for OCGs) through associations. It also argues that the associations among them result in the appropriation of tactics and modus operandi, and that the closer association of the two groups may cause the mutation of both organizations. It develops a dynamic model that explains the relationship between terrorism and organized crime and the mutative behaviours of TOs and OCGs. Depicting the present and future capabilities of TOs and OCGs and possible future forms of both terrorism and organized crime threats, it offers pathways to prevent TOs from learning and to strengthen counterterrorism measures.

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Wongse-Ek, Woraluck. "Towards a trust model in e-learning." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/400246/.

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When a student is faced with uncertainty in the trustworthiness of a learning activity to meet their intended learning goals, it may cause anxiety and a lack of confidence in the learning. A student’s trust in the learning activity is needed to reduce this uncertainty. This work develops a conceptual trust model for e-learning activities. The proposed student’s trust model is the Learning Outcome-based Trust (LOT) model. The antecedents of trust are represented based on the intended learning outcome (ILO) structures and are used to estimate the trustworthiness values of the learning activity. Once values based on the antecedents of trust are known, these values are used to assess how much the student can trust the learning activity. The LOT model was evaluate in two real learning situation: (1) where information about the trustworthiness of the learning activity was ambiguous, and (2) where information about the trustworthiness of the learning activity was clear. Students’ trust mainly related to their propensity to trust and their prior knowledge when the trustworthiness of the learning activity was ambiguous. In contrast, students’ trust mainly related to their perceived trustworthiness of the learning activity when the trustworthiness of the learning activity was clear. The LOT model showed significant prediction of student’s trust. In addition, when the student learning path was used, trust was predicted significantly better than when the learning path was not given. The LOT model may have useful application in recommendation systems or intelligent tutoring systems.

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Bengtsson, Ivar. "Autonomous Overtaking with Learning Model Predictive Control." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.

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We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built from scratch in Python3 and simulated for evaluation purposes. Optimization problems are modelled and solved using the Gurobi 9.0 Python API gurobipy. The results show that LMPC can be successfully applied to the overtaking problem, with improved performance at each iteration. However, the first proposed alternative modelling approach does not improve computational times as was the intention. The second one does but fails in other areas.
Vi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.

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Jensen, Sara Lyn. "Learning Russian Case Endings Through Model Sentences." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2000.pdf.

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Meng, Zhaoxin. "A deep learning model for scene recognition." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.

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Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene recognition tasks. Because scene images often contain multiple objects, there may be more useful local semantic information in the convolutional layers of the network, which may be lost in the full connected layers. Therefore, this paper improved the traditional architecture of CNN, adopted the existing improvement which enhanced the convolution layer information, and extracted it using Fisher Vector. Then this thesis introduced the idea of transfer learning, and tried to introduce the knowledge of two different fields, which are scene and object. We combined the output of these two networks to achieve better results. Finally, this thesis implemented the method using Python and PyTorch. This thesis applied the method to two famous scene datasets. the UIUC-Sports and Scene-15 datasets. Compared with traditional CNN AlexNet architecture, we improve the result from 81% to 93% in UIUC-Sports, and from 79% to 91% in Scene- 15. It shows that our method has good performance on scene recognition tasks.

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Warner-Metzger, C., B. C. Reed, John Paul Abner, Janet Todd, and Michele R. Moser. "PCIT training: Applying a Learning Collaborative Model." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etsu-works/4978.

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Roberts, Irma. "Performance management : a connected professional learning model." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2006. https://ro.ecu.edu.au/theses/324.

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Increasingly school principals are more accountable for the performance of both their staff and students. In Australia and most OECD countries, the performance management process is identified as a mandatory vehicle for accountability and improving teacher professional development to advance student achievements. Yet, professional development is identified as a secondary discourse in performance management models, while accountability is arguably the dominant discourse. The purpose of this portfolio is the development of a blended performance management model that meets teachers' needs. The model seeks to shift the emphasis from accountability to professional development and bring the two discourses into a more compatible relationship.

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Dissertations / Theses on the topic '3P model of learning'

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