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Olteanu, Alin. "A Peircean theory of learning." Thesis, University of Roehampton, 2015. https://pure.roehampton.ac.uk/portal/en/studentthesis/a-peircean-theory-of-learning(a3afed52-8626-4918-b41a-ca350502d46d).html.
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I develop a theory of learning grounded in Charles Peirce’s semiotics. This endeavour comes in the context of the iconic (phenomenological) turn in semiotics, which resulted in a Peircean renaissance, and of the growing semiotic trend in education. Peirce’s semiotics offers insights into the phenomenon of learning and contains an implicit philosophy of education. The application of Peirce’s phenomenological categories to education reveals the semiosic character of education. Learning, education, and research constitute a triad, having the structure of a sign (phenomenon of signification). As such, they are correspondingly governed by Peirce’s three criteria of evolution: chance, necessity, and love. Therefore, Peirce’s theory of education can only be understood in the context of his theory of evolution. I develop three central arguments: (1) that according to Peirce’s taxonomy of signs, learning is the evolution of signification from the Icon sign type to the Argument sign type, (2) that learning is the Universe’s way of discovering itself through life forms, thus being both an evolutionary factor and an explanation for the emergence of life and (3) that learning can only be fulfilled in self-denying love for the other. Using Peirce’s taxonomy of signs I analyse the student/teacher relation, explaining how the passage from Icon to Argument proceeds and how learning is fulfilled in self-denying love.
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Liang, Annie. "Economic Theory and Statistical Learning." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493561.
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This dissertation presents three independent essays in microeconomic theory. Chapter 1 suggests an alternative to the common prior assumption, in which agents form beliefs by learning from data, possibly interpreting the data in different ways. In the limit as agents observe increasing quantities of data, the model returns strict solutions of a limiting complete information game, but predictions may diverge substantially for small quantities of data. Chapter 2 (with Jon Kleinberg and Sendhil Mullainathan) proposes use of machine learning algorithms to construct benchmarks for “achievable" predictive accuracy. The paper illustrates this approach for the problem of predicting human-generated random sequences. We find that leading models explain approximately 10-15% of predictable variation in the problem. Chapter 3 considers the problem of how to interpret inconsistent choice data, when the observed departures from the standard model (perfect maximization of a single preference) may emerge either from context-dependencies in preference or from stochastic choice error. I show that if preferences are “simple" in the sense that they consist only of a small number of context-dependencies, then the analyst can use a proposed optimization problem to recover the true number of underlying context-dependent preferences.Economics
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Carlucci, Lorenzo. "Some cognitively-motivated learning paradigms in Algorithmic Learning Theory." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.68 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3220797.
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Rossiter, P. G. "Organisational improvement through learning organisation theory." Thesis, University of Salford, 2007. http://usir.salford.ac.uk/2256/.
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A research study was conducted of the management theories and quality philosophies that have been expounded throughout the twentieth century. This study included the modem thinking for quality improvement and business excellence to include the modem concept of Learning Organisations. This research project was undertaken with the aim of producing a framework based on the culture of Learning Organisation Theory and including within it the external influences on such a culture. The framework consisted on a core of human values, divided into five areas that are deemed important to learning organisations. These were surrounded by the basic values of Trust, Honesty and Openness thus protecting the core from outside influence. Elements from traditional management systems theory provided the outer casing for the framework, these elements influencing the core for both good and bad. The contents of the framework were then studied in three organisations of differing background with a view to firmly establishing the elements and areas within the framework for validity in these three organisations. The common theme between all the organisations chosen was that they had all in the recent past been involved in major management and internal change. One study involved the development of a questionnaire and supporting matrices in order to identify the areas and elements of the framework, thus establishing their existence. Active research techniques were used in the other studies in order to establish both 'why' the elements are important and any interrelationship between the areas. As a result of these studies suggestions for modification to the framework were established in order to strengthen the thinking and these were encompassed in to the framework. Probably the most significant of these changes was the inclusion of 'Leadership' as being a major factor in the filtering of undesirable elements. The outcome from the research is that the aim was achieved and a framework was developed that, for the first time, was drawn up in such a way that the elements and areas can easily be recognised and an understanding of what they represent is clearly shown. The reasons as to why these elements are important are also established. This is regarded as an advancement in this field of study.
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Gilli, Mario. "Equilibrium and learning in game theory." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389813.
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Coleman, Donnie Steve. "Technological Immersion Learning: A Grounded Theory." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/75155.
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The Technological Immersion Learning Theory (TILT) was developed through a classic grounded theory study in the seminal tradition of Glaser and Strauss (1967) and Glaser (1978, 1992, 1998, 2001, 2007). The purpose of the study was to investigate an exemplary case of self-determined technology enthusiasts in the hopes of generating a substantive grounded theory that conceptualizes their experiences and concerns. Twelve unstructured interviews of amateur radio enthusiasts from the eastern United States provided the initial / primary data for this study. Experimenting and self-teaching in technological activities was highlighted as the main concern of the participants. The basic social process (BSP) of technological immersion learning (TIL) emerged as a theoretical construct and core variable that illuminates the experiences of individuals immersed in a community of practice, where hands-on engagement with technology is a primary activity. Adventuring, Affirmation, Doing Technology, Experimenting, Overcoming Challenge, Self-teaching, and Social Networking were properties of technological immersion learning that interact dialectically in an amplifying causal loop, with Problem solving and Designing as active sub processes in response to unmet challenges. TIL occurs cyclically in three stages, beginning with Induction, a credentialing stage wherein the neophyte is prepared with the necessary knowledge and skill to become a novice participant in an activity. The transition from Induction into the Immersion phase is a status passage whereby the novice is absorbed into the technical culture of the group and commences autonomous active participation in hands-on experimenting. Hands-on experiences with experimenting, problem solving and social interactions provide diverse learning and affirmation for the doer and multiple sources of feedback that promote sustained engagement. The transition into the Maturation phase proceeds gradually over time, with prolonged engagement and cumulative gains in knowledge, skill, and experience. Maturation is a quasi-stable state that remains responsive to new contexts as a random-walk process, wherein trigger events can initiate new cycles of technological immersion learning in a perpetually evolving process of personal development. Engagement, Empowerment, and Self-Actualization are underlying dimensions of the TIL basic social process that provide the impetus for continued persistence and personal development.Ph. D.
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Chakeri, Alireza. "Scalable Unsupervised Learning with Game Theory." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6616.
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Recently dominant sets, a generalization of the notion of the maximal clique to edge-weighted graphs, have proven to be an effective tool for unsupervised learning and have found applications in different domains. Although, they were initially established using optimization and graph theory concepts, recent work has shown fascinating connections with evolutionary game theory, that leads to the clustering game framework. However, considering size of today's data sets, existing methods need to be modified in order to handle massive data. Hence, in this research work, first we address the limitations of the clustering game framework for large data sets theoretically. We propose a new important question for the clustering community ``How can a cluster of a subset of a dataset be a cluster of the entire dataset?''. We show that, this problem is a coNP-hard problem in a clustering game framework. Thus, we modify the definition of a cluster from a stable concept to a non-stable but optimal one (Nash equilibrium). By experiments we show that this relaxation does not change the qualities of the clusters practically.
Following this alteration and the fact that equilibriums are generally compact subsets of vertices, we design an effective strategy to find equilibriums representing well distributed clusters. After finding such equilibriums, a linear game theoretic relation is proposed to assign vertices to the clusters and partition the graph. However, the method inherits a space complexity issue, that is the similarities between every pair of objects are required which proves practically intractable for large data sets. To overcome this limitation, after establishing necessary theoretical tools for a special type of graphs that we call vertex-repeated graphs, we propose the scalable clustering game framework. This approach divides a data set into disjoint tractable size chunks. Then, the exact clusters of the entire data are approximated by the clusters of the chunks. In fact, the exact equilibriums of the entire graph is approximated by the equilibriums of the subsets of the graph. We show theorems that enable significantly improved time complexity for the model. The applications include, but are not limited to, the maximum weight clique problem, large data clustering and image segmentation. Experiments have been done on random graphs and the DIMACS benchmark for the maximum weight clique problem and on magnetic resonance images (MRI) of the human brain consisting of about 4 million examples for large data clustering. Also, on the Berkeley Segmentation Dataset, the proposed method achieves results comparable to the state of the art, providing a parallel framework for image segmentation and without any training phase. The results show the effectiveness and efficiency of our approach.
In another part of this research work, we generalize the clustering game method to cluster uncertain data where the similarities between the data points are not exactly known, that leads to the uncertain clustering game framework. Here, contrary to the ensemble clustering approaches, where the results of different similarity matrices are combined, we focus on the average utilities of an uncertain game. We show that the game theoretical solutions provide stable clusters even in the presence of severe uncertainties. In addition, based on this framework, we propose a novel concept in uncertain data clustering so that every subset of objects can have a ''cluster degree''. Extensive experiments on real world data sets, as well as on the Berkeley image segmentation dataset, confirm the performance of the proposed method.
And finally, instead of dividing a graph into chunks to make the clustering scalable, we study the effect of the spectral sparsification method based on sampling by effective resistance on the clustering outputs. Through experimental and theoretical observations, we show that the clustering results obtained from sparsified graphs are very similar to the results of the original non-sparsified graphs. The rand index is always at about 0.9 to 0.99 in our experiments even when lots of sparsification is done.
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Neykov, Matey. "Three Aspects of Biostatistical Learning Theory." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467395.
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In the present dissertation we consider three classical problems in biostatistics and statistical learning - classification, variable selection and statistical inference.
Chapter 2 is dedicated to multi-class classification. We characterize a class of loss functions which we deem relaxed Fisher consistent, whose local minimizers not only recover the Bayes rule but also the exact conditional class probabilities. Our class encompasses previously studied classes of loss-functions, and includes non-convex functions, which are known to be less susceptible to outliers. We propose a generic greedy functional gradient-descent minimization algorithm for boosting weak learners, which works with any loss function in our class. We show that the boosting algorithm achieves geometric rate of convergence in the case of a convex loss. In addition we provide numerical studies and a real data example which serve to illustrate that the algorithm performs well in practice.
In Chapter 3, we provide insights on the behavior of sliced inverse regression in a high-dimensional setting under a single index model. We analyze two algorithms: a thresholding based algorithm known as diagonal thresholding and an L1 penalization algorithm - semidefinite programming, and show that they achieve optimal (up to a constant) sample size in terms of support recovery in the case of standard Gaussian predictors. In addition, we look into the performance of the linear regression LASSO in single index models with correlated Gaussian designs. We show that under certain restrictions on the covariance and signal, the linear regression LASSO can also enjoy optimal sample size in terms of support recovery. Our analysis extends existing results on LASSO's variable selection capabilities for linear models.
Chapter 4 develops general inferential framework for testing and constructing confidence intervals for high-dimensional estimating equations. Such framework has a variety of applications and allows us to provide tests and confidence regions for parameters estimated by algorithms such as the Dantzig Selector, CLIME and LDP among others, non of which has been previously equipped with inferential procedures.Biostatistics
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Flynn, Michael. "Linguistics and General Process Learning Theory." University of Arizona Linguistics Circle, 1987. http://hdl.handle.net/10150/226547.
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This paper is sort of an extended footnote, with a faint Borgesian flavor. What I'm going to do is show how one rather prominent argument in the linguistics literature against one aspect of the research program of behaviorism fails to go through. But I'll also observe that this argument appears to have had no practical effect on linguistic investigations, and that many people seem to assume (tacitly, at least) that this argument fails anyway. So my remarks here don't move the field forward any, but what I hope they do is help to get us all a bit clearer about where we are. The argument I'll be examining, given by Noam Chomsky in Reflections on Language (Chomsky 1975), is against a point of view called "general process learning theory ", a view that regards one goal of psychological theorizing to be the discovery of laws of learning that hold across species and across domains of acquisition. Psychological theorizing is by no means a new development on the linguistics scene. It is true, I think, that in most cases the people who have thought about language (including but not limited to people we would call linguists) have done so against the backdrop of a psychological theory that they assumed to be at least on the right track, and the idea was often to see what you could make of language by applying the analytical tools that the given psychological theory made available. Bloomfield (1926) is an example of this. (For some discussion of Bloomfield's views on psychology, see Lyons 1978, chapter 3.) One also in this context thinks of Piaget, Skinner of course, as well as philosophers of the 17th and 18th centuries of both the continental Cartesian variety and the so-called British Empiricists. I also think it's true that Chomsky's impact on psychology is somewhat unusual in that the flow of influence is in the other direction; that is, the question is, "If human language is like this, then what must the mind be like ?" rather than the other way around. Be that as it may, Chomsky has been, by far and away, the leading expositor of the implications of linguistics for the study of the structure of the human mind. It goes without saying that the ramifications of this work have been very rich, the pivotal role of linguistics in the "cognitive sciences" being just one indication of its influence. One of the earliest engagements at discipline boundaries was Chomsky's forceful assault on B.F. Skinner's attempt to extend the domain of behaviorist psychology to human languages. It's this argument that I want to have another look at. To do this it will be useful to try to isolate several facets of the discussion. I should perhaps reiterate, for the connoisseurs of counterrevolution who I know are out there, that my conclusion will be a modest one. I will not be concluding that after all Skinner was right and Chomsky was wrong. On the contrary, I'm going to assume that this game is over, and has been for quite some time. My goal is to call attention to what I think is an Unsolved problem which acquires its interest because it bears on how we regard linguistics as influencing our judgment about the structure of the human mind.
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Kaiser, Alexander. "An enhanced theory of learning including learning from the future." IEEE computer society press, 2016. http://epub.wu.ac.at/4812/1/hicss%2Dpaper%2Dalexander.pdf.
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Previous studies showed that combining learning
based on experiences in the past with learning from
an envisioned future scenario results in more
innovative and radical ideas as well as in a higher
number of covered content domains. However, currently there is no holistic learning theory which integrates both sources of learning. The main purpose of our paper is to propose an enhanced theory of learning, linking the two most important sources of learning: learning from past experiences and learning from the future. Our suggested theory, which is based on the learning theory by Gregory
Bateson, will be described in detail. Moreover, we
will present some empirical experiences with the
enhanced theory of learning. (author's abstract)
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Smith, Susan M. "SME leaders' learning in networked learning : an actor-network theory and communities of practice theory informed analysis." Thesis, Lancaster University, 2011. http://eprints.lancs.ac.uk/61620/.
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This thesis brings a Communities of Practice perspective together with Actor-network theory to provide a rich understanding of the social learning processes of SME leaders within a networked learning programme. Networked learning as an educational approach is a growing area in higher education. The networked learning programme under investigation forms part of the knowledge exchange initiatives at Lancaster university management school. The research explores the learning process through a qualitative, inductive approach underpinned by an (online and offline) ethnography and is supported by qualitative interviews, the researcher‟s own reflections and other secondary data. The study focuses on three main issues. Firstly, it provides an in-depth understanding of the way a learning community comes together. Secondly, it shows how delegates learn through co-constructing knowledge and the practices within the learning community. It is proposed that the learning community constructs, learns and challenges the situated curriculum. This takes place through the process of legitimate peripheral participation. Gaining fuller participation leads to an increased identification with that of „leader‟. Thirdly, the study theorises four conceptual learning spaces to show where the delegates learn. They are conceived of as an effect of the delegates‟ engagement with the integrated learning model underpinning the networked learning programme. The thesis concludes with a discussion presenting a set of learning and design principles. These can be used to inform the design and thinking around networked learning and knowledge exchange. Combining the theoretical frameworks of Actor-network theory and Communities of Practice theory is unique in the context of exploring the learning processes within networked learning. This combination stretches aspects of the main tenets of each theory and offers contributions to all three theoretical frameworks.
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Pennington, Eva Patrice. "Brain-based learning theory the incorporation of movement to increase learning /." Lynchburg, Va. : Liberty University, 2010. http://digitalcommons.liberty.edu.
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Gibbs, Elizabeth Stephanie. "Unison, workplace learning and enhancing learning network theory : a case study." Thesis, Open University, 2011. http://oro.open.ac.uk/54229/.
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Following a review of the UK's long term skill requirements in 2006 (Leitch 2006), the government has been looking at ways to increase the UK's skills base through employer engagement in training and education. Trade unions and higher education institutions have had a vital role in this process through the establishment of a wide variety of collaborative projects aimed at enhancing basic, intermediate and higher skills. One example of such a partnership is the Learning Partnership Route (LPR) to Social Work qualification. Whilst a number of authors have detailed the role of trade unions in work-based learning partnerships with public sector organisations, including in social care (see, for example, Sutherland and Rainbird 2000), there is a shortage of academic literature relating specifically to UNISON's role in informal learning in these organisations. This research study uses a case study method applying a critical realist approach, to look in depth at one social care organisation in which the LPR was run. The key theory utilised for data analysis is the Learning Network Theory (LNT) (Poell et al 2000). The LNT is one theory which has been put forward to describe organisational learning in the literature and uses the actors, structures and processes to interpret and describe how learning is organised. The case study findings revealed how Unison's trusting relationship with the social care employer and their coherent notion of the concept of a learning organisation, enabled them to become involved in both formal and informal aspects of workplace learning. Evidence is also presented to critique and enhance the LNT, extending the LNT to incorporate the concepts of formal and informal learning.
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Foreman, Samuel Alfred. "Learning better physics: a machine learning approach to lattice gauge theory." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6944.
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In this work we explore how lattice gauge theory stands to benefit from new developments in machine learning, and look at two specific examples that illustrate this point. We begin with a brief overview of selected topics in machine learning for those who may be unfamiliar, and provide a simple example that helps to show how these ideas are carried out in practice.
After providing the relevant background information, we then introduce an example of renormalization group (RG) transformations, inspired by the tensor RG, that can be used for arbitrary image sets, and look at applying this idea to equilibrium configurations of the two-dimensional Ising model.
The second main idea presented in this thesis involves using machine learning to improve the efficiency of Markov Chain Monte Carlo (MCMC) methods. Explicitly, we describe a new technique for performing Hamiltonian Monte Carlo (HMC) simulations using an alternative leapfrog integrator that is parameterized by weights in a neural network. This work is based on the L2HMC ('Learning to Hamiltonian Monte Carlo') algorithm introduced in [1].
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Pietsch, James Roderick. "Collaborative learning in mathematics." Thesis, The University of Sydney, 2005. http://hdl.handle.net/2123/1088.
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This study looked at the implementation of a collaborative learning model at two schools in Sydney designed to realise the principles recommended by reform documents such as the Principles and Standards for School Mathematics (NCTM, 2000) and policy documents including Numeracy, A Priority for All (DETYA, 2000). A total of 158 year seven and year eight students ranging in age from 12 to 15 years old from two schools participated in the study. In all, seven classroom teachers participated in the study each completing two topics using the collaborative learning model. Four research questions were the focus of the current study. Three research questions were drawn from eight principles identified in the literature regarding what constitutes effective mathematics learning. These questions related to the nature of collaboration evident in each classroom, the level of motivation and self-regulation displayed by students in the different types of classrooms and the relationship between learning mathematics within the collaborative learning model and real-world mathematics. A final research question examined the degree to which the concerns of teachers relating to preparing students for examinations are met within the collaborative learning model. Several different data collection strategies were adopted to develop a picture of the different forms of activity evident in each classroom and the changes that took place in each classroom during and after the implementation of the collaborative learning model. These included classroom observations, interviews with student and teacher participants, questionnaires and obtaining test results. Both exploratory and confirmatory factor analysis were used to reduce the data collected. Factor scores and test results were compared using t-tests, ANOVAs and Mann Whitney nonparametric tests. Data collected from interviews and classroom observations were analysed using a grounded approach beginning with the open coding of phenomena. Leont’ev’s theoretical approach to activity systems (1972; 1978) was then used to describe the changing nature of classroom activity with the introduction of the collaborative learning model. Within the collaborative classrooms there were a greater number of mathematical voices participating in classroom discussions, a breaking down of traditional roles held by teachers and students, and dominant patterns of collaboration evident in each classroom reflecting pre-existing cultural ways of doing. Furthermore, there was some quantitative evidence suggesting that student levels of critical thinking, self-regulation and help seeking increased and students were also observed regulating their own learning as well as the learning of others. Classroom practice was also embedded in the cultural practice of preparing topic tests, enabling students to use mathematics within the context of a work group producing a shared outcome. Finally, there was quantitative evidence that students in some of the collaborative classes did not perform as well as students in traditional classrooms on topic tests. Comments from students and teachers, however, suggested that for some students the collaborative learning model enabled them to learn more effectively, although other students were frustrated by the greater freedom and lack of direction. Future research could investigate the effectiveness of strategies to overcome this frustration and the relationship between different types of collaboration and developing mathematical understanding.
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Pietsch, James Roderick. "Collaborative learning in mathematics." University of Sydney, 2005. http://hdl.handle.net/2123/1088.
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Doctor of Philosophy (PhD)This study looked at the implementation of a collaborative learning model at two schools in Sydney designed to realise the principles recommended by reform documents such as the Principles and Standards for School Mathematics (NCTM, 2000) and policy documents including Numeracy, A Priority for All (DETYA, 2000). A total of 158 year seven and year eight students ranging in age from 12 to 15 years old from two schools participated in the study. In all, seven classroom teachers participated in the study each completing two topics using the collaborative learning model. Four research questions were the focus of the current study. Three research questions were drawn from eight principles identified in the literature regarding what constitutes effective mathematics learning. These questions related to the nature of collaboration evident in each classroom, the level of motivation and self-regulation displayed by students in the different types of classrooms and the relationship between learning mathematics within the collaborative learning model and real-world mathematics. A final research question examined the degree to which the concerns of teachers relating to preparing students for examinations are met within the collaborative learning model. Several different data collection strategies were adopted to develop a picture of the different forms of activity evident in each classroom and the changes that took place in each classroom during and after the implementation of the collaborative learning model. These included classroom observations, interviews with student and teacher participants, questionnaires and obtaining test results. Both exploratory and confirmatory factor analysis were used to reduce the data collected. Factor scores and test results were compared using t-tests, ANOVAs and Mann Whitney nonparametric tests. Data collected from interviews and classroom observations were analysed using a grounded approach beginning with the open coding of phenomena. Leont’ev’s theoretical approach to activity systems (1972; 1978) was then used to describe the changing nature of classroom activity with the introduction of the collaborative learning model. Within the collaborative classrooms there were a greater number of mathematical voices participating in classroom discussions, a breaking down of traditional roles held by teachers and students, and dominant patterns of collaboration evident in each classroom reflecting pre-existing cultural ways of doing. Furthermore, there was some quantitative evidence suggesting that student levels of critical thinking, self-regulation and help seeking increased and students were also observed regulating their own learning as well as the learning of others. Classroom practice was also embedded in the cultural practice of preparing topic tests, enabling students to use mathematics within the context of a work group producing a shared outcome. Finally, there was quantitative evidence that students in some of the collaborative classes did not perform as well as students in traditional classrooms on topic tests. Comments from students and teachers, however, suggested that for some students the collaborative learning model enabled them to learn more effectively, although other students were frustrated by the greater freedom and lack of direction. Future research could investigate the effectiveness of strategies to overcome this frustration and the relationship between different types of collaboration and developing mathematical understanding.
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Deniz, Juan C. (Deniz Carlos) 1976. "Learning theory applications to product design modeling." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89269.
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Sloan, Robert Hal. "Computational learning theory : new models and algorithms." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/38339.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Includes bibliographical references (leaves 116-120).
by Robert Hal Sloan.
Ph.D.
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Morgan, Thomas V. "Supply Chain Learning: A Grounded Theory Analysis." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1248435/.
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Under the unifying theme of supply chain learning, this three essay dissertation extends scholarship by investigating these multi-tier relationships. Theory is emerged, grounded in data, gathered from functioning supply chains in an effort to provide scholars and practitioners with an increased understanding of the SCL phenomena. Essay 1, entitled "Supply Chain Learning: An Exploratory Literature Review" examines the current literature in an attempt to address the shortcomings and emerge areas that have been less explored and less understood. By exposing these areas of research opportunities using a grounded theory methodology, a framework was emerged allowing identification of the limitations of extant literature and providing a springboard for future research. This framework also allowed further investigation into the SCL processes and expansion of the current understanding by providing academia with a comprehensive review of the literature and revealing the shortcomings that exist related to SCL.
Using the framework emerged in Essay 1, Essay 2 entitled "Toward Supply Chain Learning: A Focus on the Customers of Logistics Service Providers" explores the rationalization and cognitive processes of senior level executives of firms utilizing national or global supply chains. These respondents are directly engaged in creating, establishing and operating relationships with third party logistics (3PL) providers within a functioning supply chain. By examining the relationships and processes from the point of view of customers of third party logistics providers, a unique perspective provides insight into these relationships. Using semi-structured interviews with these executives, grounded theory was once again used to emerge theory explaining the phenomena of SCL. In particular, this research examines the elements studied from the perspective of customers of third party logistics providers as they seek to develop new processes and solutions in hopes of obtaining a competitive advantage by adaptive learning with the help of their providers and trading partners. In addition, this research increases our understanding of SCL by examining a relationship between customers and 3PL providers, their experiences and outcomes.
Essay 3, entitled "Practical Application of Supply Chain Learning" focuses on the implications of the learning relationship and its impact on the practitioner. By providing the findings of the research in a context relatable to practitioners, this culmination of findings allows practitioners to relate the findings directly to their existing supply chains. Realizing that many supply chains are relationship driven, this research focuses on the findings of previous research to provide a more holistic view of the learning relationship process as it exists in multiple tiers of their existing supply chain. Providing a step-by-step explanation of the SCL process as emerged from previous research, executives are provided a tool to better identify, analyze and understand these processes as relatable in their existing environment.
As a methodical analysis of the IOL process, these essays provide the foundation for understanding the relationship process that exists between learning partners in a supply chain. Essay 1 provides basis for theory development by examining current literature and exposing the shortcomings while also emerging a preliminary framework on which to build future research. Essay 2 follows up on these deficiencies and attempts to saturate understanding of the IOL process, particularly from the point of view of 3PL customers in an existing supply chain. Essay 3 delivers these findings to executives in a relatable format, providing a holistic understanding of the phenomena. In summation, this dissertation provides theory emerged from data, explaining the learning relationship from the point of view of the customers of 3PL services, the cognitive dimensions and outcomes of these decisions as they relate to learning in the supply chain.
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Gyamerah, Jacquelyn. "Adolescent cigarette smoking and social learning theory /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487929745335807.
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Hussain, Z. "Sparsity in machine learning : theory and practice." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1444276/.
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The thesis explores sparse machine learning algorithms for supervised (classification and regression) and unsupervised (subspace methods) learning. For classification, we review the set covering machine (SCM) and propose new algorithms that directly minimise the SCMs sample compression generalisation error bounds during the training phase. Two of the resulting algorithms are proved to produce optimal or near-optimal solutions with respect to the loss bounds they minimise. One of the SCM loss bounds is shown to be incorrect and a corrected derivation of the sample compression bound is given along with a framework for allowing asymmetrical loss in sample compression risk bounds. In regression, we analyse the kernel matching pursuit (KMP) algorithm and derive a loss bound that takes into account the dual sparse basis vectors. We make connections to a sparse kernel principal components analysis (sparse KPCA) algorithm and bound its future loss using a sample compression argument. This investigation suggests a similar argument for kernel canonical correlation analysis (KCCA) and so the application of a similar sparsity algorithm gives rise to the sparse KCCA algorithm. We also propose a loss bound for sparse KCCA using the novel technique developed for KMP. All of the algorithms and bounds proposed in the thesis are elucidated with experiments.
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Tsividis, Pedro A. "Theory-based learning in humans and machines." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121813.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 123-130).
Humans are remarkable in their ability to rapidly learn complex tasks from little experience. Recent successes in Al have produced algorithms that can perform complex tasks well in environments whose simple dynamics are known in advance, as well as models that can learn to perform expertly in unknown environments after a great amount of experience. Despite this, no current AI models are able to learn sufficiently rich and general representations so as to support rapid, human-level learning on new, complex, tasks. This thesis examines some of the epistemic practices, representations, and algorithms that we believe underlie humans' ability to quickly learn about their world and to deploy that understanding to achieve their aims. In particular, the thesis examines humans' ability to effectively query their environment for information that helps distinguish between competing hypotheses (Chapter 2); children's ability to use higher-level amodal features of data to match causes and effects (Chapter 3); and adult human rapid-learning abilities in artificial video-game environments (Chapter 4). The thesis culminates by presenting and testing a model, inspired by human inductive biases and epistemic practices, that learns to perform complex video-game tasks at human levels with human-level amounts of experience (Chapter 5). The model is an instantiation of a more general approach, Theory-Based Reinforcement Learning, which we believe can underlie the development of human-level agents that may eventually learn and act adaptively in the real world.
by Pedro A. Tsividis.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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Korba, Anna. "Learning from ranking data : theory and methods." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009/document.
Full textAbstract:
Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classementsRanking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages
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Korba, Anna. "Learning from ranking data : theory and methods." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009.
Full textAbstract:
Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classementsRanking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages
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Tu, Zhuozhuo. "Towards Robust and Reliable Machine Learning: Theory and Algorithms." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28832.
Full textAbstract:
Machine learning models, especially deep neural networks, have achieved impressive performance across a variety of domains including image classification, natural language processing, and speech recognition. However, recent examples have shown that these models are susceptible to test-time shift such as adversarial attacks or distributional shift. Additionally, machine learning algorithms require having access to personal data, and the learned model can be discriminatory with respect to minority social groups, raising privacy and fairness risks. To tackle these issues, in this thesis, we study several topics on robustness and reliability in machine learning, with a focus on generalization, adversarial examples, distributional robustness and fairness (privacy).
We start with the generalization problem in recurrent neural networks. We propose new generalization bounds for recurrent neural networks based on matrix 1-norm and Fisher-Rao norm. Our bound has no explicit dependency on the size of networks and can potentially explain the effect of noise training on generalization of recurrent neural networks as demonstrated by our empirical results. We then move forward to dataset shift robustness, which involves adversarial examples and distributional shift. For adversarial examples, we theoretically analyze the adversarially robust generalization properties of machine learning models. For distributional shift, we focus on learning a robust model and propose new algorithms to solve Wasserstein distributionally robust optimization problem which apply to arbitrary level of robustness and general loss functions. Lastly, to ensure both privacy and fairness, we present a fairness-aware federated learning framework and provide an efficient and provably convergent algorithm to solve it. Experimental results show that our method can lead to significant benefits in practice in terms of both accuracy and fairness.
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Li, Xiao. "Regularized adaptation : theory, algorithms, and applications /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5928.
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Haight, Veronica D. "What Do Chief Learning Officers Do? An Exploratory Study of How Chief Learning Officers Build Learning Organizations." Thesis, The George Washington University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10258744.
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This study was designed to determine what it is that Chief Learning Officers do to build the learning organization by examining interview data from current Chief Learning Officers using the constructs of change and leadership. The study sample included current Chief Learning Officers who work for U.S. based organizations within the U.S. and have been in their current Chief Learning Officer position for at least two years.
The study used a qualitative, exploratory methodology combined with phone or face-to-face interviews in order to gather data. The data was analyzed using the Systems Learning Organization Model (Marquardt, 2011). 20 Chief Learning Officers were interviewed for approximately 60 minutes each and asked the same series of questions in order to further explore how Chief Learning Officers use leadership and change to build the learning organization.
The study findings show that Chief Learning Officers do four things to build the learning organization: 1. They themselves collaborate with others inside and outside of the organization, and encourage others to do so as well; 2. They assess and measure their learning and development programs on a consistent basis; 3. They seek and secure funding and other resources for their learning and development opportunities; 4. They have a vision for their learning organization, and realize that vision through strategy development and implementation.
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Stevenson, Geoffrey. "Learning to preach : social learning theory and the development of Christian preachers." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/5469.
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In this thesis I investigate contemporary education theory as a way of understanding formative influences in the development of Christian preachers. I suggest that concepts of communities of practice and legitimate peripheral participation, along with recognition of role models and mentors, have a part to play in the life-long project that is learning to preach. In my Introduction I consider a definition of preaching for the purpose of the research and some historical approaches to developing preachers. I examine in Chapter 2 adult learning principles and cognitively-oriented concepts, such as learning styles and the theory of multiple intelligences. In Chapters 3 and 4 social learning theories that I examine include imitation, the effect of role models, and the influence of the mentor or the coach. Further, I ask to what extent the development of the preacher, as in many other professions with agreed standards of competency, does and should take place within communities of practice where legitimate peripheral participation (as developed in the work of Jean Lave and Etienne Wenger) marks the developing preacher‘s sense of his or her own learning trajectory. After a fifth chapter on methodologies, these concepts are tested in three field studies that use a range of sociological research methods. I conduct in Chapter 6 quantitative analysis of questionnaires returned by Church of Scotland ministers, in Chapter 7 qualitative analysis of the published testimony of fifteen experienced preachers, and in Chapter 8 qualitative analysis of interviews with twelve young Methodist preachers. In my conclusion I develop a theologically nuanced version of Lave and Wenger‘s concept which I term a community of agreed sermonic enterprise. Principal practical recommendations deriving from this centre on creating supportive networks of reflective preaching practitioners, enhancing the provision of mentor-mentee relationships, and educating congregations for their role in shaping preachers.
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Polistina, Kim Joanne. "Outdoor Learning: A Theory of Community-Based Pro-Environmental Learning Through Leisure." Thesis, Griffith University, 2005. http://hdl.handle.net/10072/366542.
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The purpose of this thesis was to develop a theory of outdoor learning grounded in the everyday lives of community-based groups and individuals in Australian Western society. The groups involved in this research where from appreciative outdoor leisure and Indigenous communities in Queensland, Australia. These community-based groups engaged with environmental learning and education through non-formal or informal means of sharing pro-environmental knowledge and values. The theory discussed in this thesis was grounded in the outdoor lifestyles of the people involved in this research and examined the epistemological underpinnings of these lifestyles. The outdoor learning that the people in this research implemented on an ongoing basis expanded their own pro-environmental knowledge and values and also assisted others in society to increase their pro-environmental knowledge and values. This research identified that the outdoor learning practices of those in this research were linked intricately to their outdoor leisure and related everyday activities. A model of the symbiotic relationship between outdoor lifestyle contexts, outdoor leisure settings and outdoor learning practices has been developed. The participatory nature of the research and the development of the theoretical framework of outdoor learning required grounded theory methodology supported by a sub-action research process. This dual methodology process combined with the social action or change aim of the research and the need for critical reflection on the neoliberalist social system currently dominant in Australia firmly established the research within the critical theorist (structural) and social action/interpretivist paradigms. The compatibility of the dual methodology enhanced the ability of the research to provide the best possible avenue for voicing the worldviews of appreciative outdoor recreationists and Aboriginal and Torres Strait Islander peoples involved in this research. Twenty-nine non-Indigenous appreciative outdoor recreationists and nineteen Indigenous people were involved in this research. People discussed their lives, the environmental worldviews that underpinned them and their outdoor learning practices. Information was also gathered on the constraints imposed by the neoliberalist ideals of the Australian social system on the outdoor learning practices of the people in this research. This thesis also addressed gaps in the literature on the characteristics of community-based environmental education. In this literature people within the community are represented as those to be educated by formal education advocates rather than those who were able to be educators in their own right. The theory developed in this thesis rejected this 'learner' or 'student' label and explained how the people in this research implemented their own educator role through their outdoor learning practices that were manifested in their outdoor lifestyles and their appreciative outdoor leisure settings. The theoretical framework developed in this thesis explained the implementation of non-formal and informal outdoor learning practices that supported a two-way dialogue of pro-environmental knowledge and values being shared between people with pro-environmental knowledge and values and others with a growing interest. The social context for this two-way dialogue was found in the outdoor network groups with whom the people in this research interacted. These appreciative outdoor leisure and social networks provided strength for the maintenance of subcultures and cultures, working within the dominant Australian Western culture, to increase the environmental literacy of the wider social group. The strength of commitment to their outdoor learning practices was highlighted in the resilience and loyalty to the continuation of these practices despite strong constraints imposed by the maintenance of neo-liberalist ideologies in Australian society. This research indicated a number of Australians implement pro-environmental values, through their outdoor lifestyles, rather than supporting the economic rationalist values of materialism. This thesis has theorised the community-based outdoor learning practices adopted by particular appreciative outdoor recreationists and Indigenous Australians living in Queensland. Their voices contribute to the broader outdoor and environmental education discourse. This thesis has also validated appreciative outdoor leisure as a prominent setting for outdoor learning of pro-environmental knowledge and values and highlighted the valuable contribution leisure could make to the environmental education agenda in Australian society.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
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Howes, Andrew. "Learning task-action mappings by exploration." Thesis, Lancaster University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.331956.
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El-Banna, H. A. A. M. "The development of a predictive theory of science education based upon information processing theory." Thesis, University of Glasgow, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382005.
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Berrios, Andrew M. "Organizational Learning Theory and Districtwide Curriculum Reform: Principals' Perceptions." Thesis, Boston College, 2016. http://hdl.handle.net/2345/bc-ir:106801.
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Thesis advisor: Rebecca LowenhauptThis qualitative case study examined the organizational learning mechanisms utilized by a district superintendent and their impact on principals’ learning. Examining recent curriculum reform efforts, the study concentrated on a small sample of building principals within a mid-sized urban public school district. Grounded in both organizational and situated learning theories, the research focused on organizational learning mechanisms and the interplay created by their implementation through the analysis of interview data and documents. Findings highlighted how the superintendent interpreted and distributed information to principals. In addition, findings showed the impact that superintendent-initiated processes, behaviors, and structures had on principal learning. The study provided strong evidence that the superintendent under study took steps to create district structures to support organizational learning. Moreover, principal data showed the impact of these structures on principals’ perceived learning
Thesis (EdD) — Boston College, 2016
Submitted to: Boston College. Lynch School of Education
Discipline: Educational Leadership and Higher Education
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Kelly, Ian P. "Organizational Learning Theory and Districtwide Curriculum Reform: Teacher Learning and the Efficacy of Organizational Learning Mechanisms." Thesis, Boston College, 2016. http://hdl.handle.net/2345/bc-ir:106797.
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Thesis advisor: Rebecca LowenhauptThis qualitative case study examined the organizational learning mechanisms used by school and district leaders to support professional learning within the context of curriculum reform. Elements of organizational learning theory provided a conceptual framework through which the researcher explored how teachers learned and how district leaders supported their learning about a district-wide curriculum reform. Data were collected through document review and semi-structured interviews with eighteen professionals from an urban district in the Northeast. Findings showed that (a) the district implemented an integrated system of organizational learning mechanisms to support teacher/instructional coach learning relevant to curriculum reform efforts, (b) teachers and coaches perceived these learning mechanisms to be effective in supporting their learning and (c) teachers and coaches demonstrated varying levels of understanding regarding the district’s curriculum reform priorities. Recommendations included: (a) enhancements to school and district strategic planning documents, (b) connecting principals closely to the teaching and learning operations of the district and (c) implementing feedback mechanisms to monitor individual interpretations of district priorities
Thesis (EdD) — Boston College, 2016
Submitted to: Boston College. Lynch School of Education
Discipline: Educational Leadership and Higher Education
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He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.
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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning; (3) understanding the geometrical structures of the loss landscape that drives the trajectories of the dynamic systems, which sheds light in reconciling the over-representation and excellent generalizability of deep learning; and (4) discovering the interplay between generalization, privacy preservation, and adversarial robustness, which have seen rising concerns in deep learning deployment.
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Brecht, Matthew de. "Topological and Algebraic Aspects of Algorithmic Learning Theory." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120375.
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Schinkel, Maarten Pieter. "Disequilibrium theory reflections towards a revival of learning /." [Maastricht] : Maastricht : UPM, Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 2001. http://arno.unimaas.nl/show.cgi?fid=7802.
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Jankowska, Gierus Bogumila. "Learning with visual representations through cognitive load theory." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104827.
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This study examined two different strategies of learning with diagrams: drawing diagrams while learning or learning from pre-constructed diagrams. One hundred ninety six junior high school students were randomly placed in a condition either to draw while learning about how airplanes fly or to study from pre-constructed diagrams. Before the learning, students' prior knowledge and elaboration strategies were measured. During learning in either condition, students reported their mental effort. Afterwards, students' learning was tested on both a similar task and transfer task. Cook's (2006) theoretical framework, which combines prior knowledge and cognitive load theory on visual representations in science education, was used to analyze the results. Results showed that students' mental effort significantly increased in the drawing condition, yet results on the posttest were mixed. Students did not do better, and sometimes did worse, on the posttest measures when they learned by drawing diagrams versus using pre-constructed diagrams to learn. The exception was that students with low initial prior knowledge did do better. Elaborations strategies did not have an effect on students' achievement or mental effort in either condition.Cette étude a examiné deux stratégies différentes d'apprendre à l'aide des diagrammes: le dessin de diagrammes tout en apprenant ou en apprenant sur la base des diagrammes préconstruits. Cent quatre-vingt-seize étudiants de lycée ont été aléatoirement placés dans une condition où soit ils dessinaient tout en se renseignant sur la façon dont les avions volent ou étudiaient à partir des diagrammes préconstruits. Avant l'étude, les stratégies de connaissance et d'élaboration des étudiants ont été vérifiées. Pendant l'étude sous l'une ou l'autre des conditions, les étudiants signalaient leur effort mental. Suite à cela, l'étude des étudiants est examinée sur une tâche semblable et une tâche de transfert. Cadre théorique de Cook (2006), qui combine la théorie de la connaissance antérieure et de charge cognitive sur les représentations visuelles dans l'éducation de la science, ont été employés pour analyser les résultats. Les résultats ont prouvé que l'effort mental des étudiants a augmenté sensiblement sous condition de dessin, pourtant les résultats sur le post-test étaient mitigés. En règle générale, les étudiants ont fait plus ou moins mauvais sur les mesures de post-test quand ils ont appris en traçant des diagrammes au contraire de l'utilisation des diagrammes préconstruits pour apprendre. Cependant, les étudiants ayant une faible connaissance de base ont mieux exécuté le post-test en traçant leurs propres diagrammes. Les stratégies d'élaborations n'ont pas exercé d' effet sur l'accomplissement ou l'effort mental des étudiants pour chacune des conditions.
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Davies, Lindsay. "Adult teaching and learning theory : a psychoanalytic investigation." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11791/.
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The overarching aim of this thesis is to enhance the theoretical status of post-compulsory education by effecting a non-empirical, psychoanalytic examination of adult teaching and learning theories. The study arises out of the observation that – in comparison with disciplines within the Social Sciences, Arts and Humanities - psychoanalytic perspectives are both underemployed in practical contexts and have negligible theoretical impact within this field. The study provides a contribution to educational theory and practice through the development of a postmodern psychoanalytical methodology and its methods. These are operationalised through the development of a suite of tools based on Jung's (1971) the theory of psychological types. This involves the adaptation of Jung's psychological categories into epistemological characteristics that are then used to identify strengths, contradictions and omissions within theoretical material. The resulting Jungian Typological Instrument (JTI) is applied to selected case study topics that reflect contemporary discourses, theories and concepts within the field of adult teaching and learning. In order to demonstrate the effectiveness of the JTI it is applied to two case study topics and critically examines their theoretical underpinnings. The first of these topics is the 'micro' model of reflection-in-action, which is drawn from Schön's (1983) formulation of reflective practice. The larger, 'macro', discourse of lifelong learning forms the second case study as articulated in the UNESCO report Learning to Be (Faure et al. 1972) and the Green Paper The Learning Age (DfEE 1998). The insights and outcomes arising from these analyses form the evaluation process for the methods. As psychoanalytic approaches are not unproblematic, this investigation incorporates philosophical reflection and conceptual analysis as meta-level evaluation strategies for the methodological level of the study. This dual-level evaluation reveals that the proposed methodology and methods together offer a robust and trustworthy approach to theoretical analysis, which has potential applications for both theory and practice within the adult teaching and learning context.
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Cunningham, David Edward. "A grounded theory study of protected learning time." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3329/.
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Introduction: Protected learning time (PLT) has been adopted by a number of NHS primary health care organizations throughout the United Kingdom as a resource for learning. Primary health care teams are protected from service delivery by Out-of-hours services for a small number of afternoons per year. Learning events are generally of two types: practice-based PLT events organised by the primary health care team and usually held in practice premises; and large centrally organised meetings held in large conference venues, and arranged by a PLT committee. PLT schemes were started by NHS Ayrshire and Arran in 2002 after a pilot study in 2001 was considered successful. A quantitative evaluation of the PLT scheme in two Community Health Partnerships within NHS Ayrshire and Arran in 2004 showed a significant difference in the views of Administrative and Clerical staff (A & C staff) and practice managers compared with clinicians in the team. Only 41% of A & C staff and 51% of practice managers wanted PLT to continue in one of the areas surveyed. An additional questionnaire study answered by practice managers in 2005 in NHS Ayrshire and Arran suggested that attendance of community nurses (health visiting and district nursing teams) at practice-based PLT events had fallen sharply, and that only a few were attending regularly. The questionnaires were unable to give the reasons for the low attendance, nor could they explain why some wanted the scheme to end. Two research questions were developed to improve the understanding of what was happening during PLT: 1. What are the perceptions and experiences of A & C staff, and of practice managers with regards to PLT? 2. What are the perceptions and experiences of the community nursing team (community nurses and nursing managers) with regards to PLT? Method: A Charmazian grounded theory approach was adopted, both as a method of data analysis, and as a research strategy. The data collection consisted of two phases: A & C staff, and practice managers (2005); and the community nursing team (2007). Focus groups were recruited, and the interviews were audio-recorded and transcribed. Transcriptions were coded, and themes and categories of themes were constructed from the codes. Mind mapping software was used to show the connections between the participants’ quotes and the themes and categories. A grounded theory was then constructed from the three categories. Findings: 12 focus group interviews were held with a total of 88 staff members participating. Details of the categories constructed are as follows: Structures in primary health care Physical structures were important. There were perceptions of the organizational schism between individual practices and the community nursing team. Community nurses valued co-location with their general practice as this improved close working. Different working patterns of district nurses meant that they could not always be protected during PLT, and they felt their managers did not provide sufficient cover. The introduction of the 2004 GMS Contract emphasized the separation of community nurses from general practices. Some nurses felt that practice-based PLT was irrelevant as it was centred on the learning needs of the practice. Some practices were strongly hierarchical resulting in separate learning events for individual staff groups during PLT. Relationships in primary health care: Relationships between community nurses and practices varied greatly. Some health visitors felt very isolated from the general practice. Community nurses wanted to work closely with practices and wanted their work to be visible and valued. Relationships between A & C staff and GPs varied considerably. Those practices with a high degree of hierarchy found collective learning difficult to do. Other practices had good relationships between different staff groups, and made good use of PLT. Learning processes: In general, participants did not feel their learning needs were identified or acted upon. As a result, learning offered to them was usually considered irrelevant, and based on the needs of others. A & C staff found some events to be dull and uninteresting, when passive learning methods were employed. Some practice managers perceived a lack of resources for learning events, and pharmaceutical representatives were keen to provide learning for clinicians. In some teams, practice-based PLT could be uncomfortable for community nurses, and some felt unwelcome by GPs. Practice managers were considered to be the natural leaders of practice-based PLT. Grounded theory: A theory with three elements was constructed from the findings. Proximity was an important factor in the ability of teams to learn from each other. Those teams who were not co-located, or did not work together in the provision of patient care, found PLT to be difficult. Perceptions of power affected the experiences of PLT. GPs usually had learning based on needs, and they could influence who attended PLT with them, and what was learnt. Some staff groups had little power, namely A & C staff and community nurses, and at times, the quality of learning for these groups was low. Authenticity was important. Participants wanted PLT to be for the whole team and to involve everyone in learning together. Many were disappointed when this was not achieved, and considered it to be contrary to the original aims and objectives of the scheme. Comparisons with other theories: The grounded theory was compared to Bourdieu’s theory of practice. This helped with the understanding of issues relating to the element of power. The element of proximity had similarities to Wenger’s theory of Communities of Practice. Those primary health care teams who displayed high levels of proximity were working as a Community of Practice. Conclusions: A deeper understanding of participants’ perceptions and experiences was gained and explored by the thesis. A number of recommendations were made to improve PLT in the future. These included improved learning needs assessment and aiding practice managers with the delivery of practice-based events. Individuals within primary health care teams need to improve team-working and need learning to help them with this endeavour. Health authorities need to value teamwork more, and require to locate teams together to facilitate the delivery of primary health care.
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Park, Seongmin. "A hypertext learning system for theory of computation." Virtual Press, 1993. http://liblink.bsu.edu/uhtbin/catkey/897499.
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The Hypertext concept was introduced about 50 years ago. This thesis presents the development of a reference system using the Hypertext concept. HYATS (HYpertext Automata and Turing Theory Learning 5ys,em) is a system which helps users learn many topics in the area of theory of computation. The system is implemented by Guide which is a general purpose Hypertext system running on PC-Windows environment. HYATS also includes a Turing machine simulating program which was written by Dominique Atger as her Master's Thesis in 1993, so that users can actually experiment with Turing machines learned through HYATS. HYATS will be not only the reference system, but also the complete package of actual learning system. The motivation behind this project is to study basic concepts of a Hypertext system so that it will also contribute to G-Net research. HYATS can be used as a prototype for future development of versions of by using other Hypertext systems such as NoteCards.Department of Computer Science
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Leo, Valentine. "Incorporating learning theory into existing systems engineering models." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37661.
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Approved for public release; distribution is unlimitedSystems engineering and learning theories are two major disciplines that involve preparing people to solve problems. While learning theories and their elements are apparent in the field of systems engineering, limited work has been performed on the interactions and relationship between these two disciplines. This thesis aims to establish and discuss a relationship between systems engineering and learning theories over the key phases of a systems life cycle. This thesis discusses how organizations can use the information attained from the collaborative approach between systems engineering and learning theories to leverage practitioners work quality, capability, and decisions to help justify and improve key systems parameters.
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Schoenberg, Uta. "Learning, mobility and wage dynamics : theory and evidence." Thesis, University College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406249.
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Swann, Philip Howard. "Theory and practice of computer assisted language learning." Thesis, Open University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280634.
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Slonim, Donna K. "Learning from imperfect data in theory and practice." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11004.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 167-176).
by Donna Karen Slonim.
Ph.D.
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Herseth, Todd L. "Business ethics education and Mezirow's transformative learning theory." Thesis, University of South Dakota, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10131552.
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The purpose of this study was to determine if using intentional, transformational learning strategies in an undergraduate business ethics course improved the curriculum with respect to targeted, student learning outcomes. Since business schools have a social mandate to provide opportunities for ethical growth and development, improving the efficacy of business ethics education is of paramount importance. The importance of this mandate has been further highlighted in recent years by egregious instances of misconduct by business professionals whose actions have had obvious and profoundly negative impacts upon the stability of our financial systems and state of the world economy.
This was a quasi-experimental, quantitative study conducted at a university of approximately 8,000 students. The focus of the study was to measure the effects of intentional, transformational learning strategies on the occurrence of transformational learning and cognitive moral development among students enrolled in the university's online business ethics course. The intentional, transformational learning strategies utilized were those identified by David Warren Keller in a 2007 study and adapted to an online learning environment. The correlation between epistemological development based on the Perry Scheme (of William G. Perry Jr.) and the occurrence of transformational learning was also examined in this study.
While this curricular intervention was not found to have had a statistically significant impact on the targeted outcomes, a statistically significant correlation was observed between epistemological development and transformational learning. A principle conclusion of the researcher is that the online learning environment is the most likely explanation for the difference in the efficacy of the curricular intervention when comparing the results of the Keller study to the current study due to the affective dimensions of the student learning experience (central to transformational learning) in the online learning environment and the limitations inherent therein, which are detailed in the study. Finally, the correlation observed between epistemological development and transformational learning, while statistically significant, was inconclusive due to the absence of additional correlations which would have been expected, yet merits further study.
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Marchenkova, Ludmila Alexandrovna. "Interpreting dialogue: Bakhtin’s theory and second language learning." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1111777929.
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Zimmermann, Tom. "Inductive Learning and Theory Testing: Applications in Finance." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467320.
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This thesis explores the opportunities for economic research that arise from importing empirical methods from the field of machine learning.
Chapter 1 applies inductive learning to cross-sectional asset pricing. Researchers have documented over three hundred variables that can explain differences in cross-sectional stock returns. But which ones contain independent information? Chapter 1 develops a framework, deep conditional portfolio sorts, that can be used to answer this question and that is based on ideas from the machine learning literature, tailored to an asset-pricing application. The method is applied to predicting future stock returns based on past stock returns at different horizons, and short-term returns (i.e. the past six months of returns) rather than medium- or long-term returns are recovered as the variables that convey almost all information about future returns.
Chapter 2 argues that machine learning techniques, although focusing on predictions, can be used to test theories. In most theory tests, researchers control for known theories. In contrast, chapter 2 develops a simple model that illustrates how machine learning can be used to conduct an inductive test that allows to control for some unknown theories, as long as they are covered in some way by the data. The method is applied to the theory that realization utility and nominal loss aversion lead to the disposition effect (the propensity to sell winners rather than losers). An inductive test finds that short-term price trends and other features of the price history are more important to predict selling decisions than returns relative to purchase price.
Chapter 3 provides another perspective on the disposition effect in the more traditional spirit of behavioral finance. It assesses the implications of different theories for an investor's probability to sell a stock as a function of the stock's return and then tests those implications empirically. Three different approaches that have been used in the literature are shown to lead to the, at first sight, contradictory findings that the probability to sell a stock is either V-shaped or inverted V-shaped in the stock's return. Since these approaches compute different conditional probabilities, they can be reconciled, however, when the conditioning set is taken into account.Economics
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Marchenkova, Ludmila Alexandrovna. "Interpreting dialogue Bakhtin's theory and second language learning /." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1111777929.
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Thesis (Ph. D.)--Ohio State University, 2004.Document formatted into pages; contains x, 153 p.; also contains graphics. Includes bibliographical references. Abstract available online via OhioLINK's ETD Center; full text release delayed at author's request until 2010 March 25.
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Gordon, Susan Eve. "Understanding Students Learning Statistics: An Activity Theory Approach." Thesis, The University of Sydney, 1998. http://hdl.handle.net/2123/353.
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In this project I investigate university students orientations to learning statistics. The students who participated in my research were studying statistics as a compulsory component of their psychology course. My central thesis is that learning develops in the relationship between the thinking, feeling and acting person and the social, institutional and cultural contexts surrounding him or her. How students orient themselves or position themselves to learn statistics is reflected in their engagement with the learning task and their activities. These activities determine the quality of their learning and emerging knowledge. To understand student learning I draw on the powerful theories of Vygotsky (1962, 1978) and Leontev (1978, 1981). In particular, I extend and apply Leontev's construct of activity (Leontev, 1981). This suggests that individuals act in accordance with their purposes and needs which are shaped by and reflect histories and resources, both personal and cultural. My investigation consists of two studies. Study One is a qualitative exploration of the orientations to learning statistics of five older students. These students sought help with statistics at the Mathematics Learning Centre where I work. My case studies of these students are inseparable from my efforts to help them learn statistics. Study Two is grounded in Study One. The main source of data for this broader study is a survey which was completed by 279 psychology students studying statistics. In keeping with the theoretical framework, my methodology involves a holistic analysis of students and the milieu in which they act. My findings suggest relationships among students affective appraisals; their conceptions of statistics; their approaches to learning it; their evaluations and the outcomes of their actions. In Study One the relationships emerged from the students' descriptions. In Study Two I quantified the ways in which variables related to each other. Structure for the data was provided by means of correlations, factor analysis and cluster analysis. For this study I also interviewed students and teachers of statistics. My data support the systemic view of teaching and learning in context afforded by my theoretical perspective. Learning statistics involves the whole person (Semenov, 1978) and is inseparable from the arena of his or her actions. The goal of statistics education is surely to enable students to develop useful, meaningful knowledge. My findings suggest that for many of the participants in my project this goal was not being met. Most of these students reported their reluctance to learn statistics and described adopting primarily surface approaches to learning it. A range of conceptions of the subject was expressed, but for many of the students statistical meaning was evidently reduced to performance on assessment tasks. Such orientations to learning statistics may lead to it becoming irrelevant and inert information. For a few students, however, the experience of learning statistics led to self development and enhanced perspectives on the world in which we live. My project indicates the diversity of students' experiences. It raises issues as to why we teach statistics today and how the teaching and learning of statistics is being supported at university. //REFERENCES Leontev, A. N. (1978). Activity, Consciousness, and Personality. (M. J. Hall, Trans.). Englewood Cliffs, New Jersey: Prentice-Hall. Leontev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology, (pp. 37-71). New York: M. E. Sharpe. Semenov, N. (1978). An empirical psychological study of thought processes in creative problem-solving from the perspective of the theory of activity. Soviet Psychology, 16(1), 3-46. Vygotsky, L. S. (1962). Thought and Language. Cambridge, Massachusetts: The M.I.T. Press. Vygotsky, L. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.
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Gordon, Susan Eve. "Understanding Students Learning Statistics: An Activity Theory Approach." University of Sydney. School of Development and Learning, 1998. http://hdl.handle.net/2123/353.
Full textAbstract:
In this project I investigate university students orientations to learning statistics. The students who participated in my research were studying statistics as a compulsory component of their psychology course. My central thesis is that learning develops in the relationship between the thinking, feeling and acting person and the social, institutional and cultural contexts surrounding him or her. How students orient themselves or position themselves to learn statistics is reflected in their engagement with the learning task and their activities. These activities determine the quality of their learning and emerging knowledge. To understand student learning I draw on the powerful theories of Vygotsky (1962, 1978) and Leontev (1978, 1981). In particular, I extend and apply Leontev's construct of activity (Leontev, 1981). This suggests that individuals act in accordance with their purposes and needs which are shaped by and reflect histories and resources, both personal and cultural. My investigation consists of two studies. Study One is a qualitative exploration of the orientations to learning statistics of five older students. These students sought help with statistics at the Mathematics Learning Centre where I work. My case studies of these students are inseparable from my efforts to help them learn statistics. Study Two is grounded in Study One. The main source of data for this broader study is a survey which was completed by 279 psychology students studying statistics. In keeping with the theoretical framework, my methodology involves a holistic analysis of students and the milieu in which they act. My findings suggest relationships among students affective appraisals; their conceptions of statistics; their approaches to learning it; their evaluations and the outcomes of their actions. In Study One the relationships emerged from the students' descriptions. In Study Two I quantified the ways in which variables related to each other. Structure for the data was provided by means of correlations, factor analysis and cluster analysis. For this study I also interviewed students and teachers of statistics. My data support the systemic view of teaching and learning in context afforded by my theoretical perspective. Learning statistics involves the whole person (Semenov, 1978) and is inseparable from the arena of his or her actions. The goal of statistics education is surely to enable students to develop useful, meaningful knowledge. My findings suggest that for many of the participants in my project this goal was not being met. Most of these students reported their reluctance to learn statistics and described adopting primarily surface approaches to learning it. A range of conceptions of the subject was expressed, but for many of the students statistical meaning was evidently reduced to performance on assessment tasks. Such orientations to learning statistics may lead to it becoming irrelevant and inert information. For a few students, however, the experience of learning statistics led to self development and enhanced perspectives on the world in which we live. My project indicates the diversity of students' experiences. It raises issues as to why we teach statistics today and how the teaching and learning of statistics is being supported at university. //REFERENCES Leontev, A. N. (1978). Activity, Consciousness, and Personality. (M. J. Hall, Trans.). Englewood Cliffs, New Jersey: Prentice-Hall. Leontev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology, (pp. 37-71). New York: M. E. Sharpe. Semenov, N. (1978). An empirical psychological study of thought processes in creative problem-solving from the perspective of the theory of activity. Soviet Psychology, 16(1), 3-46. Vygotsky, L. S. (1962). Thought and Language. Cambridge, Massachusetts: The M.I.T. Press. Vygotsky, L. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.