Dissertations / Theses on the topic 'Policy learning'
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Molnár, Krisztina. "Essays on monetary policy and learning." Doctoral thesis, Universitat Pompeu Fabra, 2006. http://hdl.handle.net/10803/7341.
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Mi tesis se basa en los resultados de least squares learning, que modela agentes individuales como econometricos: los agentes funcionan como regresiones, usan datos disponibles para formar sus expectativas. En el primer capítulo de mi tesis demuestro que la presencia de principiantes de learning en una economía se puede racionalizar incluso en coexistencia con los agentes racionales. En el segundo capítulo, examino cuál es la implicación en la política monetaria óptima cuando los agentes privados siguen aprendiendo con least squares learning. Este capítulo demuestra que la política monetaria óptima bajo learning introduce unas nuevas características del comportamiento de la política que no son presentes cuando los agentes privados tienen expectativas racionales.My thesis builds on the results of the least squares learning literature, which models individual agents as econometricians: agents are running least squares regressions using available data in order to form their expectations. I the ¯first chapter of my thesis I show that the presence of learners in an economy can be rationalized even in coexistence with rational agents. In the second chapter, I examine what is the implication on optimal policy when private agents follow learning. This chapter shows that optimal monetary policy under learning introduces new features of policy behavior that are not present under rational expectations.
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Locarno, Alberto. "Learning, monetary policy and asset prices." Thesis, London School of Economics and Political Science (University of London), 2012. http://etheses.lse.ac.uk/341/.
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The dissertation examines several policy-related implications of relaxing the assumption that economic agents are guided by rational expectations. A first, introductory chapter presents the main technical issues related to adaptive learning. The second chapter studies the implications for monetary policy of positing that both the private sector and the central bank form their expectations through adaptive learning and that the central bank has private information on shocks to the economy but cannot credibly commit. The main finding of this chapter is that when agents learn adaptively a bias against activist policy arises. The following chapter focuses on large, non-linear models, where no unambiguous linear approximation eligible as perceived law of motion exists. Accordingly, there are heterogeneous expectations and the system converges to a misspecification equilibrium, affected by the communication strategies of the central bank. The main results are: (1) the heterogeneity of expectations persists even when a large number of observations are available; (2) the monetary policymaker has no incentive to be an inflation hawk; (3) partial transparency enhances welfare somewhat but full transparency does not. The final chapter adopts a model in which agents are fully informed and use Bayesian techniques to estimate the hidden states of the economy. The monetary policy stance is unobservable and state-independent, generating uncertainty among agents, who try to gauge it from inflation: a change in consumer prices that confirms beliefs reduces stock risk premia, while a change that contradicts beliefs drives the risk premia upward. This may generate a negative correlation between returns and inflation that explains the Fisher puzzle. The model is tested on US data. The econometric evidence suggests: (1) that a mimickingportfolio proxying for monetary policy uncertainty is a risk factor priced by financial markets; and (2) that conditioning on monetary uncertainty and fundamentals eliminates the Fisher puzzle.
3
Burke, Patrick Joseph. "Policy learning and policy change : advocacy groups and key moments in Irish homelessness policy." Thesis, Queen's University Belfast, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554345.
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This dissertation identifies three key moments of policy change in the area of homelessness in Ireland. The key change moments identified point to specific moments when official government policy in relation to homelessness explicitly changed. The first moment was the enactment of the 1988 Housing Act in which for the first time the state defined and recognised that homelessness. Second, the publication in 2000 of Homelessness - An Integrated Strategy - the first national integrated strategy on homelessness. At this moment the state formally recognised the multi- faceted nature of homelessness and the reality that a co-ordinated response was needed. Third, the publication of The Way home: A Strategy to Address Adult Homelessness in Ireland 2008-2013, which sets out 2010 as the target date for the elimination of rough sleeping and long term homelessness. I use Sabatier's Advocacy Coalition Framework as the theoretical framework for my analysis. Having identified the key moments of policy change in the area of homelessness in Ireland the critical questions of my dissertation emerge. First, what drives these moments? I identify a number of societal factors which drive the moments of policy change. First, the increased numbers and visibility of homelessness, second, the growing heterogeneity of the homelessness, third, the growing consensus on the need for a definition of homelessness, fourth, the growing influence of the non-profit sector as advocates and fifth, the existence of a culture of policy- orientated learning. The second critical question asks what characterizes the development of non-profit sector thinking and advocacy on homelessness. I note first that non-profits are united at the level of a core belief that everybody ought to have access to adequate housing, second, non-profits successfully garner support from the public through their campaigning work, third, there is a movement from the individualistic versus structuralist debate on the causes of homelessness, fourth, the sector has a deep knowledge of the many nexus between homelessness and other social issues, fifth, the sector constructs itself as 'the expert' on homelessness and a necessary partner to government. The third critical question explores the nature of the policy orientated learning in the lead up to these critical moments. I note that first, policy orientated learning is incremental, second, policy orientated learning is evidence based, third, policy orientated learning exhibits a deep commitment to a core belief and fourth, that is non conflictual at the level of this core belief. In conclusion the dissertation presents a number of recommendations which arise from the reached.
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Söderberg, Charlotta. "Environmental policy integration in bioenergy : policy learning across sectors and levels?" Doctoral thesis, Umeå universitet, Statsvetenskapliga institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-42810.
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A central principle within UN and EU policy is environmental policyintegration (EPI), aiming at integrating environmental aspirations, targetsand requirements into sector policy in order to promote sustainabledevelopment. The focus of this study is EPI in bioenergy policy. Bioenergy isa renewable energy source of increasing importance in the EU and Swedishenergy mix. At the same time, it is debated how environmentally friendlybioenergy really is. Furthermore, bioenergy can be considered both a multisectorand a multi-level case, since bioenergy is produced in many differentsectors and bioenergy policy is formulated and implemented on differentlevels. Therefore, EPI in bioenergy policy is here analysed over time in twosectors (energy and agriculture) and on three levels (EU, national, subnational).A cognitive, policy learning perspective on EPI is adopted, tracingEPI through looking for reframing of policy towards incorporatingenvironmental objectives in policy rhetoric and practice. Furthermore,institutional and political explanations for the development are discussed.Paper I analyses EPI in Swedish bioenergy policy within energy andagriculture. Paper II analyses institutional conditions for multi-sector EPI inSwedish bioenergy policy. Paper III analyses EPI in EU bioenergy policywithin energy and agriculture. Paper IV analyses sub-national EPI in thecase of the Biofuel Region in north Sweden. The material examined consistsof policy documents complemented by semi-structured interviews. Together, the four papers provide a more complex and holistic picture ofthe EPI process than in previous research, which mainly has focused onstudying EPI in single sectors and on single levels. The study shows thatpriorities are different on different levels; that EPI has varied over time; butthat EPI today is detectable within bioenergy policy in both studied sectorsand on all levels. Policy learning in bioenergy is found to be mainly a topdownprocess. Furthermore, policy coherence between sectors and levels;long-term goals; and concrete policy instruments are found to be importantboth for the EPI process as such and for the outcomes from this process.However, when attempting to marry different goals, such as growth, securityand sustainability, in line with the three-tiered (economic, social, ecologic)sustainable development concept, environmental aspects risks not to beprioritised when goal conflicts arise. The study proposes that future researchboth continues the analysis of multi-sector and multi-level EPI, and furtherexplores to what extent ecological sustainability is improved by EPI.
5
Lamoureux, Marcel. "Policy learning theory derived from Russian power sector liberalisation policy experience." Thesis, Glasgow Caledonian University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.726804.
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Lidström, Christian, and Hannes Leskelä. "Learning for RoboCup Soccer : Policy Gradient Reinforcement Learning inmulti-agent systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157469.
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Robo Cup Soccer is a long-running yearly world wide robotics competition,in which teams of autonomous robot agents play soccer against each other.This report focuses on the 2D simulator variant, where no actual robots are needed and the agents instead communicate with a server which keeps trackof the game state. RoboCup Soccer 2D simulation has become a major topic of research for articial intelligence, cooperative behaviour in multi-agent systems, and the learning thereof. Some form of machine learning is mandatory if you want to compete at the highest level, as the problem is too complex for manualconguration of a teams decision making.This report nds that PGRL is a common method for machine learning in Robo Cup teams, it is utilized in some of the best teams in Robo Cup. The report also nds that PGRL is an effective form of machine learning interms of learning speed, but there are many factors which affects this. Most often a compromise have to made between speed of learning and precision.Robo Cup Soccer är en årlig världsomspännande robotiktävling, i vilken lag av autonoma robotagenter spelar fotboll mot varandra. Denna rapport fokuserar på 2D-simulatorn, vilken är en variant där inga riktiga robotar behövs, utan där spelarklienterna istället kommunicerar med en server vilken håller reda på speltillståndet. RoboCup Soccer 2D simulation har blivit ett stort ämne för forskning inom articiell intelligens, samarbete och beteende i multi-agent-system, och lärandet därav. Någon form av maskininlärning är ett krav om man villkunna tävla på den högsta nivån, då problemet är för komplext för att beslutsfattandet ska kunna programmeras manuellt.Denna rapport finner att PGRL är en vanlig metod för maskininlärning i Robo Cup-lag, den används inom några av de bästa lagen i Robo Cup. Rapporten nner också att PGRL är en effektiv form av maskininlärningn är det gäller inlärningshastighet, men att det finns många faktorer som kan påverka detta. Oftast måste en avvägning ske mellan inlärningshastighet och precision.
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Tezel, Nezahat. "Policy Implications In The." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/12604944/index.pdf.
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This study explores the relatively new concept of &ldquoThe Learning Economy&rdquo
in the context of system of Innovation, which provides a basic understanding of all elements and their relations necessary to enhance the innovative capacity. This thesis aims to examine the structure and characteristics of ASELSAN (Electronic Industries Inc.) including i.e., firm-level technological activities. In the &lsquo
Learning Economy&rsquo
, rapid learning is the key factor for accelerating innovative capabilities and competitiveness for firms and nations. On the other hand, this concept is closely correlated with the &lsquo
New Economy&rsquo
, ICT (Information communication Technologies) that enhances the knowledge dissemination and learning. In this perspective, ASELSAN acquired high-level technological capabilities and rapid development such that it can be considered as a model for other firms in Turkey. Furthermore, this research aims to point out the &lsquo
Learning Process Model of ASELSAN&rsquo
comparing it with the catching-up firms in South Korea and emphasize transformation of technology and institutional structure in the period from 1980 to 2002. As an individual firm, &lsquo
ASELSAN&rsquo
is a leading firm in the defense industry as a system integrator
and the next step may be &lsquo
network-based&rsquo
learning process model. In summary, there could be policy lessons to be taken for other firms to become a &lsquo
learning organization and &lsquo
innovative firm&rsquo
.
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Caprioli, Francesco. "Optimal fiscal policy, limited commitment and learning." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7396.
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Esta tesis trata sobre cómo la autoridad fiscal debe fijar los impuestos distorsivos de manera óptima. El capítulo 1 analiza el problema de la política fiscal cuando el gobierno tiene un incentivo a hacer default con su deuda externa. El capítulo 2 trata sobre el problema de la política fiscal cuando los agentes no conocen cómo el gobierno fija las tasas impositivas. La principal conclusión que obtengo es que, en ambos contextos, el resultado de suavidad de las tasas, que es estándar en la literatura de imposición óptima, se rompe. Cuando los gobiernos no tienen una tecnología de compromiso, los impuestos responden a los incentivos de default; cuando los agentes poseen información parcial sobre el modelo subyacente de la economía, los impuestos dependen de sus expectativas sobre los mismos.This thesis is about how fiscal authority should optimally set dissorting taxes. Chapter 1 deals with the optimal fiscal policy problem when the government has an incentive to default on external debt. Chapter 2 deals with the optimal fiscal policy problem when households do not know how government sets taxes. The main conclusion I get is that, in each of these two contexts, the tax smoothing result, which is the standars result in the optimal taxation literature, is broken. When governments do not have a commitment technology taxes respond to the incentives to default; when agents have partial information about the underlying economic model, taxes depend on their beliefs about it.
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Pau, Jason. "Global antidumping use retaliation or policy learning? /." CONNECT TO ELECTRONIC THESIS, 2007. http://dspace.wrlc.org/handle/1961/4179.
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McGough, Bruce. "Learning, oil price shocks, and monetary policy /." view abstract or download file of text, 2000. http://wwwlib.umi.com/cr/uoregon/fullcit?p9987239.
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Thesis (Ph. D.)--University of Oregon, 2000.Typescript. Includes vita and abstract. Includes bibliographical references (leaves 143-145). Also available for download via the World Wide Web; free to University of Oregon users.
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Liu, Keqing. "Three essays on macroprudential policy and learning." Thesis, University of Exeter, 2017. http://hdl.handle.net/10871/28614.
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The first chapter proposes an alternative macroprudential policy in the framework of Gertler, Kiyotaki and Queralto (2012). In their model, the central bank subsidizes bank outside equity, where the subsidy rate is determined by the shadow cost of the deposit. We find that the alternative rule in which the subsidy rate responds to the aggregate bank outside equity ratio is welfare improving because it has a better stabilization effect on the bank asset deterioration after a nancial shock. We disentangle different channels through which macroprudential policies affect the economy and demonstrate that the better stabilization in the post-crisis economy has a positive effect on the economy in normal times through security prices. In the second chapter, we consider a model where producers set their prices based on their prediction of the aggregated price level and an exogenous variable, which can be a demand or a cost-push shock. To form their expectations, they use OLS-type econometric learning with bounded memory. We show that the aggregated price follows the random coe cient autoregressive process and we prove that this process is covariance stationary. This chapter comments on Angeloni and Faia (2013, Journal of Monetary Economics), a dynamic stochastic general equilibrium model with a risky banking sector. We identify the sources of inefficiency in the model and disentangle the channels through which banks choose a high level of leverage. We explain that their assumptions that generate banks over-borrowing feature lead to the return on assets and the bankruptcy probability that are unrealistically high. Next, we modify the model by incorporating the banking sector of Gertler and Karadi (2011) into the AF model and show that the calibration result improves.
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L'Hour, Jérémy. "Policy evaluation, high-dimension and machine learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLG008/document.
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Cette thèse regroupe trois travaux d'économétrie liés par l'application du machine learning et de la statistique en grande dimension à l'évaluation de politiques publiques. La première partie propose une alternative paramétrique au contrôle synthétique (Abadie and Gardeazabal, 2003; Abadie et al., 2010) sous la forme d'un estimateur reposant sur une première étape de type Lasso, dont on montre qu'il est doublement robuste, asymptotiquement Normal et ``immunisé'' contre les erreurs de première étape. La seconde partie étudie une version pénalisée du contrôle synthétique en présence de données de nature micro-économique. La pénalisation permet d'obtenir une unité synthétique qui réalise un arbitrage entre reproduire fidèlement l'unité traitée durant la période pré-traitement et n'utiliser que des unités non-traitées suffisamment semblables à l'unité traitée. Nous étudions les propriétés de cet estimateur, proposons deux procédures de type ``validation croisée'' afin de choisir la pénalisation et discutons des procédures d'inférence par permutation. La dernière partie porte sur l'application du Generic Machine Learning (Chernozhukov et al., 2018) afin d'étudier l'hétérogénéité des effets d'une expérience aléatoire visant à comparer la fourniture publique et privée d'aide à la recherche d'emploi. D'un point de vue méthodologique, ce projet discute l'extension du Generic Machine Learning à des expériences avec compliance imparfaiteThis dissertation is comprised of three essays that apply machine learning and high-dimensional statistics to causal inference. The first essay proposes a parametric alternative to the synthetic control method (Abadie and Gardeazabal, 2003; Abadie et al., 2010) that relies on a Lasso-type first-step. We show that the resulting estimator is doubly robust, asymptotically Gaussian and ``immunized'' against first-step selection mistakes. The second essay studies a penalized version of the synthetic control method especially useful in the presence of micro-economic data. The penalization parameter trades off pairwise matching discrepancies with respect to the characteristics of each unit in the synthetic control against matching discrepancies with respect to the characteristics of the synthetic control unit as a whole. We study the properties of the resulting estimator, propose data-driven choices of the penalization parameter and discuss randomization-based inference procedures. The last essay applies the Generic Machine Learning framework (Chernozhukov et al., 2018) to study heterogeneity of the treatment in a randomized experiment designed to compare public and private provision of job counselling. From a methodological perspective, we discuss the extension of the Generic Machine Learning framework to experiments with imperfect compliance
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Sarunas, Girdenas. "Three essays on monetary policy and learning." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/16503.
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The rst chapter co-authored with Tatiana Damjanovic studies optimal mon- etary policy in a New Keynesian model at the zero bound interest rate where households use cash alongside house equity borrowing to conduct transactions. The amount of borrowing is limited by a collateral constraint. When either the loan to value ratio declines or house prices fall, we observe a decrease in the money multiplier. We argue that the central bank should respond to the fall in the money multiplier and therefore to the reduction in house prices or the loan to collateral value ratio. We also nd that optimal monetary policy generates a large and per- sistent fall in the money multiplier in response to the drop in the loan to collateral value ratio. The second chapter is focused on a macroeconomic model with sticky prices, rms borrowing market and the labour market frictions. We study connection be- tween monetary policy and labour market under the negative nancial and the positive productivity shocks. We have found that the interest rate rule with in a- tion and labour market targeting performs better than the rules with the aggregate consumption and debt targeting and is closest to the optimal policy as compared to the other regimes in terms of the welfare measure. We demonstrate too that the sign of the coe¢ cient next to unemployment in the policy rule depends on the value of workers bargaining power. The third chapter co-authored with Tatiana Damjanovic and Keqing Liu uses the classical cobweb model framework to investigate properties of the transition matrix in the bounded memory econometric OLS-type learning. We de ne memory length as the number of past observations used to form a forecast and analytically prove that for any length, the eigenvalues of the transition matrix lie within the unit circle. In addition, we sketch the proof of stationarity of the cobweb model under bounded memory learning. Furthermore, we investigate the relationship between the volatility of forecasts and the length of memory and nd that shorter memory causes higher variance in both forecasts and estimates of the OLS parameters.
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Murrall-Smith, Sally. "Policy learning and the development of renewable energy policy in the United Kingdom." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/932.
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Despite the UK’s abundance of renewable energy sources and the imperative for renewable energy to make a significant contribution to addressing the problems of climate change and fossil-fuel dependency, renewable energy capacity in the UK has developed slowly compared with some other EU states. The UK has introduced a succession of policies to promote renewable energy, but so far these have failed to meet national and EU targets. This signals the need for detailed examination of the reasons for these ‘failures’ and, in particular, the extent, nature and constraints on ‘policy learning’ within UK renewable energy policy. Policy learning has emerged in recent years as an innovative way of exploring the roles of knowledge acquisition and use in policy change. This study examines the contribution of policy learning to the development of UK renewable energy policy. It is argued that interpreting UK renewable energy policy development through the lens of policy learning yields fresh perspectives on why policies develop in certain directions and not others. In so doing, it critically examines problems caused by failings in policy learning and identifies options for the further promotion of renewable energies in the UK. The study distinguishes four different forms of policy learning: technical, conceptual, social and political. Little research has been conducted on the characteristics of these different learning types, the conditions under which they occur, the psychological, institutional and cultural factors that stimulate or constrain learning, and how they interact to shape policy change. The study utilises a qualitative methodology to analyse and explain changes in UK renewable energy policy over the past 20 years. The main methods employed are content analysis of policy documents (including legislative acts and instruments, consultations and select committee reports); and semi-structured interviews with key stakeholders from government, industry, NGOs, academia and the media. It is argued in the thesis that UK energy policy has tended to become ‘locked’ into low-level forms of technical learning because current government learning mechanisms do not challenge the parameters of existing policy and, thus, fail to stimulate broader processes of conceptual and social learning that might encourage more radical policy change. These forms of policy learning are particularly constrained by hierarchical institutional structures that hinder communication and learning between policy areas. Furthermore, the current style of policy making for renewable energy in the UK privileges the interests of incumbent energy companies, giving them the ability to filter or block new ideas that do not align with their commercial interests. Political learning was shown to operate alongside other types of policy learning and to take multiple forms but focused predominantly on political risk management rather than political innovation: thus, it tended to narrow rather than extend the parameters of debate. These findings were used to develop a model of policy learning in UK renewable energy policy. This was used to conceptualise relationships between different learning types, highlight specific barriers to policy learning, and illustrate dynamics of policy learning and change that might be extended to other policy areas and countries. Finally, it is argued that many of the barriers identified might be overcome by fostering more evidence-based policy making and learning mechanisms that engage with a broader range of stakeholders to stimulate more pluralistic government processes.
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Rietig, Katharina. "Learning in governance : the role of policy entrepreneurs in European climate policy integration." Thesis, London School of Economics and Political Science (University of London), 2014. http://etheses.lse.ac.uk/3121/.
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Learning is frequently regarded as facilitating factor for policy outcomes across multiple levels of governance. Learning however competes with alternative explanations such as bargaining, actor’s interests and organisational objectives. This thesis examines from an institutional perspective the link between individual learning of policymakers and learning among governmental institutions and analyses to what extent learning matters for the policy outcome. It finds that policy entrepreneurs play a key role in transferring learning to the organisational level and in achieving policy outcomes. The empirical focus is on learning in climate policy integration, which carries increasing importance for effective environmental governance as it can help create synergies for economic development and climate mitigation. The European Union is a frontrunner in integrating climate objectives into energy, transport and agriculture policy via regulatory instruments setting overall targets and conditioning financial resources upon compliance. This thesis uses qualitative methods to examine learning in the policymaking aspects of climate policy integration at the examples of the Renewable Energy Directive, its controversial biofuels component and the greening measures in the Common Agricultural Policy. This research makes several original contributions to the agency aspects of environmental governance: the meta-theoretical framework on learning allows a more nuanced analysis of what learning aspects occur in governance such as knowledge- and experience-based learning versus changes in different types of underlying beliefs. It also allows determining the extent to which a policy outcome results from learning or alternative explanations. This contribution clarifies the under-researched link between the learning individual, changes in beliefs and the factors hindering learning from being transferred to the organisational level where policy decisions are made. Policy outcomes resulted predominantly from policy entrepreneurs using previously acquired knowledge and experience to achieve a policy outcome aligned with their pre-formed deeper beliefs and policy objectives. Overall, the thesis provides a fresh perspective on the relevance of learning in the policymaking process and of bureaucrats as policy entrepreneurs.
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Irani, Arya John. "Utilizing negative policy information to accelerate reinforcement learning." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53481.
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A pilot study by Subramanian et al. on Markov decision problem task decomposition by humans revealed that participants break down tasks into both short-term subgoals with a defined end-condition (such as "go to food") and long-term considerations and invariants with no end-condition (such as "avoid predators"). In the context of Markov decision problems, behaviors having clear start and end conditions are well-modeled by an abstraction known as options, but no abstraction exists in the literature for continuous constraints imposed on the agent's behavior.
We propose two representations to fill this gap: the state constraint (a set or predicate identifying states that the agent should avoid) and the state-action constraint (identifying state-action pairs that should not be taken). State-action constraints can be directly utilized by an agent, which must choose an action in each state, while state constraints require an approximation of the MDP’s state transition function to be used; however, it is important to support both representations, as certain constraints may be more easily expressed in terms of one as compared to the other, and users may conceive of rules in either form.
Using domains inspired by classic video games, this dissertation demonstrates the thesis that explicitly modeling this negative policy information improves reinforcement learning performance by decreasing the amount of training needed to achieve a given level of performance. In particular, we will show that even the use of negative policy information captured from individuals with no background in artificial intelligence yields improved performance.
We also demonstrate that the use of options and constraints together form a powerful combination: an option and constraint can be taken together to construct a constrained option, which terminates in any situation where the original option would violate a constraint. In this way, a naive option defined to perform well in a best-case scenario may still accelerate learning in domains where the best-case scenario is not guaranteed.
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Handa, Rhea. "A Lesson in Learning: Improving Learning Outcomes in India Via Pedagogical Innovation." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/scripps_theses/1105.
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When delivered well, education is key to addressing a host of individual and societal ills, from poverty and disease to crime and poor voter engagement. India has demonstrated considerable progress in improving various aspects of its primary education system, including infrastructure and buildings, teacher-student ratios, and school enrollment. However, student learning outcomes remain consistently low across the country. A review of the literature surrounding learning outcomes has highlighted gaps in school instruction and has shown the dire need for innovations in pedagogy and curriculum to improve student learning. This paper assesses the long-term impact of one such pedagogy, called Teaching at the Right Level (or TaRL), in the districts of five states of India via an ordered probit model and linear regressions. The quantitative model shows a positive and significant effect of TaRL exposure on learning levels and income, as hypothesized throughout this paper. Additionally, case studies of two students exposed to TaRL are explored to illustrate individual effects of the pedagogy.
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Alghamdi, Turki Mohammed K. "Policy-based runtime tracking for e-learning environments." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/8237.
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The Virtual Learning Environment (VLE) is a form of e-learning environment that is becoming widely adopted in higher educational institutions and universities. The term “Tracking” in relation to an e-learning context is the learner’s observation process of any possible interaction with learning activities. Learning activities are a collection of objects designed within e-learning environments to support learners in better understanding and fulfilling the learning objectives during the learning process. A tracking tool is an essential tool fixed within e- learning environments. Most current VLEs (e.g. MOODLE and Blackboard) have utilized similar tracking functions, which aim at recording statistical data for each learner. The current e-learning environments are unable to track individual learning activities where the tracked information can be used to support and guide learners. In this thesis, we propose a policy-based runtime tracking system. Such a tracking system is implemented as an integral part of an e-learning environment (e.g. MOODLE). Our proposed approach does the following: 1) keeps track of and captures the learning activity events and learner interaction events within a learning activity; 2) enforces a set of policies at runtime that specify how to manage the learning activities and the way the learners behave during them; and 3) provides the learners with supportive feedback in a timely manner. We present a computational model which defines the behaviour of the system’s components and describes the tracking mechanism applied in our proposed approach. We designed architecture for our proposed approach with respect to the computational model. We present learning activity policies based on the ECA model to be enforced at runtime; this is done in response to the captured events about either the learning activities or the interactions of learners within the learning activities. A policy-based enforcement mechanism is proposed where learning activity policies are specified to support and guide learners to achieve the learning objective and thus to meet such requirements. Finally, we present a case study based on a current e-learning environment to evaluate our approach.
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Jones, Michelle Suzette. "Professional collaborative learning : policy, practice and research perspectives." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/63108/.
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In this introduction to the publications selected for examination for the degree of PhD in Education, at the University of Warwick, I will begin by outlining some the contextual influences on my published work. During my career, spanning over 30 years, I have had the privilege to be a head-teacher, local authority adviser, government policy adviser and a researcher. The publications that follow therefore focus on professional collaborative learning from these different vantage points, as these have inevitably influenced my writing.
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Weber, Anke. "Essays on monetary policy committees, learning and expectations." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608450.
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Cuningham, Blake. "Evolutionary algorithms for optimising reinforcement learning policy approximation." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31170.
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Reinforcement learning methods have become more efficient in recent years. In particular, the A3C (asynchronous advantage actor critic) approach demonstrated in Mnih et al. (2016) was able to halve the training time of the existing state-of-the-art approaches. However, these methods still require relatively large amounts of training resources due to the fundamental exploratory nature of reinforcement learning. Other machine learning approaches are able to improve the ability to train reinforcement learning agents by better processing input information to help map states to actions - convolutional and recurrent neural networks are helpful when input data is in image form that does not satisfy the Markov property. The specific required architecture of these convolutional and recurrent neural network models is not obvious given infinite possible permutations. There is very limited research giving clear guidance on neural network structure in a RL (reinforcement learning) context, and grid search-like approaches require too many resources and do not always find good optima. In order to address these, and other, challenges associated with traditional parameter optimization methods, an evolutionary approach similar to that taken by Dufourq and Bassett (2017) for image classification tasks was used to find the optimal model architecture when training an agent that learns to play Atari Pong. The approach found models that were able to train reinforcement learning agents faster, and with fewer parameters than that found by OpenAI’s model in Blackwell et al. (2018) - a superhuman level of performance.
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Mink, Tarin L. "Using Service Learning to Teach Social Welfare Policy." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243281610.
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Aryasomayajula, Naga Srinivasa Baradwaj. "Machine Learning Models for Categorizing Privacy Policy Text." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535633397362514.
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Ueno, Tsuyoshi. "General Approach to Policy Evaluation via Statistical Learning." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/142136.
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Waterfield, Jacqueline. "Post-registration learning in physiotherapy : practice and policy." Thesis, Keele University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435153.
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The profession of physiotherapy is evolving in an era of evidence-based health C
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Hawasly, Majd. "Policy space abstraction for a lifelong learning agent." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9931.
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This thesis is concerned with policy space abstractions that concisely encode alternative ways of making decisions; dealing with discovery, learning, adaptation and use of these abstractions. This work is motivated by the problem faced by autonomous agents that operate within a domain for long periods of time, hence having to learn to solve many different task instances that share some structural attributes. An example of such a domain is an autonomous robot in a dynamic domestic environment. Such environments raise the need for transfer of knowledge, so as to eliminate the need for long learning trials after deployment. Typically, these tasks would be modelled as sequential decision making problems, including path optimisation for navigation tasks, or Markov Decision Process models for more general tasks. Learning within such models often takes the form of online learning or reinforcement learning. However, handling issues such as knowledge transfer and multiple task instances requires notions of structure and hierarchy, and that raises several questions that form the topic of this thesis – (a) can an agent acquire such hierarchies in policies in an online, incremental manner, (b) can we devise mathematically rigorous ways to abstract policies based on qualitative attributes, (c) when it is inconvenient to employ prolonged trial and error learning, can we devise alternate algorithmic methods for decision making in a lifelong setting? The first contribution of this thesis is an algorithmic method for incrementally acquiring hierarchical policies. Working with the framework of options - temporally extended actions - in reinforcement learning, we present a method for discovering persistent subtasks that define useful options for a particular domain. Our algorithm builds on a probabilistic mixture model in state space to define a generalised and persistent form of ‘bottlenecks’, and suggests suitable policy fragments to make options. In order to continuously update this hierarchy, we devise an incremental process which runs in the background and takes care of proposing and forgetting options. We evaluate this framework in simulated worlds, including the RoboCup 2D simulation league domain. The second contribution of this thesis is in defining abstractions in terms of equivalence classes of trajectories. Utilising recently developed techniques from computational topology, in particular the concept of persistent homology, we show that a library of feasible trajectories could be retracted to representative paths that may be sufficient for reasoning about plans at the abstract level. We present a complete framework, starting from a novel construction of a simplicial complex that describes higher-order connectivity properties of a spatial domain, to methods for computing the homology of this complex at varying resolutions. The resulting abstractions are motion primitives that may be used as topological options, contributing a novel criterion for option discovery. This is validated by experiments in simulated 2D robot navigation, and in manipulation using a physical robot platform. Finally, we develop techniques for solving a family of related, but different, problem instances through policy reuse of a finite policy library acquired over the agent’s lifetime. This represents an alternative approach when traditional methods such as hierarchical reinforcement learning are not computationally feasible. We abstract the policy space using a non-parametric model of performance of policies in multiple task instances, so that decision making is posed as a Bayesian choice regarding what to reuse. This is one approach to transfer learning that is motivated by the needs of practical long-lived systems. We show the merits of such Bayesian policy reuse in simulated real-time interactive systems, including online personalisation and surveillance.
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Su, Pei-Hao. "Reinforcement learning and reward estimation for dialogue policy optimisation." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275649.
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Modelling dialogue management as a reinforcement learning task enables a system to learn to act optimally by maximising a reward function. This reward function is designed to induce the system behaviour required for goal-oriented applications, which usually means fulfilling the user’s goal as efficiently as possible. However, in real-world spoken dialogue systems, the reward is hard to measure, because the goal of the conversation is often known only to the user. Certainly, the system can ask the user if the goal has been satisfied, but this can be intrusive. Furthermore, in practice, the reliability of the user’s response has been found to be highly variable. In addition, due to the sparsity of the reward signal and the large search space, reinforcement learning-based dialogue policy optimisation is often slow. This thesis presents several approaches to address these problems. To better evaluate a dialogue for policy optimisation, two methods are proposed. First, a recurrent neural network-based predictor pre-trained from off-line data is proposed to estimate task success during subsequent on-line dialogue policy learning to avoid noisy user ratings and problems related to not knowing the user’s goal. Second, an on-line learning framework is described where a dialogue policy is jointly trained alongside a reward function modelled as a Gaussian process with active learning. This mitigates the noisiness of user ratings and minimises user intrusion. It is shown that both off-line and on-line methods achieve practical policy learning in real-world applications, while the latter provides a more general joint learning system directly from users. To enhance the policy learning speed, the use of reward shaping is explored and shown to be effective and complementary to the core policy learning algorithm. Furthermore, as deep reinforcement learning methods have the potential to scale to very large tasks, this thesis also investigates the application to dialogue systems. Two sample-efficient algorithms, trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER), are introduced. In addition, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning to handle the cold start problem. Combining these two methods, a practical approach is demonstrated to effectively learn deep reinforcement learning-based dialogue policies in a task-oriented information seeking domain. Overall, this thesis provides solutions which allow truly on-line and continuous policy learning in spoken dialogue systems.
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Khmelnitskaya, Marina. "Social learning and policy-making in Russia : the case of housing policy since 1991." Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.550798.
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This thesis aims to explain the development of post-Soviet Russian housing policy by analysing the process of policy-making in this sphere at the federal level. The policy process is examined through the lens of the social learning framework. This framework views policy- making as a learning dynamic that unfolds in time, and holds the interaction of institutions, actors and ideas central to the understanding of policy development. The thesis constructs a model of social learning (MSL) that helps to trace a pattern of reiterating cycles of policy transformation in the post-Soviet Russian housing sphere. The analysis begins in the late-Soviet period. It finds that the first fundamental change in Russian housing occurred in the final Soviet years. In order to facilitate the analysis of this complex and multi-faceted policy area I have divided it into three interrelated sub-cases or issue areas - (1) housing property rights, (2) housing and utility services, and (3) housing finance. This analysis reveals divergent patterns of policy transformation in these three sub- cases. In the first and the third sub-cases two fundamental policy shifts were observed over the post-Soviet period, whereas only one such shift was present in the sub-case of housing utilities. This is attributed to the stability of policy ideas present in the latter sub-case and absent in the former two sub-cases. Overall, the thesis argues that the process of policy- making in Russia struggles to produce fundamental change in areas where a plurality of policy ideas is present. This is due to the effect of the Russian political institutional environment - in which the impact of informal connections among influential policy actors and the lack of social representation in the policy process are prominent. As a result, policy-making within the Russian state suffers.
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Schofield, Jill. "Policy into practice : a study of public policy implementation and the role of learning." Thesis, Aston University, 1998. http://publications.aston.ac.uk/10718/.
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Public policy becomes managerial practice through a process of implementation. There is an established literature within Implementation Studies which explains the variables and some of the processes involved in implementation, but less attention has been focused upon how public service managers convert new policy initiatives into practice. The research proposes that managers and their organisations have to go through a process of learning in order to achieve the implementation of public policy. Data was collected over a five year period from four case studies of capital investment appraisal in the British National Health Service. Further data was collected from taped interviews by key actors within the case studies. The findings suggest that managers do learn to implement policy and four factors are important in this learning process. These are; (i) the nature of bureaucratic responsibility; (ii) the motivation of actors towards learning; (iii) the passage of time which allows for the development of competence and (iv) the use of project team structures. The research has demonstrated that the conversion of policy into practice occurs through the operationalisation of solutions to policy problems via job tasks. As such it suggests that in understanding how policy is implemented, technical learning is more important than cultural learning, in this context. In conclusion, a "Model of Learned Implementation" is presented, together with a discussion of some of the implications of the research. These are the possible use of more pilot projects for new policy initiatives and the more systematic diffusion of knowledge about implementation solutions.
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Latief, Shahnaz. "Time and school learning." Master's thesis, University of Cape Town, 2002. http://hdl.handle.net/11427/7948.
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Bibliography: leaves 67-71.This study, conducted at Poor Man's Friend Secondary School (fictitious name), describes the use of Time Tabled School time. In fact, it quantifies the Time spent on Instruction and relates it to Learner Engagement-rates. Cumulatively, these variables impact on Learner Outcomes.
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White, James T. "Foundations of School Stakeholder's Perceptions Related to School Building Conditions and Learning." ScholarWorks, 2011. https://scholarworks.waldenu.edu/dissertations/1018.
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As school buildings continue to age, school stakeholders are increasingly concerned about the influence school facilities have on the academic achievement of students, especially in urban low income school districts. The purpose of this qualitative multiple case study was to examine school building conditions through the perceptions of 12 stakeholders including teachers, facility managers, administrators, and school board members in 3 school districts. The research question involved understanding how local school stakeholders perceived or acknowledged the relevance and relationship of school building conditions to student learning, social constructivism and aspects of organizational theory severed as the conceptual frameworks for this study. A recursive coding method and a comparative content analysis of semistructured interviews was completed. Themes that emerged included thermal comfort, technology, and symbolism. Analysis of interview responses revealed stakeholders perceived that thermal comfort and the presence of stationary technology within classrooms are of primary importance to student learning. Also, the analysis highlighted a common perception supporting the premise that the condition of school facilities represents a symbolic measure of the importance placed on student achievement by the school community. Implications for positive social change include a data-driven dialogue involving policies and practices that support providing optimum school buildings and facilities to support low-income and minority student achievement.
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Jin, Siwon. "Learning, institutions and Korea's FDI policy compared with Japan." Online version, 2000. http://bibpurl.oclc.org/web/25892.
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O'Hagan, L. "Learning mentors : a study of identity, policy and practice." Thesis, University of the West of England, Bristol, 2013. http://eprints.uwe.ac.uk/23969/.
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This study has investigated the role of learning mentoring within one secondary school with a focus on developing an understanding of practice, professional identity and policy enactment at an institutional level. Learning mentors were first introduced in schools through the Excellence in Cities initiative in 1999, guided by the principle of helping pupils overcome barriers to learning. However, from the published literature little is known about the role of learning mentors in schools, especially in the period following the cessation of the Excellence in Cities programme in 2006. This practitioner based research was therefore devised to enhance current knowledge of learning mentors activity.
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Chin, Si-wŏn. "Learning, institutions and Korea's FDI policy compared with Japan." Thesis, University of Warwick, 2000. http://wrap.warwick.ac.uk/4221/.
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The basic assertion of this thesis is that policy makers' belief systems and economic institutions have to change their structures and contents as the nation's economic developmental stage is upgraded. Put differently, a state's economic performance or achievement of economic objectives will be facilitated if there is no cleavage or conflict among economic policy, economic belief systems, and economic institutions. This means that the utility of the developmental state is valid until a nation's economy is in a take-off position. Persistent developmentalism after this stage will result in developmentalism losing its validity and becoming a main obstacle for further economic development. At this time, more liberalised economic policies which are not only supported by changed belief systems and institutions but also compatible with the neo-liberalising international political economy are needed. In other words, this thesis does not seek to answer the question 'which is the better strategy for economic development between developmentalism and neo-liberalism?' but emphasises the importance of the proper timing of transition from developmentalism to a liberalised and deregulated economy which is compatible with a mature civil society and the neo-liberalising international political economy.
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Wong, Man Chiu. "Essays on learning dynamics, monetary policy and macroeconomic outcomes /." view abstract or download file of text, 2002. http://wwwlib.umi.com/cr/uoregon/fullcit?p3055723.
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Thesis (Ph. D.)--University of Oregon, 2002.Typescript. Includes vita and abstract. Includes bibliographical references (leaves 161-169). Also available for download via the World Wide Web; free to University of Oregon users.
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Mokoena, Mamolahluwa Amelia. "The effects of developmental appraisal policy on teacher learning." Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-03302005-103907.
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Haglund, Dan. "Policy evolution and organisational learning in Zambia's mining sector." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535378.
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This thesis explores how policy evolves in weak commodity dependent African states, in a context of increasing organisational diversity due to fast-growing foreign investment from emerging markets. Through a case study of the Zambian mining sector, where state-firm relations have been highly contentious following privatisation in the late 1990s, I develop an interdisciplinary and empirically grounded account of how policy evolves. My findings are based on over 100 interviews conducted with employees of Chinese, Indian and 'western' case-study firms, as well as government officials, NGOs and other stakeholders. My aim is to give adequate attention to both state and firm perspectives on regulatory policy making. To this end, the thesis first reviews the literature on policy making in the African state as well as theories of institutional change. It then draws on evolutionary economics and organisational theory, to develop a framework for understanding firm behaviour that emphasises the role of organisational routines and objectives. I develop and define the concept of organisational learning as the process through which organisational routines and objectives change over time. I find that policy development in Zambia, in its formal expression, appears framed around a 'partnership approach' to regulation. This approach is based on a view of government as enabler of private investment, emphasising state-firm consultations and consensus in policy development. This approach contrasts but co-exists with an alternative expression of state power through the Presidency, which maintains strong informal links with the foreign mining companies. I find significant yet nuanced variation in organisational routines and aims across the companies under study. This includes the tendency among western companies to seek operational stability by engaging with local stakeholders directly, whilst Chinese investors prefer to operate through close relationships with the Presidency, relying on the Zambian state to 'broker' its wider social relations. Over time there is some evidence of organisational learning taking place among the mining companies, including through convergence in regulatory standards and practices. Yet differences persist because routines and aims - conditioned by a firm's institutional background - change slowly over time, even in the presence of inefficiencies. Zambia's mining sector was during the period under study subject to various pressures for policy reform, stemming from discontent with limited tax contributions and pervasive negative environmental and safety impacts. These reform proposals, however, failed to move ahead despite support from some of the mining companies. I show how Zambia's accommodating and pro-investor regulatory framework sits uneasily with an interventionist political culture and a diverse mining sector. The consensus among the regulated companies - on which effective partnership regulation relies - proves elusive in a 'presidential' state that readily accommodates different articulations of state-firm relations.
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Jimenez, Antonio R. "Policy search approaches to reinforcement learning for quadruped locomotion." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36798.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 63-65).
Legged locomotion is a challenging problem for machine learning to solve. A quadruped has 12 degrees of freedom which results in a large state space for the resulting Markov Decision Problem (DP). It is too difficult for computers to completely learn the state space, while it is too difficult for humans to fully understand the system dynamics and directly program the most efficient controller. This thesis combines these two approaches by integrating a model-based controller approach with reinforcement learning to develop an effective walk for a quadruped robot. We then evaluate different policy search approaches to reinforcement learning. To solve the Partially Observable Markov Decision Problem (POMIDP), a deterministic simulation is developed that generates a model which allows us to conduct a direct. policy search using dynamic programming. This is compared against using a, nondeterministic simulation to generate a model that evaluates policies. We show that using deterministic transitions to allow the use of dynamic programming has little impact on the performance of our system. Two local policy search approaches are implemented.
(cont.) A hill climbing algorithm is compared to a policy gradient algorithm to optimize parameters for the robot's model-based controller. The optimal machine-learned policy achieved a 155'% increase in performance over the hand-tuned policy. The baseline hill climbing algorithm is shown to outperform the policy gradient. algorithm with this particular gait.
by Antonio R. Jimenez.
M.Eng.
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Benfer, Richard H. (Richard Hunt). "Learning during ramping : policy choices for semiconductor manufacturing firms." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12358.
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Wang, Jiexin. "Policy Hyperparameter Exploration for Behavioral Learning of Smartphone Robots." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225744.
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HUI, Sze Sze Stephanie. "Lifelong learning for older persons in Hong Kong." Digital Commons @ Lingnan University, 2005. https://commons.ln.edu.hk/soc_etd/17.
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Lifelong learning (LL) has been widely regarded as one of the activities that can enhance well-being of the society and benefit older persons in terms of psychological, physical, mental, and cognitive well-being. In foreign countries like the Unites States of America, the United Kingdom, Finland, France, Australia, and also China, LL among older persons had been developed successfully. Hong Kong, in contrast, has no systematic planning for the development of LL even though the aged population is increasing rapidly. This research aims at constructing a LL model for older persons in Hong Kong. The theoretical framework of study focused on figuring out the breaches between satisfactory models proposed by older persons and the existing lifelong learning model in Hong Kong.
Interview survey and documentary study have been employed in this study. Interview survey was carried out from March to April of 2005 in order to understand older persons’ expectations towards LL. A questionnaire consisting of 39 big items was constructed. The author successfully interviewed 54 out of the 60 older persons originally intended, including 31 female and 23 male. The samples of this study were older persons aged 55 or above, and the median age of the respondents was 67.84. Also, 26 respondents had had learning experience in the six months prior to the survey being carried out. In the documentary study, both formal and nonformal learning programmes in Hong Kong were studied. Seven major older persons’ education providers were included, including one tertiary institution, one radio broadcasting company and five active NGOs.
The findings show the gaps between older persons’ expectations and present provision of LL programmes. The Hong Kong Lifelong Learning Model should have tertiary institutions actively involved in the provision of both formal and nonformal learning programmes. Face to face interviews should be adopted. Formal learning programmes should be made available on the internet or radio. Moreover, older persons would be more satisfied if they could learn at tertiary institutions or centres most convenient for them. Thus, various organizations should have stronger cooperation with each other so that resources can be shared. Older persons preferred a greater variety of courses and lessons and would like to receive grants or travel subsidy. Most of the elder learners were willing to be instructors, therefore they could be recruited as voluntary teachers, and more courses to train older learners as instructors should be offered. The duration of formal learning programmes may be too long for the elderly learners, and they asked for a credit-accumulating system to be implemented in those programmes. A central data bank and newsletters should be made available so that older persons could gain access to information more easily. In order to improve the quality of courses, evaluation and needs assessments should be carried out regularly by service providers, and they should consider designing the course curriculum with elder learners. In order to help those who did not receive much education when young, foundation literacy courses could be offered. In addition, instead of written course work, tests, and examinations, oral presentations and examinations could be carried out. Finally, certificates, qualifications, and public and large scale graduation ceremonies are found to be good reinforcements for older persons’ learning behaviour.
In conclusion, Hong Kong, as a beginner in the systematic development of LL for older persons, has much to do to improve the existing system. Irrefutably, making it perfect is arduous because both the service providers and the government have their own constraints. Nonetheless, trying the best to fill in the gaps between the ideal and the reality will bring the greatest benefits to older persons and the society.
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Lassahn, D. Eric. "A Necessary Evil?| Barriers to Transformative Learning Outcomes for Resistant Participants in Required Experiential Learning Activities." Thesis, Prescott College, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3705855.
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Required experiential learning within the context of higher education is on the rise. This dissertation endeavors to expand current understandings of resistance to required experiential learning including root causes, implications, and opportunities to address and alleviate resistance. The debate regarding the merits of required service, service-learning, study abroad, and other experiential learning opportunities is examined. In addition, access to such opportunities, causes and effects of resistance that develops for some participants, and ways of addressing this phenomenon are identified. To this end, an exploration of existing literature related to required experiential learning and reluctant participation is offered. In addition to a case study of Susquehanna University’s Global Opportunities program, data for this study was gathered through research methods including focus groups and semi-structured, open-ended interview. Findings reveal a variety of causes of resistance, why resistance manifests for some students prior to required study away, and strategies that practitioners in the field of experiential education employ to address such resistance.
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Shore, Raymond. "Exploring the relationship between policy generation and policy implementation : an investigation of testbed learning communities pilot." Thesis, University of Hull, 2008. http://hydra.hull.ac.uk/resources/hull:5797.
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This thesis explores the relationship between policy development and policy implementation with particular reference to the Testbed Learning Communities Pilot as outlined in the Skills Strategy (2003). The policy process, it is argued, is an important aspect of government and, as such, successful implementation is a desired outcome. The key proposition at the heart of this research is that: the relationship between policy generation and policy implementation is critical to the achievement of the intended policy outcomes. This thesis begins by discussing the concepts of Lifelong Learning and Learning Communities within the context of globalisation and it is argued that globalisation exerts an influence on public policy. This is particularly apparent in the fields of learning and skills, which, through the Skills Strategy (2003), are connected directly to economic success. Case studies of two Testbed Learning Communities contextualise the policy implementation process and from the subsequent analysis four common themes emerge. These are evaluation, project duration, axiological issues and the extent to which the project took account of existing practice. The analysis and discussion of themes from the case studies supports the proposition above and also suggests that the policy process undermined the achievement of the intended policy outcomes. This thesis proposes that the values deployed in support of policy should be a fundamental part of the policy process, providing the framework within which policy can be defined and deployed. The implications of the findings from this research lead to a conclusion that a change is required in the way that policy is developed and implemented and that this change must begin within the political culture of the government itself.
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Guo, Jia, and 果佳. "Policy learning and policy implementation in China: a case study of the Grain for Green project." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45154624.
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Kwak, Hyon Kun. "Lessons for the design of interconnection pricing policy in the telecommunications industry : a policy learning approach /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1278527071.
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Price, Monica Hatfield. "Narrative Policy Analysis of Prior Learning Assessment: Implications for Democratic Participation in Higher Education Policy Making." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1454610356.
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Carroll, Ed. "Lifelong learning in the arts : policy and practice in Ireland." Thesis, City University London, 2002. http://openaccess.city.ac.uk/7583/.
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This study will examine the conditions impinging on lifelong learning systems to foster participation in the political, social economic and cultural life of society. The research will monitor the manner in which policy and systems of lifelong learning become more cognisant of and responsive to the needs and entitlements of the human person. The aim of the study is to investigate the distinctive role played by the arts in effecting a cultural shift in the provision of lifelong learning. It will seek to bring a distinctive contribution to our understanding of the part played by the arts in giving people an authentic share in society. 'The arts', reflected in events, processes and manifestations occurring in non-formal settings, are espoused as a significant place where some individuals and communities discover a place to participate meaningfully in society. It is not possible to adequately present this study in isolation from the policy and systems of lifelong learning. These policies and systems have come under increasing pressure to create the conditions for greater linkages between the aims and objectives of education, training and employment measures. The rationale for a convergence of education, training and employment aims will be explored, and the adequacy of an approach centred on integration and collaboration will be assessed. The analysis employed in the study was undertaken between 1994-2000. It was based on a process of theory testing utilising four methods of investigation and examination: (1) a review of national and international literature, (2) a survey, (3) a questionnaire and (4)specific focus group exercises. At the outset, new base line data was collated on nonformal learning provisions, i.e. Irish Post Leaving Certificate courses and community arts learning programmes. Post Leaving Certificate courses are state-led and take place within the education system. The community arts learning programmes under investigation are arts sector-led and broadly located within the state training system. A standardised classification system was developed that enabled the documentation of four learning programmes. The study will contend that changes and adaptations to the structures and systems of accreditation and certification are necessary to accommodate non-formal learning opportunities. Finally, an analysis will be undertaken of existing structures and systems, with particular focus on education and training practices within the community and youth arts sector. The distinctive role that the arts can play in effecting change in the culture of lifelong learning will be affirmed.
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Baldwin, Mark. "Constructing care management : policy implementation as a participative learning process." Thesis, University of Bath, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285339.
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This thesis is a record of research exploring the limitations to successful policy implementation. Using Community Care as the illustrative example, it asks what these limitations might be, casting a particular light on the part played by care managers, the front-line policy implementers responsible for "needs assessments" which is a key activity in the implementation of Community Care. There is a tension in care management between the influence of procedures and the degree of discretion necessary for needs assessment to be completed effectively. In what ways, then, are policy intentions affected by the activities of care managers? Community Care is an illustration of a public policy imposed by central government through a top-down process of implementation in what is argued as a rationalist endeavour to simplify the complexities of community care and reduce it to questions of technique and structure. This attempt to present a unified conceptualisation of community care is backed by managerial procedures referred to in the public management and policy literature as "managerialism". Social work practice theory provides a third example of the rationalist attempt to simplify processes involving complex social interactions. The limitations to rationalist explanations of community care implementation and the necessity for a different kind of analysis are explored. There is a parallel with the research methodologies employed for this research. The initial interviews were helpful in revealing the degree to which policy implementation was being thwarted by care managers, but this resistance was mirrored in their rejection of my interpretation of their practice. The common thread running through the normative approach to policy implementation, management, social work practice and research methodology is an adherence to positivist forms of knowledge. The implementation of Community Care raises questions of epistemology and ontology that undermine these powerful forms of knowledge. The claim is that a different epistemology suggests practices more likely to lead to effective outcomes. An organisational orientation to effectiveness is revealed in the degree to which outcome has become wedded to techniques of scientific rationalism. A service orientation would define outcome by the degree to which the needs of vulnerable adults were met through reflection upon key relationships. The first of these is an exercise in objectivity which is not well equipped to take account of the subjective experiences of practitioners exploring needs in relationship with vulnerable adults. The service orientation suggests an experiential and participative epistemology in which people engage in the process of learning and understanding most successfully when it is collaborative rather than imposed. The second phase of fieldwork was an experiment using a method built upon a participatory epistemology and gives the reader a glimpse of what might be possible in direct contrast to rationalist approaches. Work with two co-operative inquiry groups has led me to new understandings about the nature of learning for individuals and organisations. The thesis concludes that an effective learning environment facilitating positive and reflective use of discretion can be created through co-operative inquiry, although any approach would need to include other important participants, notably managers and service users, if it is to maximise its effectiveness in the long term.
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Zadrozny, Bianca. "Policy mining : learning decision policies from fixed sets of data /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3099556.
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Stigson, Peter. "The industry role in policymaking : Policy learning in climate politics." Doctoral thesis, Västerås : School of Sustainable Development of Society and Technology, Mälardalen University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-7324.
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