980 resultados para learning aid
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Agency Performance Plan, Iowa College Student Aid Commission
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This report outlines the strategic plan for Iowa College Student Aid Commissions including,goals and mission.
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Foreign aid provides a windfall of resources to recipient countries and may result in the same rent seeking behavior as documented in the curse of natural resources literature. In this paper we discuss this effect and document its magnitude. Using data for 108 recipient countries in the period 1960 to 1999, we find that foreign aid has a negative impact on democracy. In particular, if the foreign aid over GDP that a country receives over a period of five years reaches the 75th percentile in the sample, then a 10-point index of democracy is reduced between 0.6 and one point, a large effect. For comparison, we also measure the effect of oil rents on political institutions. The fall in democracy if oil revenues reach the 75th percentile is smaller, (0.02). Aid is a bigger curse than oil.
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We study the statistical properties of three estimation methods for a model of learning that is often fitted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood withand without unobserved heterogeneity. After discussing identification issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties areobtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated.
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Audit report on the Iowa Federal Family Education Loan Program Division, a Division of the Iowa College Student Aid Commission, for the year ended June 30, 2008
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We incorporate the process of enforcement learning by assuming that the agency's current marginal cost is a decreasing function of its past experience of detecting and convicting. The agency accumulates data and information (on criminals, on opportunities of crime) enhancing the ability to apprehend in the future at a lower marginal cost.We focus on the impact of enforcement learning on optimal stationary compliance rules. In particular, we show that the optimal stationary fine could be less-than-maximal and the optimal stationary probability of detection could be higher-than-otherwise.
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This paper uses a model of boundedly rational learning to accountfor the observations of recurrent hyperinflations in the lastdecade. We study a standard monetary model where the fullyrational expectations assumption is replaced by a formaldefinition of quasi-rational learning. The model under learningis able to match remarkably well some crucial stylized factsobserved during the recurrent hyperinflations experienced byseveral countries in the 80's. We argue that, despite being asmall departure from rational expectations, quasi-rationallearning does not preclude falsifiability of the model and itdoes not violate reasonable rationality requirements.
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Learning has been postulated to 'drive' evolution, but its influence on adaptive evolution in heterogeneous environments has not been formally examined. We used a spatially explicit individual-based model to study the effect of learning on the expansion and adaptation of a species to a novel habitat. Fitness was mediated by a behavioural trait (resource preference), which in turn was determined by both the genotype and learning. Our findings indicate that learning substantially increases the range of parameters under which the species expands and adapts to the novel habitat, particularly if the two habitats are separated by a sharp ecotone (rather than a gradient). However, for a broad range of parameters, learning reduces the degree of genetically-based local adaptation following the expansion and facilitates maintenance of genetic variation within local populations. Thus, in heterogeneous environments learning may facilitate evolutionary range expansions and maintenance of the potential of local populations to respond to subsequent environmental changes.
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The aim of this research is to to investigate how a supportive relationship between teachers and students in the classroom can improve the learning process. By having a good relationship with students, teachers can offer to students chances to be motivated and feel engaged in the learning process. Students will be engaged actively in the learning instead of being passive learners. I wish to investigate how using communicative approach and cooperative learning strategies while teaching do affect and improve students’ learning performance. To achieve these goals qualitative data collection was used as the primary method. The results show that teachers and students value a supportive and caring relationship between them and that interaction is essential to the teacher-student relationship. This sense of caring and supporting from teachers motivates students to become a more interested learner. Students benefit and are motivated when their teachers create a safe and trustful environment. And also the methods and strategies teachers uses, makes students feel engaged and stimulated to participate in the learning process. The students have in their mind that a positive relationship with their teachers positively impacts their interest and motivation in school which contributes to the enhancement of the learning process.
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This publication is a guide for parents and guardians of teenagers learning to drive. It should be used with the Iowa Driver’s Manual to aid you in instructing your new driver about how to safely and responsibly operate a motor vehicle. Since the task of driving is affected by changing conditions, this manual does not attempt to cover all situations that may arise. Under Iowa’s graduated driver licensing system young drivers must complete 20 hours of supervised drive time with their parents or guardians during the instruction permit stage and 10 hours during the intermediate license stage. Even though your teenager is taking or has completed driver education in school, there is a great deal of benefit to be obtained from you providing this additional practice time. Learning from your experience and under your guidance, your teenager will apply the rules of the road and more fully understand the risks involved in driving. This will require time and patience on your part, but the effort will result in you knowing that your teenager will be better able to cope with the demands of safe driving. In the back of this manual you will find several pages of diagrams. Use these diagrams to illustrate different driving situations for your teenage driver. Consider taking a notepad and pencil along during your practice sessions for additional drawings. This manual also contains a chart to log your supervised drive time. As your new driver advances through the graduated system you will be required to certify to an Iowa driver’s license examiner that you completed the minimum number of hours of supervised drive time. By becoming involved in the learning driver’s educational process, you are contributing to Iowa’s overall highway safety effort and helping your teenager develop safe driving habits that will last a lifetime.
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Report on the Iowa College Student Aid Commission for the year ended June 30, 2008
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“Estudiantes motivados producen profesores motivados y viceversa” (Lesley Denham)La cita refleja el efecto recíproco que tiene el comportamiento del profesor en el compromiso de los estudiantes a lo largo del año y viceversa. Es sorprendente como, destacando las fortalezas de cada estudiante en lugar de sus debilidades, nunca comparándolos entre ellos sino con su propio rendimiento, puede despertar una motivación intrínseca en el estudiante, y una merecida satisfacción personal para el profesor.Sin embargo, no existen botones motivacionales mágicos que podamos pulsar y hacer que el alumno quiera aprender. Como profesores, tomar la iniciativa será crucial: dar a nuestros estudiantes el espacio suficiente para experimentar, realzar su autonomía, e intuir las respuestas a través de un proceso inductivo. En definitiva, hacerles protagonistas de su proceso de aprendizaje.Incluir AICLE en la clase de inglés es una metodología que nos ayudará a conseguirlo. Los estudiantes asocian AICLE con algo interesante y divertido, diferente a las sesiones teóricas. Como resultado, al utilizar la lengua, lo hacen movidos por sus sentimientos, aprendiendo de forma implícita.“Estudiants motivats produeixen professors motivats i viceversa” (Lesley Denham)La cita reflecteix l'efecte recíproc que té el comportament del professor en el compromís dels estudiants al llarg de l'any i viceversa. És sorprenent com, destacant les fortaleses de cada estudiant en lloc de les seves debilitats, mai comparant-los entre ells sinó amb el seu propi rendiment, pot despertar una motivació intrínseca a l'estudiant, i una merescuda satisfacció personal per al professor.No obstant això, no existeixen botons motivacionals màgics que puguem prémer i fer que l'alumne vulgui aprendre. Com a professors, prendre la iniciativa serà crucial: donar als nostres estudiants l'espai suficient per experimentar, realçar la seva autonomia, i intuir les respostes a través d'un procés inductiu. En definitiva, fer-los protagonistes del seu procés d'aprenentatge.Incloure AICLE en la classe d'anglès és una metodologia que ens ajudarà a aconseguir-ho. Els estudiants consideren AICLE interessant i divertit, diferent a les sessions teòriques. Com a resultat, en utilitzar la llengua, ho fan moguts pels seus sentiments, aprenent de forma implícita.
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In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems