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  • 1
    Book
    Book
    Boca Raton ; London ; New York :CRC Press, Taylor & Francis Group,
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    gbv_186916573X
    Format: 1 Online-Ressource (6 p.)
    ISBN: 9791221501063
    Series Statement: Proceedings e report
    Content: Educational achievement can be considered a multifaceted issue, which comprises different domains of learning. In Italy, on one hand the INVALSI tests administered to students through the schooling years aim to measure the ability of students in numeracy, literacy and English reading and listening competencies, separately. On the other hand, the high school final mark may be considered an overall performance outcome, formed by the combination of several marks in different subjects. Finally, at university academic achievement may be represented by the number of credits earned during the first year of enrolment, usually considered a good predictor of successful academic performances. The aim of the present work is to understand if the INVALSI scores, the high school final mark and the number of credits earned in the first academic year are associated. More specifically, our objective is twofold: first, we intend to verify if and how the INVALSI scores are associated with students’ high school final mark; second, we aim to check if the INVALSI scores and / or the high school final mark are predictive of students’ career in terms of credits earned in the first year. We will interpret our results concentrating our attention on eventual differences depending on type of school, university, field of study, student’s geographical area of residence. We use the MOBYSU.it database, selecting nearly 200.000 students who obtained their high school diploma in Italy in 2019 and enrolled in an Italian university in academic year 2019/2020. For the first objective, we estimate a multilevel ordered logit models, with students nested within high schools; as for the second objective, we estimate a cross-classified multilevel models, with students nested within high schools and athenaeums. We interpret our results in the light of assessing eventual divergences in students’ performances during the transition from high school to university
    Note: English
    Language: Undetermined
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  • 3
    UID:
    gbv_1832309185
    Format: 1 Online-Ressource (5 p.)
    ISBN: 9788855184618
    Series Statement: Proceedings e report
    Content: In the sport psychology, the theories of motivation, such as the McClelland's need achievement theory and the Nicholls' achievement goal theory, play an important role in the team sports in motivating and encouraging team members. The practical implementation of these theories relies on detecting the variables that significantly affect the probability of winning so as to identify the key elements for the team motivation, the role assignment, and the decision-making process. As the relevant variables change in accordance with the type of sport, in this contribution we focus on the basketball. In detail, we consider the traditional box score of the U.S. National Basket Association (NBA) regular season games played in the seasons 2016-17, 2017-18, 2018-19 and 2020-21. Each season comprises of 82 games played by each of the 30 teams, which cumulates to 4920 games. Hence, data have a multilevel structure, with multiple observations for each team. To properly address the data structure, the probability of winning is modelled through a random-intercept logit model, where teams are the upper-level units and games are the lower-level units. Among the independent variables, we take into account several possible determinants of winning, such as number of assists, number of offensive rebounds, number of defensive rebounds, number of turnovers, number of stolen balls, percentage of free throws made, number of fouls made. Moreover, we devote a special attention to the effect of two more independent variables: the number of key-players that are missing or injured and a dummy if the team plays without a day of rest between consecutive games. The study provides insights in the determinants of success of the basketball games: these results can be used by the team decision makers to assign roles that favor motivation and performance of players and of team as a whole
    Note: English
    Language: Undetermined
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  • 4
    UID:
    gbv_1832313964
    Format: 1 Online-Ressource (5 p.)
    ISBN: 9788855184618
    Series Statement: Proceedings e report
    Content: Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to detect and measure the possession of abilities underlying the aforementioned skills. This paper addresses both aspects of this topic by presenting the results of a research that we conducted between October and December 2020 on students enrolled in various degree courses at the University of Florence. The dataset has been collected by a three-stage initiative. We started from an entrance examination to assess the students' initial abilities. This test included ten questions, each regarding a specific aspect of logical reasoning. Then, students underwent a short training course to strengthen their knowledge of logic. At the end of the short course, students underwent a final examination to evaluate the effectiveness of the training. To verify the effectiveness of the training program we used students' characteristics into a Structural Equation Model comparing the distribution of abilities before and after the training
    Note: English
    Language: Undetermined
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  • 5
    UID:
    almahu_9949383385802882
    Format: 1 online resource (xvi, 287 pages) : , illustrations
    ISBN: 9781315112220 , 1315112221 , 9781351621380 , 1351621386 , 9781351621397 , 1351621394 , 9781351621373 , 1351621378
    Content: Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference. The book is specifically designed to appeal to students and researchers that intend to acquire a knowledge of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and uncertainty Illustrates expected utility theory and its extensions Describes approaches to elicit the utility function Reviews classical and Bayesian approaches to statistical inference based on decision theory Discusses the role of causal analysis in statistical decision theory
    Additional Edition: Print version: Bacci, Silvia. Introduction to statistical decision theory. Boca Raton, FL : CRC Press, [2020] ISBN 9781138083561
    Language: English
    Keywords: Electronic books. ; Electronic books.
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