<p>Doktorska disertacija razmatra kori&scaron;ćenje računarskog adaptivnog testa u evaluaciji znanja programskih jezika C++ i Java. Za potrebe istraživanja realizovana je aplikacija koja omogućava adaptivno testiranje i njena funkcionalnost proverena je u praksi. Izvr&scaron;eno je anketiranje studenata radi evaluacije, dobijeni podaci su statistički obraĎeni i protumačeni. Na osnovu rezultata istraživanja utvrĎeno je da postoji statistički značajna razlika u korist studenata eksperimentalne grupe, naspram studenata koji su test radili na konvencionalan način (papir i olovka).</p> / <p>The doctoral dissertation discusses the application of the computer adaptive test in knowledge evaluation of the programming languages C++ and Java. For the purpose of this research an application was realized which enables adaptive testing. Its functionality was checked in practice. A survey was carried out among students for the purpose of evaluation, the attained data was statistically analyzed and interpreted. Based on the results of the research it was determined that there is a statistically significant difference in favor of the students in the experimental group as opposed to the students who had taken the test in the conventional way (paper-pencil tests).</p>

The purpose of this study was to examine the predictive validity of several variables to determine if the Florida Computerized Placement Test - Reading (CPT-R) score alone, or other variables, could determine whether or not a student would successfully pass the highest level college preparatory reading course. The study examined fall sessions 1997-2004 (n=276,079) reading scores for all forms of the CPT to determine at what standard deviation below the cutoff score of 83 a student could still successfully complete the highest level college preparatory reading course. According to the College Board, the 83 scaled score, which exempts a student from taking the reading course, equates to approximately a 70% on the paper/pencil version of the test, yet the study revealed that a scaled score of 64 was the average score for fall sessions 1997-2004, which according to previous studies equates to 9/10th reading grade level on the Nelson-Denny Reading Test (Napoli & Raymond, 1998). In addition, the most frequently obtained scaled score was 75 for fall sessions 1997-2004, which equates to an 11th grade reading level on the Nelson-Denny Reading Test; however, the results of this study showed only 61% (49, 281 out of 79,167) of the upper quartile of students (scaled scores >74) passed the highest level college preparatory reading course. Although a statistically significant relationship was found between the entry test and successful completion of the highest level college preparatory reading course, the relationship was small, and therefore does not provide very good predictive validity. Interestingly, the study revealed that students who were exempt from the reading course, and still enrolled in the course, did not have higher passing rates in the course. In addition, students with higher placement scores did not have significantly higher passing rates in the reading course than students with lower placement scores. In fact, students with the lowest scaled scores of 11-20 had the highest percentage of successfully completing the highest level college preparatory reading course.The placement test scores in reading indicate a large number of students entering Florida's community colleges are not prepared for college-level courses. In addition, the results of this study indicated that the placement test did very little to discriminate between levels of students' actual reading abilities and predict which students will ultimately pass required remedial/developmental reading classes. Although many first-time-in-college students are not recent high school graduates, high schools should be required to include reading as part of the core curriculum, separate and distinct from the language arts courses.Teachers, credentialed in reading, should be teaching reading courses in all four years of high school. Diagnostic testing and year-end testing should occur each year to chart a student's progress for all four years of high school. In addition, Florida's college entrance reading placement test should be revised so that it provides a comprehensive measurement of college-level reading skills.

Ces travaux contribuent à la théorie des tests non paramétriques minimax dans le modèle de grandes matrices de covariance. Plus précisément, nous observons $n$ vecteurs indépendants, de dimension $p$, $X_1,ldots, X_n$, ayant la même loi gaussienne $mathcal {N}_p(0, Sigma)$, où $Sigma$ est la matrice de covariance inconnue. Nous testons l'hypothèse nulle $H_0:Sigma = I$, où $I$ est la matrice identité. L'hypothèse alternative est constituée d'un ellipsoïde avec une boule de rayon $varphi$ autour de $I$ enlevée. Asymptotiquement, $n$ et $p$ tendent vers l'infini. La théorie minimax des tests, les autres approches considérées pour le modèle de matrice de covariance, ainsi que le résumé de nos résultats font l'objet de l'introduction.Le deuxième chapitre est consacré aux matrices de covariance $Sigma$ de Toeplitz. Le lien avec le modèle de densité spectrale est discuté. Nous considérons deux types d'ellipsoïdes, décrits par des pondérations polynomiales (dits de type Sobolev) et exponentielles, respectivement.Dans les deux cas, nous trouvons les vitesses de séparation minimax. Nous établissons également des équivalents asymptotiques exacts de l'erreur minimax de deuxième espèce et de l'erreur minimax totale. La procédure de test asymptotiquement minimax exacte est basée sur une U-statistique d'ordre 2 pondérée de façon optimale.Le troisième chapitre considère une hypothèse alternative de matrices de covariance pas nécessairement de Toeplitz, appartenant à un ellipsoïde de type Sobolev de paramètre $alpha$. Nous donnons des équivalents asymptotiques exacts des erreurs minimax de 2ème espèce et totale. Nous proposons une procédure de test adaptative, c-à-d libre de $alpha$, quand $alpha$ appartient à un compact de $(1/2, + infty)$.L'implémentation numérique des procédures introduites dans les deux premiers chapitres montrent qu'elles se comportent très bien pour de grandes valeurs de $p$, en particulier elles gagnent beaucoup sur les méthodes existantes quand $p$ est grand et $n$ petit.Le quatrième chapitre se consacre aux tests adaptatifs dans un modèle de covariance où les observations sont incomplètes. En effet, chaque coordonnée du vecteur est manquante de manière indépendante avec probabilité $1-a$, $ ain (0,1)$, où $a$ peut tendre vers 0. Nous traitons ce problème comme un problème inverse. Nous établissons ici les vitesses minimax de séparation et introduisons de nouvelles procédures adaptatives de test. Les statistiques de test définies ici ont des poids constants. Nous considérons les deux cas: matrices de Toeplitz ou pas, appartenant aux ellipsoïdes de type Sobolev / Our work contributes to the theory of non-parametric minimax tests for high dimensional covariance matrices. More precisely, we observe $n$ independent, identically distributed vectors of dimension $p$, $X_1,ldots, X_n$ having Gaussian distribution $mathcal{N}_p(0,Sigma)$, where $Sigma$ is the unknown covariance matrix. We test the null hypothesis $H_0 : Sigma =I$, where $I$ is the identity matrix. The alternative hypothesis is given by an ellipsoid from which a ball of radius $varphi$ centered in $I$ is removed. Asymptotically, $n$ and $p$ tend to infinity. The minimax test theory, other approaches considered for testing covariance matrices and a summary of our results are given in the introduction.The second chapter is devoted to the case of Toeplitz covariance matrices $Sigma$. The connection with the spectral density model is discussed. We consider two types of ellipsoids, describe by polynomial weights and exponential weights, respectively. We find the minimax separation rate in both cases. We establish the sharp asymptotic equivalents of the minimax type II error probability and the minimax total error probability. The asymptotically minimax test procedure is a U-statistic of order 2 weighted by an optimal way.The third chapter considers alternative hypothesis containing covariance matrices not necessarily Toeplitz, that belong to an ellipsoid of parameter $alpha$. We obtain the minimax separation rate and give sharp asymptotic equivalents of the minimax type II error probability and the minimax total error probability. We propose an adaptive test procedure free of $alpha$, for $alpha$ belonging to a compact of $(1/2, + infty)$.We implement the tests procedures given in the previous two chapters. The results show their good behavior for large values of $p$ and that, in particular, they gain significantly over existing methods for large $p$ and small $n$.The fourth chapter is dedicated to adaptive tests in the model of covariance matrices where the observations are incomplete. That is, each value of the observed vector is missing with probability $1-a$, $a in (0,1)$ and $a$ may tend to 0. We treat this problem as an inverse problem. We establish the minimax separation rates and introduce new adaptive test procedures. Here, the tests statistics are weighted by constant weights. We consider ellipsoids of Sobolev type, for both cases : Toeplitz and non Toeplitz matrices