Étude de cas sur la dynamique motivationnelle d’élèves à risque du 3e cycle du primaire et de l’influence du jeu éducatif numérique en mathématiques sur celle-ci

Le phénomène du décrochage scolaire est encore très présent dans notre société, particulièrement chez les garçons. Notre mémoire s’intéresse à la question et vise à mieux comprendre la dynamique motivationnelle d’un échantillon (N=11) d’élèves masculins considéré comme étant « à risque » de décrochage au 3e cycle d’une école primaire de Montréal. De plus, notre expérimentation vise spécifiquement à décrire l’influence de l’utilisation d’une activité pédagogique dite « motivante » : le jeu éducatif numérique « Math en Jeu » sur la dynamique motivationnelle à apprendre en mathématiques. Il s’agit d’une étude de cas avec une approche mixte de collecte de données. Nos résultats révèlent quatre profils de dynamique motivationnelle chez les élèves de notre échantillon : 1) les élèves en difficulté en mathématiques, 2) les élèves démotivés et 3) les élèves démotivés et en difficulté en mathématiques, puis, 4) des cas plus complexes. Notre analyse montre que « Math en Jeu » suscite un grand intérêt chez tous les élèves de notre échantillon. L’influence du jeu sur la dynamique motivationnelle semble toutefois mieux convenir aux élèves avec des dynamiques motivationnelles de type « démotivé » ou « démotivé et en difficulté en mathématiques » et dans une certaine mesure, certains élèves catégorisés comme étant des « cas complexes ». Nos résultats indiquent que le jeu pourrait notamment avoir une certaine influence sur le sentiment de compétence à réussir de l’élève. Toutefois, pour être en mesure de mieux décrire et analyser ces influences, il serait préférable de mener des recherches sur une plus longue durée, dans un contexte naturel de classe et sur un échantillon d’élèves plus grand.

Remédiation cognitive portant sur les aspects fluide et cristallisé de l’intelligence auprès d'adolescents présentant un fonctionnement intellectuel limite : approche par le jeu.

L’objectif principal de cette thèse était de créer, d’implanter et d’évaluer l’efficacité d’un programme de remédiation cognitive, intervenant de façon comparable sur les aspects fluide (Gf) et cristallisé (Gc) de l’intelligence, au sein d’une population d’intérêt clinique, les adolescents présentant un fonctionnement intellectuel limite (FIL). Compte tenu de la forte prévalence de ce trouble, le programme de remédiation GAME (Gains et Apprentissages Multiples pour Enfant) s’est développé autour de jeux disponibles dans le commerce afin de faciliter l’accès et l’implantation de ce programme dans divers milieux. Le premier article de cette thèse, réalisé sous forme de revue systématique de la littérature, avait pour objectif de faire le point sur les études publiées utilisant le jeu comme outil de remédiation cognitive dans la population pédiatrique. L’efficacité, ainsi que la qualité du paradigme utilisé ont été évaluées, et des recommandations sur les aspects méthodologiques à respecter lors de ce type d’étude ont été proposées. Cet article a permis une meilleure compréhension des écueils à éviter et des points forts méthodologiques à intégrer lors de la création du programme de remédiation GAME. Certaines mises en garde méthodologiques relevées dans cet article ont permis d’améliorer la qualité du programme de remédiation cognitive développé dans ce projet de thèse. Compte tenu du peu d’études présentes dans la littérature scientifique concernant la population présentant un FIL (70

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

The Ghost in the Machine: Inferring Machine-Based Strategies from Observed Behavior

We introduce a procedure to infer the repeated-game strategies that generate actions in experimental choice data. We apply the technique to set of experiments where human subjects play a repeated Prisoner's Dilemma. The technique suggests that two types of strategies underly the data.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

The credibility of simulation-based environments : user judgments of verisimilitude

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.