Évolution des projets de formation de futurs enseignants du primaire au contact de situations probabilistes

Il semble y avoir des attentes réciproques non comblées en formation initiale à l’enseignement des mathématiques. Cherchant à comprendre la genèse de ces attentes, nous nous sommes intéressée à la vision que les étudiants nourrissent des phénomènes d’enseignement. Ayant postulé que les étudiants ont une vision déterministe de ces phénomènes, et considérant que leur anticipation oriente leur projet de formation, nous nous sommes attaquée au problème de la rencontre des projets des étudiants et des formateurs. Deux objectifs généraux ont été formulés : le premier concerne la description des projets de formation des étudiants tandis que le second concerne l’expérimentation d’une séquence de situations susceptible de faire évoluer leurs projets. Cette recherche a été menée auprès de 58 étudiants du baccalauréat en enseignement en adaptation scolaire et sociale d’une même université, lesquels entamaient leur formation initiale à l’enseignement des mathématiques. Afin d’explorer les projets qu’ils nourrissent a priori, tous les étudiants ont complété un questionnaire individuel sur leur vision des mathématiques et de leur enseignement et ont participé à une première discussion de groupe sur le sujet. Une séquence de situations probabilistes leur a ensuite été présentée afin d’induire une complexification de leur projet. Enfin, cette expérimentation a été suivie d’une seconde discussion de groupe et complétée par la réalisation de huit entretiens individuels. Il a été mis en évidence que la majorité des étudiants rencontrés souhaitent avant tout évoluer en tant qu’enseignant, en développant leur capacité à enseigner et à faire apprendre ou comprendre les mathématiques. Bien que certaines visées se situent dans une perspective transmissive, celles-ci ne semblent pas représentatives de l’ensemble des projets "visée". De plus, même si la plupart des étudiants rencontrés projettent de développer des connaissances relatives aux techniques et aux méthodes d’enseignement, la sensibilité à la complexité dont certains projets témoignent ne permet plus de réduire les attentes des étudiants à l’endroit de leur formation à la simple constitution d’un répertoire de techniques d’enseignement réputées efficaces. En ce qui a trait aux modes d’anticipation relevés a priori, nos résultats mettent en relief des anticipations se rattachant d’abord à un mode adaptatif, puis à un mode prévisionnel. Aucune anticipation se rattachant à un mode prospectif n’a été recensée a priori. La séquence a permis aux étudiants de s’engager dans une dialectique d’action, de formulation et de validation, elle les a incités à recourir à une approche stochastique ainsi qu’à porter un jugement de probabilité qui prenne en compte la complexité de la situation. A posteriori, nous avons observé que les projets "visée" de certains étudiants se sont complexifiés. Nous avons également noté un élargissement de la majorité des projets, lesquels considèrent désormais les autres sommets du triangle didactique. Enfin, des anticipations se rattachant à tous les modes d’anticipation ont été relevées. Des anticipations réalisées grâce à un mode prospectif permettent d’identifier des zones d’incertitude et de liberté sur lesquelles il est possible d’agir afin d’accroître la sensibilité à la complexité des situations professionnelles à l’intérieur desquelles les futurs enseignants devront se situer.

What We Don't Know Can Hurt Us: Clinical Inference Bias and Constructed Memories of Sexual Abuse.

The false memory/recovered memory debate, research regarding the malleability of memory, and the current lack of methods for validating recovered memories all support the view that heightened care is required of therapists dealing with clients whom they suspect have been sexually abused. The judgmental heuristics that underlie the major clinical inference biases of confirmatory bias, biased covariation, base rate fallacies, and schematic processing errors are all relevant to the processes leading to therapist-client constructions of memories of sexual abuse. Suggestions for minimizing each of these biases are offered. Personal motivations of the client and client suggestibility are factors that may contribute to the construction of memories of sexual abuse, and suggestions for minimizing the impact of these motivations are offered. In conclusion, general suggestions for minimizing the impact of clinical inference biases within the sexual abuse treatment context are summarized.

Patterns, heuristics for architectural design support: making use of evolutionary modelling in design

Software used by architectural and industrial designers – has moved from becoming a tool for drafting, towards use in verification, simulation, project management and project sharing remotely. In more advanced models, parameters for the designed object can be adjusted so a family of variations can be produced rapidly. With advances in computer aided design technology, numerous design options can now be generated and analyzed in real time. However the use of digital tools to support design as an activity is still at an early stage and has largely been limited in functionality with regard to the design process. To date, major CAD vendors have not developed an integrated tool that is able to both leverage specialized design knowledge from various discipline domains (known as expert knowledge systems) and support the creation of design alternatives that satisfy different forms of constraints. We propose that evolutionary computing and machine learning be linked with parametric design techniques to record and respond to a designer’s own way of working and design history. It is expected that this will lead to results that impact on future work on design support systems-(ergonomics and interface) as well as implicit constraint and problem definition for problems that are difficult to quantify.

Revisiting the GameFlow model with detailed heuristics

The GameFlow model strives to be a general model of player enjoyment, applicable to all game genres and platforms. Derived from a general set of heuristics for creating enjoyable player experiences, the GameFlow model has been widely used in evaluating many types of games, as well as non-game applications. However, we recognize that more specific, low-level, and implementable criteria are potentially more useful for designing and evaluating video games. Consequently, the research reported in this paper aims to provide detailed heuristics for designing and evaluating one specific game genre, real-time strategy games. In order to develop these heuristics, we conducted a grounded theoretical analysis on a set of professional game reviews and structured the resulting heuristics using the GameFlow model. A selection of the resulting 165 heuristics are presented in this paper and discussed with respect to key evaluations of the GameFlow model.

GameFlow heuristics for designing and evaluating real-time strategy games

The GameFlow model strives to be a general model of player enjoyment, applicable to all game genres and platforms. Derived from a general set of heuristics for creating enjoyable player experiences, the GameFlow model has been widely used in evaluating many types of games, as well as non-game applications. However, we recognize that more specific, low-level, and implementable criteria are potentially more useful for designing and evaluating video games. Consequently, the research reported in this paper aims to provide detailed heuristics for designing and evaluating one specific game genre, real-time strategy games. In order to develop these heuristics, we conducted a grounded theoretical analysis on a set of professional game reviews and structured the resulting heuristics using the GameFlow model. The resulting 165 heuristics for designing and evaluating real-time strategy games are presented and discussed in this paper.

Heuristics and building price forecasting

The construction industry contains two types of estimators the contractors' estimator and the designers' price forecaster. Each has two models of the building in which to systemize his procedures - the production model and the design model. The use of these models is discussed in the light of the industry's particular problems of complexity and uncertainty together with the pressures of the market. It is argued that estimators and forecasters, in order to function effectively in these conditions, are forced to exercise a high degree of subjective judgment. Means of eliciting good heuristics involved in judgment making are considered by reference to the artificial intelligence and construction literature and a methodology is proposed based on these findings. The results of two early trials of the method with students are given, indicating the usefulness of the approach.

Natural ventilation heuristics in high-rise residential buildings : evaluation and prediction

Complex behaviour of air flow in the buildings makes it difficult to predict. Consequently, architects use common strategies for designing buildings with adequate natural ventilation. However, each climate needs specific strategies and there are not many heuristics for subtropical climate in literature. Furthermore, most of these common strategies are based on low-rise buildings and their performance for high-rise buildings might be different due to the increase of the wind speed with increase in the height. This study uses Computational Fluid Dynamics (CFD) to evaluate these rules of thumb for natural ventilation for multi-residential buildings in subtropical climate. Four design proposals for multi-residential towers with natural ventilation which were produced in intensive two days charrette were evaluated using CFD. The results show that all the buildings reach acceptable level of wind speed in living areas and poor amount of air flow in sleeping areas.

Towards simple rules heuristics for IT business value

The business value of IT (BVIT) has been a prominent and central research topic in the IS discipline. Due to continuous and unpredictable technology and business changes, a more dynamic perspective on IT business value that includes organizational learning is required. We suggest that simple rules heuristics can address this challenge. The simple rules heuristics approach has been introduced by Eisenhardt and co-authors (Bingham & Eisenhardt, 2011; Bingham, Eisenhardt, & Furr, 2007; Eisenhardt & Sull, 2001) to better understand strategic decision making for capturing superabundant, heterogeneous, fastmoving opportunities. They argue that explicit organizational learning can translate accumulated experience into increasingly effective heuristics for strategic processes in highvelocity environments. We make three main contributions by exploring the suitability of a simple rules heuristics approach for the creation of IT business value: (1) we propose six types of simple rules heuristics for capturing IT-based opportunities in dynamic environments, including synergy heuristics as specifically relevant in an IT context, (2) we show how a simple rules heuristics approach can advance our understanding of dynamics and organizational learning for BVIT, and; (3) we introduce the strategic logic of opportunity to BVIT.

Robust heuristics for scalable optimization of complex SQL queries

Modern database systems incorporate a query optimizer to identify the most efficient "query execution plan" for executing the declarative SQL queries submitted by users. A dynamic-programming-based approach is used to exhaustively enumerate the combinatorially large search space of plan alternatives and, using a cost model, to identify the optimal choice. While dynamic programming (DP) works very well for moderately complex queries with up to around a dozen base relations, it usually fails to scale beyond this stage due to its inherent exponential space and time complexity. Therefore, DP becomes practically infeasible for complex queries with a large number of base relations, such as those found in current decision-support and enterprise management applications. To address the above problem, a variety of approaches have been proposed in the literature. Some completely jettison the DP approach and resort to alternative techniques such as randomized algorithms, whereas others have retained DP by using heuristics to prune the search space to computationally manageable levels. In the latter class, a well-known strategy is "iterative dynamic programming" (IDP) wherein DP is employed bottom-up until it hits its feasibility limit, and then iteratively restarted with a significantly reduced subset of the execution plans currently under consideration. The experimental evaluation of IDP indicated that by appropriate choice of algorithmic parameters, it was possible to almost always obtain "good" (within a factor of twice of the optimal) plans, and in the few remaining cases, mostly "acceptable" (within an order of magnitude of the optimal) plans, and rarely, a "bad" plan. While IDP is certainly an innovative and powerful approach, we have found that there are a variety of common query frameworks wherein it can fail to consistently produce good plans, let alone the optimal choice. This is especially so when star or clique components are present, increasing the complexity of th- e join graphs. Worse, this shortcoming is exacerbated when the number of relations participating in the query is scaled upwards.