A relatively large number of 8th-grade public middle school students in the United States, particularly in urban communities, are not performing at acceptable levels in mathematics. One concept that poses significant difficulty for these students and negatively affects their overall mathematics achievement is fractions. Many researchers have attributed these difficulties primarily to traditional fraction instruction that emphasizes procedural rather than conceptual knowledge. Therefore this study was designed to investigate how students use their computational and conceptual knowledge and fraction representations to solve fraction-related performance-based mathematical tasks. Social constructivism was used as the theoretical framework in examining conceptual knowledge related to learning fractions. This qualitative study was implemented in an urban middle school in the southeast. It involved an initial sample of 37, 8th-grade, African American pre-algebra students who completed a fraction interest questionnaire and two fraction pretests. During the implementation period, 34 students in the researcher’s pre-algebra class completed three performance-based tasks, three reflection logs, and participated in an interview after completing each task. Of the 34 students who completed all tasks, three were purposefully selected as the informants for the study. In addition, observations, field notes, and artifacts (student work) were utilized to facilitate triangulation of the data. The findings of the study indicated the informants could compute fractions with an average of 85% of mastery but could conceptualize fractions only to a small extent. This validated prior findings and led to the conclusion that student deficiency with fractions results primarily from their level of conceptual knowledge. In the investigation of the ways in which 8th-grade students use fraction representations, this study found the informants used representations to develop a visual map of their mathematical thinking and reasoning and to check the accuracy of their computations. Therefore, this study suggests, when students’ mathematical learning experiences relative to fractions have not emphasized the use of representations to develop conceptual knowledge, they may not be comfortable with the accuracy of the solutions demonstrated in their fractions models.

Context: Games involve people to a large extent where they relate themselves with the game characters; this is commonly known as game immersion. Generally, some players play games for enjoyment, some for stress relaxation and so on.Game immersion is usually used to describe the degree of involvement with a game. When people play games, they don’t necessarily realize that they have been dissociated with the surrounding world. Real world dissociation (RWD) can be defined as the situation where a player is less aware of the surroundings outside the game than about what is happening in the game itself. The RWD factor has been expected to measure the losing track of time, lack of awareness of surroundings and mental transportation. Objectives: In this thesis, we measure and compare the difference in game immersion between experienced and inexperienced players using RWD factor. In addition, the study involves exploring the significance of game immersion and various approaches used to measure it. Methods: In this study literature review has been carried out to explore the meaning of game immersion and further user studies in the form of an experiment has been conducted to measure game immersion between experienced and inexperienced gamers. The game immersion has been measured using the real world dissociation (RWD) factor. After the experiment has been conducted, a statistical technique has been carried out to measure the difference in game immersion among the two groups. Results:The empirical investigation on the measurement of game immersion has been done using RWD factor. The results state that the significance value is less than 0.05 and hence null hypothesis is rejected for both the games. The measurable difference has been calculated by using Cohen’s d effect size between experienced and inexperienced players. The Cohen’s d value between experienced players and inexperienced players for Dota 2 is 0.7423 and CS:GO is 0.8383. Conclusions: After analyzing the data and calculating the effect size, the overall results state that inexperienced group of players are more immersed than the experienced group of players when measured by RWD factor. Hence it can be concluded that irrespective of the game played, inexperienced players are more dissociated from the real world than the experienced players.

The National Education Department of South Africa has mandated a policy of outcomes-based education for all learners and educators in this country. Two of the most important principles of outcomes-based education are collaborative work in groups and continuous assessment by the teacher and peers. In Computer Studies, taken on the higher grade, learners are expected to construct algorithms and programs by themselves. In the real world such algorithms and programs would be constructed by groups of people working together. The researcher’s purpose of conducting this study was to breach the gap that exists between what is done in accordance with the outcomes-based curriculum in schools - and what is expected in the real world where collaborative work is the norm. The researcher used Bloom’s high-order thinking skills as his point of departure for this study and examined the implications of how they contribute to real-world situations in the school environment. To evaluate the South African curriculum for Computer Studies on the higher grade, the researcher compared the South African curriculum was the curriculum used in Australia for learners of the same age group. The results led to an intervention in which South African learners were examined on high-order thinking skills and programming in the real world. / Dissertation (MEd (CIE))--University of Pretoria, 2005. / Curriculum Studies / unrestricted

Un grand nombre d'études ont été faites dans le domaine de l'Optimisation Stochastique en général et les Algorithmes Génétiques en particulier. L'essentiel des nouveaux développements ou des améliorations faites sont alors testés sur des jeux de tests très connus tels que BBOB, CEC, etc. conçus de telle manière que soient présents les principaux défis que les optimiseurs doivent relever : non séparabilité, multimodalité, des vallées où le gradient est quasi-nul, et ainsi de suite. La plupart des études ainsi faites se déroulent via une application directe sur le jeu de test, optimisant un nombre donné de variables pour atteindre un critère précis. La première contribution de ce travail consiste à étudier l'impact de la remise en cause de ce fonctionnement par deux moyens : le premier repose sur l'introduction d'un grand nombre de variables qui n'ont pas d'impact sur la valeur de la fonction optimisée ; le second quant à lui relève de l'étude des conséquences du mauvais conditionnement d'une fonction en grande dimension sur les performances des algorithmes d'optimisation stochastique. Une deuxième contribution se situe dans l'étude de l'impact de la modification des mutations de l'algorithme CMA-ES,où, au lieu d'utiliser des mutations purement aléatoires, nous allons utiliser des mutations quasi-aléatoires. Ce travail introduit également la ``Sieves Method'', bien connue des statisticiens. Avec cette méthode, nous commençons par optimiser un faible nombre de variables, nombre qui est ensuite graduellement incrémenté au fil de l'optimisation.Bien que les jeux de tests existants sont bien sûr très utiles, ils ne peuvent constituer que la première étape : dans la plupart des cas, les jeux de tests sont constitués d'un ensemble de fonctions purement mathématiques, des plus simples comme la sphère, aux plus complexes. Le but de la conception d'un nouvel optimiseur, ou l'amélioration d'un optimiseur existant, doit pourtant in fine être de répondre à des problèmes du monde réel. Ce peut-être par exemple la conception d'un moteur plus efficace, d'identifier les bons paramètres d'un modèle physique ou encore d'organiser des données en groupes.Les optimiseurs stochastiques sont bien évidemment utilisés sur de tels problèmes, mais dans la plupart des cas, un optimiseur est choisi arbitrairement puis appliqué au problème considéré. Nous savons comment les optimiseurs se comparent les uns par rapport aux autres sur des fonctions artificielles, mais peu de travaux portent sur leur efficacité sur des problèmes réels. L'un des principaux aspects de des travaux présentés ici consiste à étudier le comportement des optimiseurs les plus utilisés dans la littérature sur des problèmes inspirés du monde réel, voire des problèmes qui en viennent directement. Sur ces problèmes, les effets des mutations quasi-aléatoires de CMA-ES et de la``Sieves Method'' sont en outre étudiés. / A lot of research is being done on Stochastic Optimisation in general and Genetic Algorithms in particular. Most of the new developments are then tested on well know testbeds like BBOB, CEC, etc. conceived to exhibit as many pitfalls as possible such as non-separability, multi-modality, valleys with an almost null gradient and so on. Most studies done on such testbeds are pretty straightforward, optimising a given number of variables for there cognized criterion on the testbed. The first contribution made here is to study the impact of some changes in those assumptions, namely the effect of supernumerary variables that don't change anything to a function evaluation on the one hand, and the effect of a change of the studied criterion on the other hand. A second contribution is in the modification of the mutation design for the algorithm CMA-ES, where we will use Quasi-Random mutations instead of purely random ones. This will almost always result in a very clear improvement ofthe observed results. This research also introduces the Sieves Method well known in statistics, to stochastic optimisers: by first optimising a small subset of the variables and gradually increasing the number of variables during the optimization process, we observe on some problems a very clear improvement. While artificial testbeds are of course really useful, they can only be the first step: in almost every case, the testbeds are a collection of purely mathematical functions, from the simplest one like the sphere, to some really complex functions. The goal of the design of new optimisers or the improvement of an existing one is however, in fine, to answer some real world question. It can be the design of a more efficient engine, finding the correct parameters of a physical model or even to organize data in clusters. Stochastic optimisers are used on those problems, in research or industry, but in most instances, an optimiser ischosen almost arbitrarily. We know how optimisers compare on artificial functions, but almost nothing is known abouttheir performances on real world problems. One of the main aspect of the research exposed here will be to compare someof the most used optimisers in the literature on problems inspired or directly coming from the real-world. On those problems, we will additionally test the efficiency of quasi-random mutations in CMA-ES and the Sieves-Method.

<p>Socially engaged design programs, community development coalitions, and intentional and unintentional design spaces are rich with expertise and thinkers who are developing solutions to very pressing, yet complicated problems. Little research has been conducted on the expertise and sense-making of the community partners who participate in these situations. The goal of this research endeavor is to unpack the ways various community partners make meaning of their design experiences by answering the question: What evidence of system’s thinking can be seen in the way community partners describe their work or context? A qualitative research study was conducted in which three community partners were interviewed at various points during their engagement with socially engaged design programs. They demonstrated their systems thinking ability most strongly across the following domains: differentiate and qualify elements, explore multiple perspectives, consider issues appropriately, recognize systems, identify and characterize relationships. These findings imply that the community partners are not only capable of systems thinking but have the potential to be more deeply involved in <a>developing solutions</a> within these settings. Future studies should investigate systems thinking beyond socially engaged design in formal settings and should consider investigation protocols that more directly surface systems thinking domains. Overall, this study contributes to existing work in systems thinking by calling for a more expansive and inclusive engagement of community partners in socially engaged work.</p>

<p>This qualitative study was conducted to understand the instructional strategies used by high school integrated STEM (iSTEM) teachers to apply real-world problem solving in their classrooms in the state of Indiana. The problem addressed by this study was the need to understand the instructional strategies employed by iSTEM teachers in their classrooms. Using a basic qualitative approach, data was collected through teacher interviews, classroom observations, and documents. The thematic analysis revealed several themes: (a) there is no single instructional strategy, but teachers adapt their strategies to the context, (b) the importance of preparation using various sources and building on student’s prior knowledge, (c) a focus on asking "why" questions as a priority, (d) the necessity of making group work tangible, (e) the use of modeling as a common strategy, including data collection and analysis, sketching and documentation, (f) the promotion of student independence by being aware and performing tasks independently, (g) the integration of real-world issues to relate learning to student lives, and (h) the challenges posed by time and diversity of student abilities. These findings suggest that iSTEM teachers should be flexible in their approach and emphasize preparation, questioning, modeling, group work, and real-world connections to improve student learning in an integrated STEM approach. The findings contribute to the existing literature on iSTEM teaching and have implications for iSTEM teachers, school administrators, and policymakers. The findings of this study can inform professional development programs for iSTEM teachers and can help school administrators design collaborative and problem-solving learning environments. Lastly, policymakers can use the findings to develop policies that promote the integration of real-world problem-solving into STEM education, thereby contributing to the development of a workforce that is prepared to meet the challenges of the 21st century.</p>