Active learning reduces annotation costs for supervised learning by concentrating labelling efforts on the most informative data. Most active learning methods assume that the model structure is fixed in advance and focus upon improving parameters within that structure. However, this is not appropriate for natural language processing where the model structure and associated parameters are determined using labelled data. Applying traditional active learning methods to natural language processing can fail to produce expected reductions in annotation cost. We show that one of the reasons for this problem is that active learning can only select examples which are already covered by the model. In this thesis, we better tailor active learning to the need of natural language processing as follows. We formulate the Unreliable Parameter Principle: Active learning should explicitly and additionally address unreliably trained model parameters in order to optimally reduce classification error. In order to do so, we should target both missing events and infrequent events. We demonstrate the effectiveness of such an approach for a range of natural language processing tasks: prepositional phrase attachment, sequence labelling, and syntactic parsing. For prepositional phrase attachment, the explicit selection of unknown prepositions significantly improves coverage and classification performance for all examined active learning methods. For sequence labelling, we introduce a novel active learning method which explicitly targets unreliable parameters by selecting sentences with many unknown words and a large number of unobserved transition probabilities. For parsing, targeting unparseable sentences significantly improves coverage and f-measure in active learning.

Active learning is a subfield of machine learning that has been successfully used in many applications. One of the main branches of active learning is query synthe- sis, where the learning agent constructs artificial queries from scratch in order to reveal sensitive information about the underlying decision boundary. It has found applications in areas, such as adversarial reverse engineering, automated science, and computational chemistry. Nevertheless, the existing literature on membership query synthesis has, generally, focused on finite concept classes or toy problems, with a limited extension to real-world applications. In this thesis, I develop two spectral algorithms for learning halfspaces via query synthesis. The first algorithm is a maximum-determinant convex optimization method while the second algorithm is a Markovian method that relies on Khachiyan’s classical update formulas for solving linear programs. The general theme of these methods is to construct an ellipsoidal approximation of the version space and to synthesize queries, afterward, via spectral decomposition. Moreover, I also describe how these algorithms can be extended to other settings as well, such as pool-based active learning. Having demonstrated that halfspaces can be learned quite efficiently via query synthesis, the second part of this thesis proposes strategies for mitigating the risk of reverse engineering in adversarial environments. One approach that can be used to render query synthesis algorithms ineffective is to implement a randomized response. In this thesis, I propose a semidefinite program (SDP) for learning a distribution of classifiers, subject to the constraint that any individual classifier picked at random from this distributions provides reliable predictions with a high probability. This algorithm is, then, justified both theoretically and empirically. A second approach is to use a non-parametric classification method, such as similarity-based classification. In this thesis, I argue that learning via the empirical kernel maps, also commonly referred to as 1-norm Support Vector Machine (SVM) or Linear Programming (LP) SVM, is the best method for handling indefinite similarities. The advantages of this method are established both theoretically and empirically.

Effective teacher development is significant for any educational system to remain competitive in the global arena (Bayar, 2014). However, science teachers’ professional development activities have often been found to be ineffective (Opfer & Pedder, 2011). Science teachers also minimally participate in such activities due to their ineffective experiences (Chval, Abell, Pareja, Musikul & Ritzka, 2007). Understanding how science teachers’ experiences are constructed is also crucial to create programs to meet their needs (Schneider & Plasman, 2011). It is essential in the construction of professional development experiences to recognize who is being served in professional development (Saka, 2013). But rigorous methods are required to understand the outcomes of professional development (Koomen, Blair, Young-Isebrand & Oberhauser, 2014). The purpose of this phenomenological case study was to study how secondary school science teachers describe their lived experiences of professional development in Punjab (Pakistan). How do these teachers understand, make sense, and use of those intended goals of professional development opportunities and change their practices through the implementation of learned knowledge of professional development? This study used purposive sampling to collect the qualitative data from fifteen secondary school science teachers of Punjab (Pakistan). The data collection was done through conducting semi-structured in-depth phenomenological interviews with these science teachers (Seidman, 2013). The data were analyzed using three-stage coding methods, and thematic analysis. Three main themes emerged from the analysis of data. The first theme of sense making is about their understanding and description of intended meaning of professional development activities. The second theme of meaningful experiences captured the participants perceived benefits from the PD activities. The third theme of contextual and cultural factors is focused on the understanding the impact of these factors in imparting of professional development experiences. The findings of the study communicate the significance of science teachers’ role in professional development activities. Science teachers’ voices, needs and active involvement must be taken into consideration in the designing and implementation of such activities.

This thesis makes fundamental computational and statistical advances in testing and estimation, making critical progress in theory and application of classical statistical methods like classification, regression and hypothesis testing, and understanding the relationships between them. Our work connects multiple fields in often counter-intuitive and surprising ways, leading to new theory, new algorithms, and new insights, and ultimately to a cross-fertilization of varied fields like optimization, statistics and machine learning. The first of three thrusts has to do with active learning, a form of sequential learning from feedback-driven queries that often has a provable statistical advantage over passive learning. We unify concepts from two seemingly different areas—active learning and stochastic firstorder optimization. We use this unified view to develop new lower bounds for stochastic optimization using tools from active learning and new algorithms for active learning using ideas from optimization. We also study the effect of feature noise, or errors-in-variables, on the ability to actively learn. The second thrust deals with the development and analysis of new convex optimization algorithms for classification and regression problems. We provide geometrical and convex analytical insights into the role of the margin in margin-based classification, and develop new greedy primal-dual algorithms for non-linear classification. We also develop a unified proof for convergence rates of randomized algorithms for the ordinary least squares and ridge regression problems in a variety of settings, with the purpose of investigating which algorithm should be utilized in different settings. Lastly, we develop fast state-of-the-art numerically stable algorithms for an important univariate regression problem called trend filtering with a wide variety of practical extensions. The last thrust involves a series of practical and theoretical advances in nonparametric hypothesis testing. We show that a smoothedWasserstein distance allows us to connect many vast families of univariate and multivariate two sample tests. We clearly demonstrate the decreasing power of the families of kernel-based and distance-based two-sample tests and independence tests with increasing dimensionality, challenging existing folklore that they work well in high dimensions. Surprisingly, we show that these tests are automatically adaptive to simple alternatives and achieve the same power as other direct tests for detecting mean differences. We discover a computation-statistics tradeoff, where computationally more expensive two-sample tests have a provable statistical advantage over cheaper tests. We also demonstrate the practical advantage of using Stein shrinkage for kernel independence testing at small sample sizes. Lastly, we develop a novel algorithmic scheme for performing sequential multivariate nonparametric hypothesis testing using the martingale law of the iterated logarithm to near-optimally control both type-1 and type-2 errors. One perspective connecting everything in this thesis involves the closely related and fundamental problems of linear regression and classification. Every contribution in this thesis, from active learning to optimization algorithms, to the role of the margin, to nonparametric testing fits in this picture. An underlying theme that repeats itself in this thesis, is the computational and/or statistical advantages of sequential schemes with feedback. This arises in our work through comparing active with passive learning, through iterative algorithms for solving linear systems instead of direct matrix inversions, and through comparing the power of sequential and batch hypothesis tests.

In many practical scenarios, prediction for high-dimensional observations can be accurately performed using only a fraction of the existing features. However, the set of relevant predictive features, known as the sparsity pattern, varies across data. For instance, features that are informative for a subset of observations might be useless for the rest. In fact, in such cases, the dataset can be seen as an aggregation of samples belonging to several low-dimensional sub-models, potentially due to different generative processes. My thesis introduces several techniques for identifying sparse predictive structures and the areas of the feature space where these structures are effective. This information allows the training of models which perform better than those obtained through traditional feature selection. We formalize Informative Projection Recovery, the problem of extracting a set of low-dimensional projections of data which jointly form an accurate solution to a given learning task. Our solution to this problem is a regression-based algorithm that identifies informative projections by optimizing over a matrix of point-wise loss estimators. It generalizes to a number of machine learning problems, offering solutions to classification, clustering and regression tasks. Experiments show that our method can discover and leverage low-dimensional structure, yielding accurate and compact models. Our method is particularly useful in applications involving multivariate numeric data in which expert assessment of the results is of the essence. Additionally, we developed an active learning framework which works with the obtained compact models in finding unlabeled data deemed to be worth expert evaluation. For this purpose, we enhance standard active selection criteria using the information encapsulated by the trained model. The advantage of our approach is that the labeling effort is expended mainly on samples which benefit models from the hypothesis class we are considering. Additionally, the domain experts benefit from the availability of informative axis aligned projections at the time of labeling. Experiments show that this results in an improved learning rate over standard selection criteria, both for synthetic data and real-world data from the clinical domain, while the comprehensible view of the data supports the labeling process and helps preempt labeling errors.

Fish behaviour analysis is a fundamental research area in marine ecology as it is helpful for detecting environmental changes by observing unusual fish patterns or new fish behaviours. The traditional way of analysing fish behaviour is by visual inspection using human observers, which is very time consuming and also limits the amount of data that can be processed. Therefore, there is a need for automatic algorithms to identify fish behaviours by using computer vision and machine learning techniques. The aim of this thesis is to help marine biologists with their work. We focus on behaviour understanding and analysis of detected and tracked fish with unusual behaviour detection approaches. Normal fish trajectories exhibit frequently observed behaviours while unusual trajectories are outliers or rare trajectories. This thesis proposes 3 approaches to detecting unusual trajectories: i) a filtering mechanism for normal fish trajectories, ii) an unusual fish trajectory classification method using clustered and labelled data and iii) an unusual fish trajectory classification approach using a clustering based hierarchical decomposition. The rule based trajectory filtering mechanism is proposed to remove normal fish trajectories which potentially helps to increase the accuracy of the unusual fish behaviour detection system. The aim is to reject normal fish trajectories as much as possible while not rejecting unusual fish trajectories. The results show that this method successfully filters out normal trajectories with a low false negative rate. This method is useful to assist building a ground truth data set from a very large fish trajectory repository, especially when the amount of normal fish trajectories greatly dominates the unusual fish trajectories. Moreover, it successfully distinguishes true fish trajectories from false fish trajectories which result from errors by the fish detection and tracking algorithms. A key contribution of this thesis is the proposed flat classifier, which uses an outlier detection method based on cluster cardinalities and a distance function to detect unusual fish trajectories. Clustered and labelled data are used to select feature sets which perform best on a training set. To describe fish trajectories 10 groups of trajectory descriptions are proposed which were not previously used for fish behaviour analysis. The proposed flat classifier improved the performance of unusual fish detection compared to the filtering approach. The performance of the flat classifier is further improved by integrating it into a hierarchical decomposition. This hierarchical decomposition method selects more specific features for different trajectory clusters which is useful considering the trajectory variety. Significantly improved results were obtained using this hierarchical decomposition in comparison to the flat classifier. This hierarchical framework is also applied to classification of more general imbalanced data sets which is a key current topic in machine learning. The experiments showed that the proposed hierarchical decomposition method is significantly better than the state of art classification methods, other outlier detection methods and unusual trajectory detection methods. Furthermore, it is successful at classifying imbalanced data sets even though the majority and minority classes contain varieties, and classes overlap which is frequently seen in real-world applications. Finally, we explored the benefits of active learning in the context of the hierarchical decomposition method, where active learning query strategies choose the most informative training data. A substantial performance gain is possible by using less labelled training data compared to learning from larger labelled data sets. Additionally, active learning with feature selection is investigated. The results show that feature selection has a positive effect on the performance of active learning. However, we show that random selection can be as effective as popular active learning query strategies in combination with active learning and feature selection, especially for imbalanced set classification.

A report on a research study presented to The Department of Social Work School of Human and Community Development Faculty of Humanities University of the Witwatersrand In partial fulfillment of the requirements for the degree Master of Arts in Social Work March, 2016 / The annual Conference of Parties (COP) held on the 7th-8th of December 2015 made it obvious; the environment is changing and urgent action is needed globally. Globally for the reason that damage done to the environment in one region, may have impacts in other regions. In regards to Africa and in specific South Africa, water as a finite resource is no more available as it was decades ago. This fact needs to be addressed with urgency, as human survival heavily depends on water – especially in Africa (UN Water, 2006). A part of the literature review will be dedicated to challenges related to water and its consequences for the African continent. The core of this study will be to highlight the importance of water for human beings and what can be done to raise awareness. Further, a quantitative study in Kliptown (a suburb area in Soweto suffering from water scarcity); by means of a survey was undertaken to understand more about children’s behavior in regards to water. The purpose of the research was to raise the knowledge of 10-13 year old learners and members of the Kliptown Youth Program (KYP) on the value of water and to assess their awareness on environmental friendliness as well as their daily water management. The intervention took place at Kliptown, with members of the KYP; a nongovernmental organization supporting in lifting children out of poverty. A pre and a post questionnaire was conducted as well as short video clips shown to KYP members, explaining water scarcity and climate change; supported by some recommendations on how to save water in their current environment. Random sampling has been applied to 24 members out of the population of 119 grade 5-7 members, ranging between 10-13 years of age. Respondents were of mixed genders. Data collection of the survey was cross-sectional and has been performed by means of pen-andpaper. The whole intervention with the filling out of the questionnaires, including the video clips and short presentation took about 90 minutes. Data has been interpreted by using descriptive statistics. The outcome provided information on the environmental friendliness of KYP members aged 10-13, their knowledge on the importance of water as well as their pro activeness in regards to the environment and water. Further the study tried to find out whether there is a difference of responses in regards to gender. The outcome of the study will be shared with the Director of KYP to be informed and probably implement recommendations of the study. The outcome of the study revealed that children do not know much about water, however, are interested in knowing and doing more to get acquainted to the topic. / MT2017

Nous apprenons très jeunes une quantité de règles nous permettant d'interagir avec d'autres personnes: des conventions sociales. Elles diffèrent des autres types d'apprentissage dans le sens où les premières personnes à les avoir utilisées n'ont fait qu'un choix arbitraire parmi plusieurs alternatives possibles: le côté de la route où conduire, la forme d'une prise électrique, ou inventer de nouveaux mots. À cause de celà, lorsqu'une nouvelle convention se crée au sein d'une population d'individus interagissant entre eux, de nombreuses alternatives peuvent apparaître et conduire à une situation complexe où plusieurs conventions équivalentes coexistent en compétition. Il peut devenir difficile de les retenir toutes, comment faisons-nous pour trouver un accord efficacement ? Nous exerçons communément un contrôle actif sur nos situations d'apprentissage, en par exemple sélectionnant des activités qui ne soient ni trop simples ni trop complexes. Il a été montré que ce type de comportement, dans des cas comme l'apprentissage sensori-moteur, aide à apprendre mieux, plus vite, et avec moins d'exemples. Est-ce que de tels mécanismes pourraient aussi influencer la négociation de conventions sociales? Le lexique est un exemple particulier de convention sociale: quels mots associer avec tel objet ou tel sens? Une classe de modèles computationels, les Language Games, montrent qu'il est possible pour une population d'individus de construire un langage commun via une série d'interactions par paires. En particulier, le modèle appelé Naming Game met l'accent sur la formation du lexique reliant mots et sens, et montre une typique explosion de la complexité avant de commencer à écarter les conventions synonymes ou homonymes et arriver à un consensus. Dans cette thèse, nous introduisons l'idée de l'apprentissage actif et du contrôle actif de la croissance de la complexité dans le Naming Game, sous la forme d'une politique de choix du sujet de conversation, applicable à chaque interaction. Différentes stratégies sont introduites, et ont des impacts différents sur à la fois le temps nécessaire pour converger vers un consensus et la quantité de mémoire nécessaire à chaque individu. Premièrement, nous limitons artificiellement la mémoire des agents pour éviter l'explosion de complexité locale. Quelques stratégies sont présentées, certaines ayant des propriétés similaires au cas standard en termes de temps de convergence. Dans un deuxième temps, nous formalisons ce que les agents doivent optimiser, en se basant sur une représentation de l'état moyen de la population. Deux stratégies inspirées de cette notion permettent de limiter les besoins en mémoire sans avoir à contraindre le système, et en prime permettent de converger plus rapidement. Nous montrons ensuite que la dynamique obtenue est proche d'un comportement théorique optimal, exprimé comme une borne inférieure au temps de convergence. Finalement, nous avons mis en place une expérience utilisateur en ligne sous forme de jeu pour collecter des données sur le comportement d'utilisateurs réels placés dans le cadre du modèle. Les résultats suggèrent qu'ils ont effectivement une politique active de choix de sujet de conversation, en comparaison avec un choix aléatoire.Les contributions de ce travail de thèse incluent aussi une classification des modèles de Naming Games existants, et un cadriciel open-source pour les simuler. / Social conventions are learned mostly at a young age, but are quite different from other domains, like for example sensorimotor skills. The first people to define conventions just picked an arbitrary alternative between several options: a side of the road to drive on, the design of an electric plug, or inventing a new word. Because of this, while setting a new convention in a population of interacting individuals, many competing options can arise, and lead to a situation of growing complexity if many parallel inventions happen. How do we deal with this issue?Humans often exhert an active control on their learning situation, by for example selecting activities that are neither too complex nor too simple. This behavior, in cases like sensorimotor learning, has been shown to help learn faster, better, and with fewer examples. Could such mechanisms also have an impact on the negotiation of social conventions ? A particular example of social convention is the lexicon: which words we associated with given meanings. Computational models of language emergence, called the Language Games, showed that it is possible for a population of agents to build a common language through only pairwise interactions. In particular, the Naming Game model focuses on the formation of the lexicon mapping words and meanings, and shows a typical burst of complexity before starting to discard options and find a final consensus. In this thesis, we introduce the idea of active learning and active control of complexity growth in the Naming Game, in the form of a topic choice policy: agents can choose the meaning they want to talk about in each interaction. Several strategies were introduced, and have a different impact on both the time needed to converge to a consensus and the amount of memory needed by individual agents. Firstly, we artificially constrain the memory of agents to avoid the local complexity burst. A few strategies are presented, some of which can have similar convergence speed as in the standard case. Secondly, we formalize what agents need to optimize, based on a representation of the average state of the population. A couple of strategies inspired by this notion help keep the memory usage low without having constraints, but also result in a faster convergence process. We then show that the obtained dynamics are close to an optimal behavior, expressed analytically as a lower bound to convergence time. Eventually, we designed an online user experiment to collect data on how humans would behave in the same model, which shows that they do have an active topic choice policy, and do not choose randomly. Contributions from this thesis also include a classification of the existing Naming Game models and an open-source framework to simulate them.

A área de aprendizado de máquina passa por uma grande expansão em seu universo de aplicações. Algoritmos de indução de modelos preditivos têm sido responsáveis pela realização de tarefas que eram inviáveis ou consideradas exclusividade do campo de ação humano até recentemente. Contudo, ainda é necessária a supervisão humana durante a construção de conjuntos de treinamento, como é o caso da tarefa de classificação. Tal construção se dá por meio da rotulação manual de cada exemplo, atribuindo a ele pelo menos uma classe. Esse processo, por ser manual, pode ter um custo elevado se for necessário muitas vezes. Uma técnica sob investigação corrente, capaz de mitigar custos de rotulação, é o aprendizado ativo. Dado um orçamento limitado, o objetivo de uma estratégia de amostragem ativa é direcionar o esforço de treinamento para os exemplos essenciais. Existem diversas abordagens efetivas de selecionar ativamente os exemplos mais importantes para consulta ao supervisor. Entretanto, não é possível, sem incorrer em custos adicionais, testá-las de antemão quanto à sua efetividade numa dada aplicação. Ainda mais crítica é a necessidade de que seja escolhido um algoritmo de aprendizado para integrar a estratégia de aprendizado ativo antes que se disponha de um conjunto de treinamento completo. Para lidar com esses desafios, esta tese apresenta como principais contribuições: uma estratégia baseada na inibição do algoritmo de aprendizado nos momentos menos propícios ao seu funcionamento; e, a experimentação da seleção de algoritmos de aprendizado, estratégias ativas de consulta ou pares estratégia-algoritmo baseada em meta-aprendizado, visando a experimentação de formas de escolha antes e durante o processo de rotulação. A estratégia de amostragem proposta é demonstrada competitiva empiricamente. Adicionalmente, experimentos iniciais com meta-aprendizado indicam a possibilidade de sua aplicação em aprendizado ativo, embora tenha sido identificado que investigações mais extensivas e aprofundadas sejam necessárias para apurar sua real efetividade prática. Importantes contribuições metodológicas são descritas neste documento, incluindo uma análise frequentemente negligenciada pela literatura da área: o risco devido à variabilidade dos algoritmos. Por fim, são propostas as curvas e faixas de ranqueamento, capazes de sumarizar, num único gráfico, experimentos de uma grande coleção de conjuntos de dados. / The machine learning area undergoes a major expansion in its universe of applications. Algorithms for the induction of predictive models have made it possible to carry out tasks that were once considered unfeasible or restricted to be solved by humans. However, human supervision is still needed to build training sets, for instance, in the classification task. Such building is usually performed by manual labeling of each instance, providing it, at least, one class. This process has a high cost due to its manual nature. A current technique under research, able to mitigate labeling costs, is called active learning. The goal of an active learning strategy is to manage the training effort to focus on the most relevant instances, within a budget. Several effective sampling approaches having been proposed. However, when one needs to choose the proper strategy for a given problem, they are impossible to test beforehand without incurring into additional costs. Even more critical is the need to choose a learning algorithm to integrate the active learning strategy before the existence of a complete training set. This thesis presents two major contributions to cope with such challenges: a strategy based on the learning algorithm inhibition when it is prone to inaccurate predictions; and, an attempt to automatically select the learning algorithms, active querying strategies or pairs strategy-algorithm, based on meta-learning. This attempt tries to verify the feasibility of such kind of decision making before and during the learning process. The proposed sampling approach is empirically shown to be competitive. Additionally, meta-learning experiments show that it can be applied to active learning, although more a extensive investigation is still needed to assess its real practical effectivity. Important methodological contributions are made in this document, including an often neglected analysis in the literature of active learning: the risk due to the algorithms variability. A major methodological contribution, called ranking curves, is presented.

Orientador: Fernando Augusto Silva Marins / Coorientador: José Arnaldo Barra Montevechi / Banca: Cecilia Toledo Hermández / Banca: Fabiano Leal / Banca: Messias Borges Silva / Banca: Maurício Cesar Delamaro / Resumo: Esta tese tem por objetivo a construção de mecanismos para mensurar o efeito da utilização de dinâmicas de ensino na engenharia de produção. Entre as principais questões envolvendo a educação na engenharia está a incerteza em relação aos efeitos da utilização de práticas de ensino interativas no processo ensino-aprendizagem. A abordagem metodológica utilizada foi a qualitativa, por meio do desenvolvimento de um estudo de caso explanatório do tipo múltiplos, no qual ocorreram aplicações de uma dinâmica de ensino em turmas diversas e tais aplicações foram investigadas por meio de questionários, observação, análise documental, pré e pós-testes. Os questionários aplicados têm como principal referência um modelo teórico-conceitual de avaliação multidimensional, proposto a partir de pesquisa bibliográfica, que avalia a percepção e motivação dos alunos diante da experiência de aprendizagem lúdica. Em complemento, os pré e pós-testes aplicados, buscam evidenciar o incremento da aprendizagem alcançado com a aplicação da dinâmica em questão. As evidências empíricas apontam para o fato de que as atividades lúdicas promoveram nos alunos uma maior motivação para a aprendizagem e que, de fato, houve incremento na aprendizagem. As contribuições originais mais relevantes para a teoria e para a prática são a proposição do modelo teórico-conceitual de avaliação multidimensional, que inclui as dimensões: Atenção, Relevância, Confiança, Satisfação, Interação, dentre outras possíveis; e, do modelo de avaliação, por meio de pré e pós-testes, para a verificação do incremento de aprendizagem. O modelo aqui utilizado permitiu a análise de dinâmicas de ensino na engenharia de produção, mas tem o potencial de ser aplicado em outros conteúdos. Além de avaliar os efeitos da ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This paper intends to build mechanisms that measure the effectiveness of playful activities in teaching Production Engineering concepts. The uncertainty about this effectiveness regarding these practices in the teaching-learning process is one of the main issues in the engineering education. The methodology used was qualitative, by developing an explanatory case study, applying the case and studying it through observation, surveys, documentary analysis and pre and posttests. The applied surveys are reference of a theoretical-conceptual model regarding multidimensional evaluation, withdrawn from a theoretical background, that assesses the students' perception and motivation in facing the playful experience. On the other hand, pre and posttests point out the learning increase that students acquired with the dynamics. The empirical evidence indicate that the playful activities provided to the students motivation to learn, increasing indeed their learning. The most relevant and original contributions to this theory and its practice are the proposition of a theoretical-conceptual model regarding multidimensional evaluation that includes many dimensions (Attention, Relevance, Confidence, Satisfaction, Interaction, among others) and the assessment model (pretests and posttests) that checks the learning increase. This model allowed the dynamic's analysis in teaching Production Engineering concepts, although it could also be applied in other situations. Besides evaluating the effectiveness of this teaching technique, the suggested model predicates diagnosis and action plans in order to improve instructional design of the playful activity, in view of future applications / Doutor