There is a continual challenge within the construction industry to meet schedule, budget, and quality expectations. At the same time, there is an underlying problem where the older and more experienced workforce is retiring from industry at a faster rate than the newer workforce can replace them. As the more experienced workforce departs from the industry, they are taking with them much-needed skills and experience that fail to get transitioned to the newer and less experienced workforce. Among these skills are spatial skills. The construction industry has already caught on that this is a serious problem that they must contend with, and so, they have looked to the postsecondary institutions to help resolve it. However, the postsecondary institutions have a problem of their own, whereby they commonly default to passive teaching techniques that are not well suited to teaching spatial skills. So, therefore, there is a need to graduate construction management students with better spatial skills in order to meet the necessities of industry. Along with this, is the need for academia to reconsider teaching styles to better train spatial skills. Spatial skills, it has been found, are better retained when active and collaborative teaching engagements are arranged. Therefore, identifying and testing a practical and non-interfering classroom tool that students can easily use, would be the most favorable way to overcome academia’s tendency towards passive teaching. Spatial skills are needed in every part of the construction industry. In fact, everyday simple tasks require spatial skills and while these skills are honed over time, more refined skills, capable of interpreting abstract space, are required to assemble a complex construction project. Construction projects are getting more complex and often the design involves some measure of abstract thinking. Teaching these abstract-based spatial skills in postsecondary institutions has typically been done through drafting and plan reading courses, with some success. However, the need from industry is not being fully met with these skills and so an alternative solution is recommended. While Building Information Modeling (BIM) has become an adequate solution to aid in the understanding and planning of highly abstract designs, successfully using it requires excellent spatial skills. Consequently, it would be advantageous if those spatial skills were developed before students were introduced to BIM. Augmented reality is a collection of technologies that allows a user to view the “real” world with additional information that is intended to provide a better understanding of what is being observed. Augmented reality already has applications in many industries and is fast becoming a proven technology. With the availability of smaller and more powerful consumer mobile devices, augmented reality has the potential of becoming a more ubiquitous and practical tool. Recognizing that this technology can be practical, non-interfering, and known by the masses makes it an excellent solution for the classroom. Therefore, this research will study the use of an augmented reality tool to determine if there is an improvement of spatial skills in terms of accuracy, time to execute, and the retention of concepts over time. Furthermore, a separate analysis will be conducted to determine if the teaching tool is a benefit or disruption to the overall learning experience.

Thesis (MEd)--University of Stellenbosch, 2001. / ENGLISH ABSTRACT: Many students that embark on higher education do not have study and thinking skills that are sufficiently well developed in order for them to become autonomous, selfdirected learners. This is partly due to the fact that the historically authoritarian and rigid approaches to teaching in some schools have discouraged independent thought. Other contributory factors such as negative beliefs, attitudes and dispositions, and distorted concepts of the self and of learning, also prevent effective learning from taking place. The focus of this research is a study and thinking skills programme. This programme is part of a four week bridging course for freshmen at the University of Stellenbosch. It is based on a comprehensive rationale derived from cognitive education theory, comprising a number of well known theorists such as Piaget, Vygotsky, Feuerstein and Lozanov. This is further supplemented by instruments from authors in the field of thinking skills (de Bono, Buzan). The purpose of this research was to identify elements of the programme which might be responsible for aspects of transformative learning, as defined by Mezirow in his Transformative Learning Theory. These aspects initially became apparent from students' responses in post-programme evaluations. The responses represented an unexpected outcome, as Mezirow's theory was not represented in the programme's rationale. Eight criteria were developed from Mezirow's theory, operationalised as questions, and then utilized to assess transformative learning in the context of the programme. In a conceptual analysis, four categories of the programme (the rationale, the objectives, the course material and implementation procedures) were compared and contrasted with criteria from Transformative Learning Theory. From this analysis it was apparent that several criteria of Transformative Learning Theory were present in the programme: it facilitated learning in both instrumental and communicative domains; it provided opportunities to explore meaning structures and to investigate distorted meaning perspectives; and it instigated disorientating or conflicting experiences with regard to these. Some criteria were absent from the programme in that it failed to promote rational discourse according Mezirow's definition, it did not adequately promote reflection on premises, and it did not intentionally address the transformation of meaning perspectives. These three omissions may be traced to the lack of an "adult learning" focus in the programme's theoretical structure. Despite this, a number of parallels were identified which may explain the representation of Mezirow's criteria in the programme, and hence the students' responses. Conclusions are drawn regarding a theoretically justified view of transformative learning in the context of the Study and Thinking Skills (S&TS) programme, and practical implications for educators are explained. Finally, recommendations are made for enhancing trans formative learning within the Study and Thinking Skills (S&TS) programme, and for the design of similar programmes. Recommendations are also proposed for further research in this area which, in the contemporary South African educational context, clearly deserves more attention in adult education and related settings. / AFRIKAANSE OPSOMMING: Talle studente wat tot hoer onderwys toetree se studie en denkvaardighede is me voldoende ontwikkeld om as outonome, selfgerigte leerders sukses te kan behaal nie. Dit is deels toe te skryf aan die outoritere en rigiede benaderings tot onderwys in sommige skole, wat selfstandige denke ontmoedig. Daar is egter ook ander belemmerende faktore soos studente se negatiewe houdings en verkeerde opvattings van leer en van hulself wat verhinder dat effektiewe leer plaasvind. Die fokus van hierdie navorsing is 'n Studie- en Denkvaardigheidprogram (S&TS). Hierdie program vorm deel van 'n vier week lange oorbruggingsprogram vir eerstejaarstudente aan die Universiteit van Stellenbosch. Die program is gebaseer op 'n omvattende rasionaal vanuit die kognitiewe opvoedkunde perspektief wat die werk van 'n aantal bekende teoretici (Piaget, Vygotsky, Feuerstein en Lozanov) insluit en word aangevul met oefeninge deur outeurs in die veld van denkvaardighede (De Bono en Buzan). Die doel van die navorsmg was om elemente van die program te identifiseer wat verantwoordelik kon wees vir aanduidings van transformatiewe leer, soos gedefinieer deur Mezirow in sy Transformatiewe Leerteorie. Hierdie aanduidings spruit uit studente se response tydens evalueringsessies na afloop van die program. Transformatiewe leeruitkomste was onverwags, omdat Mezirow se teorie nie verteenwoordig was in die rasionaal waarvolgens die program ontwerp is nie. Agt kriteria wat uit Mezirow se teorie ontwikkel kon word, is geoperasionaliseer en in vraagvorm gebruik om die inhoud van die program te analiseer. Die kriteria is as verteenwoordigend van transformatiewe leer in die konteks van 'n studie en denkvaardigheidsprogram beskou. In die analise van die inhoud is vier kategoriee van die program (die rasionaal, die doelstellings, die kursusmateriaal en die implementerings-prosedures) vergelyk en gekontrasteer met die kriteria vanuit die Transformatiewe Leerteorie. Uit hierdie analise het geblyk dat die program aan sekere kriteria voldoen, naamlik dat dit leer in be ide die kommunikatiewe en intrumentele domeine fasiliteer; geleenthede skep om betekenisstrukture te verken en versteurde betekenisperspektiewe te ondersoek; en dat dit disorienterende of konflikterende ervarings veroorsaak met betrekking tot bestaande betekenisstrukture en -perspektiewe. Sommige kriteria was glad nie verteenwoordig in die program nie. Die program het nie daarin geslaag om rasionele diskoers, volgens Mezirow se definisie daarvan, te ontlok nie.; dit het nie voldoende reflektering met betrekking tot onderliggende aannames aangemoedig nie en dit het nie doelbewus die transformasie van betekenisprespektiewe bevorder nie. Hierdie drie weglatings uit die program mag verband hou met die feit dat die teoretiese onderbou van die program nie op volwassene leer fokus nie. Ten spyte hiervan is daar egter steeds 'n aantal ooreenkomste tussen die kognitiewe ontwikkelingsteoriee en Mezirow se transformatiewe leerteorie geidentifiseer wat die verteenwoordiging van Mezirow se teorie in die program en dus die studente se response moontlik kan verklaar. Gevolgtrekkings met betrekking tot 'n teoreties geregverdigde beskouing van transformatiewe leer in die konteks van die Studie- en Denkvaardigheidprogram en die praktiese implikasies hiervan vir opvoeders, word beskryf. Ten slotte word aanbevelings gemaak om transformatiewe leer in die program te bevorder en vir die ontwerp van soortgelyke progamme.

An image retrieval system needs to be able to communicate with people using a common language, if it is to serve its user's information need. I propose techniques for interactive image search with the help of visual attributes, which are high-level semantic visual properties of objects (like "shiny" or "natural"), and are understandable by both people and machines. My thesis explores attributes as a novel form of user input for search. I show how to use attributes to provide relevance feedback for image search; how to optimally choose what to seek feedback on; how to ensure that the attribute models learned by a system align with the user's perception of these attributes; how to automatically discover the shades of meaning that users employ when applying an attribute term; and how attributes can help learn object category models. I use attributes to provide a channel on which the user of an image retrieval system can communicate her information need precisely and with as little effort as possible. One-shot retrieval is generally insufficient, so interactive retrieval systems seek feedback from the user on the currently retrieved results, and adapt their relevance ranking function accordingly. In traditional interactive search, users mark some images as "relevant" and others as "irrelevant", but this form of feedback is limited. I propose a novel mode of feedback where a user directly describes how high-level properties of retrieved images should be adjusted in order to more closely match her envisioned target images, using relative attribute feedback statements. For example, when conducting a query on a shopping website, the user might state: "I want shoes like these, but more formal." I demonstrate that relative attribute feedback is more powerful than traditional binary feedback. The images believed to be most relevant need not be most informative for reducing the system's uncertainty, so it might be beneficial to seek feedback on something other than the top-ranked images. I propose to guide the user through a coarse-to-fine search using a relative attribute image representation. At each iteration of feedback, the user provides a visual comparison between the attribute in her envisioned target and a "pivot" exemplar, where a pivot separates all database images into two balanced sets. The system actively determines along which of multiple such attributes the user's comparison should next be requested, based on the expected information gain that would result. The proposed attribute search trees allow us to limit the scan for candidate images on which to seek feedback to just one image per attribute, so it is efficient both for the system and the user. No matter what potentially powerful form of feedback the system offers the user, search efficiency will suffer if there is noise on the communication channel between the user and the system. Therefore, I also study ways to capture the user's true perception of the attribute vocabulary used in the search. In existing work, the underlying assumption is that an image has a single "true" label for each attribute that objective viewers could agree upon. However, multiple objective viewers frequently have slightly different internal models of a visual property. I pose user-specific attribute learning as an adaptation problem in which the system leverages any commonalities in perception to learn a generic prediction function. Then, it uses a small number of user-labeled examples to adapt that model into a user-specific prediction function. To further lighten the labeling load, I introduce two ways to extrapolate beyond the labels explicitly provided by a given user. While users differ in how they use the attribute vocabulary, there exist some commonalities and groupings of users around their attribute interpretations. Automatically discovering and exploiting these groupings can help the system learn more robust personalized models. I propose an approach to discover the latent factors behind how users label images with the presence or absence of a given attribute, from a sparse label matrix. I then show how to cluster users in this latent space to expose the underlying "shades of meaning" of the attribute, and subsequently learn personalized models for these user groups. Discovering the shades of meaning also serves to disambiguate attribute terms and expand a core attribute vocabulary with finer-grained attributes. Finally, I show how attributes can help learn object categories faster. I develop an active learning framework where the computer vision learning system actively solicits annotations from a pool of both object category labels and the objects' shared attributes, depending on which will most reduce total uncertainty for multi-class object predictions in the joint object-attribute model. Knowledge of an attribute's presence in an image can immediately influence many object models, since attributes are by definition shared across subsets of the object categories. The resulting object category models can be used when the user initiates a search via keywords such as "Show me images of cats" and then (optionally) refines that search with the attribute-based interactions I propose. My thesis exploits properties of visual attributes that allow search to be both effective and efficient, in terms of both user time and computation time. Further, I show how the search experience for each individual user can be improved, by modeling how she uses attributes to communicate with the retrieval system. I focus on the modes in which an image retrieval system communicates with its users by integrating the computer vision perspective and the information retrieval perspective to image search, so the techniques I propose are a promising step in closing the semantic gap. / text

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.

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.

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.

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