Este trabalho aborda um estudo sobre os STI - Sistemas Tutores Inteligentes - dando uma visão geral do que esta sendo feito nesta área e quais são as tendências futuras que direcionam os STI a trabalhar com arquiteturas de agentes. Para simular as mudanças que ocorrem em certos estados mentais dos agentes, fizemos uma unido dos STI com a IAD - Inteligência Artificial Distribuída - e construímos os modelos dos agentes com base no ambiente dos STI e na arquitetura SEM - Sociedade dos Estados Mentais - [CORM que baseia seu formalismo na Teoria das Situações. Exploramos e adotamos a ideia da arquitetura aberta dos STI [OLI92], pois, através dela, foi possível criar um ambiente cooperativo de aprendizagem no qual o tutor e o aluno podem ensinar e aprender. Trabalhamos com dois agentes globais, a saber, o tutor e o aluno, sendo cada um deles composto por quatro agentes locais associados a determinados estados mentais do agente. Os agentes locais correspondem aos estados mentais: crença, desejo, intenção e expectativa, definidos na arquitetura SEM como agentes locais, e tratados individualmente nesta, que se preocupa com o comportamento particular de cada um. Optamos por usar a arquitetura SEM, que é uma arquitetura de agentes, no lugar de uma funcional tradicional, ou seja, composta por módulos, que é característica dos STI, porque nela podemos tratar os estados mentais como agentes locais, e assim é possível modelar o comportamento individual de cada estado e as mudanças que a interação entre os agentes provoca em cada um Abordamos três situações de ensino/aprendizagem com peculiaridades diferentes nas quais os agentes globais interagem cooperativamente com o objetivo de um ensinar o outro. Para cada dialogo, estabelecemos objetivos específicos: no primeiro, nosso interesse é na maneira como o aluno ensina uma nova estratégia ao tutor; no segundo, analisamos as mudanças das crenças do tutor sobre o conhecimento do aluno; no terceiro, nos preocupamos com as estratégias de ensino utilizadas pelo tutor. O processo de ensino/aprendizagem que acontece no desenrolar da interação entre os agentes é realizado usando o método de aprendizagem simbólica automática EBL - Explanation-Based Learning - [MIT86],[COS90] Este método proporciona a generalização do exemplo de treinamento que é incorporado as crenças e as estratégias do agente que desempenha o papel daquele que aprende, enriquecendo-as. As estratégias, que são fundamentais para os STI, são tratadas como pianos de ensino, utilizadas para promover a aprendizagem, pois definem a maneira como determinado conteúdo deve ser ensinado. Tratamos aqui as estratégias de uma maneira inovadora e diferente da tratada anteriormente [COR94]. Elas são um conjunto de ações e possuem armazenados procedimentos que são usados pelos agentes durante a interação. São determinadas e controladas conforme a intenção e usadas de acordo com as crenças, no sentido de selecionar a mais adequada para cada situação. / This study focuses on the Intelligent Tutoring System (ITS) and aims at presenting a general view concerning what has been developed in this field as well as the coming trends which lead the ITS to deal with agents' architecture. In order to simulate the changes which occur in certain mental states of the agents, we linked ITS with Distributed Artificial Intelligence (DAI) and then we built the agents' modules based on ITS environment and on SEM - Sociedade dos Estados Mentais that means Mental States Society - architecture [COR94]. Such an architecture bases its formalism on the Situation Theory. We explored and adopted the idea of the ITS open architecture [OLI92] for, through it, it has been possible to create a cooperative learning environment in which both the tutor and the student are able to teach and learn. The two global agents we worked on - tutor and student - both of them are made up of four local agents which are their mental states. The mental states involved are: belief, desire, intention, and expectation. These mental states are treated individually and defined as local agents according to SEM architecture. Instead of using a functional architecture - characteristic of ITS - we chose an agent architecture, for this latter makes it possible to treat the mental states as subagents. It is possible, therefore, to model the individual behavior of each state as well as the changes resulted from the agents' interaction. We focused on three teaching/learning situations that present different situations in which the global agents interact co-operatively in such a way that they teach each other. Specific aims were meant to each dialogue, as follows: the first dialogue concern has to do with the way the student teaches the tutor a new strategy; the second dialogue aim is to explore the tutor's "belief revision" about the student's knowledge; the third dialo gue goal has to do with the teaching strategies used by the tutor. The teaching/learning process brought about as the interaction between the agents happens is applied by using the Explanation-Based Learning (EBL) method [MIT86],[COS 90]. This method makes it possible to generalize the test example which is added to the learning agent's beliefs and strategies, making them more complete. The strategies, which are vital to the ITS, are treated as teaching plans and used to bring about learning, for they define the way in which a certain content is supposed to be taught. The strategies are treated here in a new manner, differently from the way they had formerly been [COR94]. They are a set of actions and present procedures on file that are used by the agents during the interaction. Also, the strategies are chosen and controlled by the intention and consulted by the beliefs so as to select the most suitable one, according to the situation.

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-05-02T22:30:08Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) samuel_henrique_falci.pdf: 1834360 bytes, checksum: 4a13b8b43d407ce67debbdcdfeb51b55 (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-05-04T16:19:57Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) samuel_henrique_falci.pdf: 1834360 bytes, checksum: 4a13b8b43d407ce67debbdcdfeb51b55 (MD5) / Made available in DSpace on 2018-05-04T16:19:57Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) samuel_henrique_falci.pdf: 1834360 bytes, checksum: 4a13b8b43d407ce67debbdcdfeb51b55 (MD5) Previous issue date: 2017 / Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) / Este trabalho tem por objetivo apresentar solu??es para o aperfei?oamento do ensino atrav?s das plataformas de ensino ? dist?ncia. Com o avan?o tecnol?gico, a procura por esta modalidade de ensino vem crescendo significativamente, por?m, alguns problemas podem ser observados, como o abandono do curso ou o insucesso no aprendizado do estudante. Na tentativa de minimizar problemas como este citado, algumas abordagens vem sido propostas. Dentre elas, algumas fazem uso de conceitos conhecidos como Estilos de Aprendizagem para definir as prefer?ncias de aprendizagem de cada aluno. Os Estilos de Aprendizagem defendem que cada indiv?duo possui caracter?sticas pessoais para o processo de aprendizagem e quando o m?todo de ensino n?o coincide com esta prefer?ncia, o aluno pode apresentar problemas para assimilar o conte?do. Para minimizar estes problemas, a proposta deste trabalho analisou outras abordagens j? existentes na literatura e os modificou para poss?veis melhorias. Sendo assim, este trabalho fez uso de t?cnicas de Intelig?ncia Artificial, L?gica Fuzzy e Aprendizagem por Refor?o para detectar automaticamente os Estilos de Aprendizagem de alunos simulados computacionalmente. A partir desta detec??o um curr?culo personalizado pode ser desenvolvido para cada aluno de Plataformas de Ensino ? Dist?ncia de acordo com as suas prefer?ncias de aprendizagem. As t?cnicas utilizadas nesta abordagem demonstraram melhorias significativas ao se comparar com outra abordagem espec?fica presente na literatura. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / This paper aims to show solutions for the improviment in education through the distance learning platform. Along with the technological progress, the search for this modality has been growing significantly, however, some problems could be observed, as the course abandonment or the learning unsuccess by the student. In order to try to minimize these issues, some approaches have been proposed. Among them, a few use known concepts, as the Learning Styles, to define the learning preferences of each student. The Learning Styles advocate that each individual has a personal methodology to the learning process and, when the education method does not match the student?s preference, he may present problems to assimilate the content. In order to minimize these issues, this paper analyzed others existing approaches from the literature and modified them to possible improvement. Then, this paper has used Artificial Inteligence, Fuzzy Logic and Reinforcement Learning techniques, in order to detect, automatically, the student?s Learning Styles which was computationally simulated. With this detection, a personalyzed curriculum could be developed for each student of the Distance Learning Platform according to their learning preferences. The techniques applyed in this approach, demonstrate significant improvements when comparing to another specific approach in the literature.

Este trabalho está inserido no grupo de Inteligência Artificial da UFRGS e contribui com os estudos que estão sendo realizados pela Profª. Magda Bercht, ambos sob orientação da Profa. Rosa Maria Viccari. Situa-se na área de Inteligência Artificial, Inteligência Artificial Aplicada à Educação, Sistemas Tutores Inteligentes e Agentes. O objetivo deste trabalho é propor a arquitetura de um agente capaz de identificar fatores motivacionais e afetivos pela monitoração das ações do usuário através da interface de um STI. Esta proposta inclui a descrição de um protótipo e descrição em detalhes de diversos itens necessários à integração, entre os quais estão: a arquitetura do STI utilizado, o sistema de comunicação utilizado pela sociedade de agentes, a interface e os requisitos necessários. Inicialmente, apresentam-se algumas estruturas de STI e alguns conceitos de agentes, seguidos de conceitos sobre emoção e afetividade. Em seguida apresentam-se as estratégias de identificação de três importantes fatores motivacionais e afetivos: Independência, Confiança e Esforço. Também são apresentados os requisitos mínimos necessários para aplicação das estratégias de identificação e integração do agente em um STI. / This work is inserted in the group of Artificial Intelligence of UFRGS and it contributes with the studies that are being accomplished by Magda Bercht. These studies are both under the guidance of Rosa Maria Viccari. They are focus the areas of Artificial Intelligence, Artificial Intelligence applied to the education, Intelligent Tutoring Systems, and Agents. The objective of this work is to propose an agent's architecture able to identify motivational and affective factors for the monitoring of the user's actions through the interface of an ITS. This proposal includes the description of a prototype and details of several items necessary to the integration. Among these are: the used ITS architecture, the communication system used by the agents' society, the interface, and the necessary requirements. Initially, some structures of ITS and some agents' concepts, followed by concepts about emotion and affectivity are presented. After, we present identification of identification of three important motivational and affective factors: Independence, Confidence and Effort. The necessary minimum requirements for application of the identification strategies and the agent's integration in an ITS are also presented.

Este volume descreve o trabalho multi e interdisciplinar de pesquisa realizado na área de Inteligência Artificial aplicada à Educação, tendo como objetivo principal à proposta de uma metodologia de trabalho suportada por um ambiente na Web como recurso de apoio à aprendizagem almejada pela subárea de Programação Computacional, nos cursos de graduação em Informática. O trabalho investigativo e experimental foi desenvolvido com base na teoria proposta por Ausubel e o ambiente desenvolvido possui arquitetura baseada nos Sistemas Tutores Inteligentes, onde se buscou criar um Assistente Virtual de Ensino Inteligente, cujo comportamento é modelado a partir de um conjunto de regras oriundas da Lógica Fuzzy. Esta base de regras busca estabelecer uma nova forma de análise e assistência no acompanhamento da evolução de aprendizagem do aluno, tendo por base os pressupostos da teoria ausubeliana. A organização dos conteúdos na base de domínio utiliza uma estrutura organizada na forma de Mapas Conceituais, os quais possuem relação com as funções de pertinência associadas aos objetivos destes conteúdos. O trabalho experimental foi realizado na universidade onde o autor trabalha como docente, permitindo que fossem realizadas as análises e entrevistas de forma facilitada. O protótipo, criado para validar alguns aspectos da tese, obteve sucesso e constatou um aproveitamento médio melhor na aprendizagem em Programação Computacional. / This volume describe the multi and interdisciplinary work of made research in the Artificial Intelligence area applied to Education, with the main objective to the purpose of one methodology of work that has the support by Web environment as a way of support for the expected learning by the Computational Programming sub area in the graduation courses of Informatics. The investigative and experimental work was developed based in the theory proposed by Ausubel and the developed environment has the architecture based in the Intelligent Tutoring Systems, looking for a creation of one Intelligent Teaching Assistant, which the behavior is model from the rule group with origin in Fuzzy Logic. This base of rules search to establish one new form of analysis and assistance to go along with evolution of student knowledge, basing in the presuppositions of Subsumption Theory. The organization of contents in the base of dominion use one organized structure in Conceptual Maps form, that have the relation with functions of associated relevant to the objectives of these contents. The experimental work was realized in university where the author works as a teacher, giving the permission to realize the analysis and interviews in the easy way. The prototype, created for some aspect validation of the thesis it was obtained success and it was consisted a medium development better than the learning in the Computacional Programming.

Este trabalho aborda um estudo sobre os STI - Sistemas Tutores Inteligentes - dando uma visão geral do que esta sendo feito nesta área e quais são as tendências futuras que direcionam os STI a trabalhar com arquiteturas de agentes. Para simular as mudanças que ocorrem em certos estados mentais dos agentes, fizemos uma unido dos STI com a IAD - Inteligência Artificial Distribuída - e construímos os modelos dos agentes com base no ambiente dos STI e na arquitetura SEM - Sociedade dos Estados Mentais - [CORM que baseia seu formalismo na Teoria das Situações. Exploramos e adotamos a ideia da arquitetura aberta dos STI [OLI92], pois, através dela, foi possível criar um ambiente cooperativo de aprendizagem no qual o tutor e o aluno podem ensinar e aprender. Trabalhamos com dois agentes globais, a saber, o tutor e o aluno, sendo cada um deles composto por quatro agentes locais associados a determinados estados mentais do agente. Os agentes locais correspondem aos estados mentais: crença, desejo, intenção e expectativa, definidos na arquitetura SEM como agentes locais, e tratados individualmente nesta, que se preocupa com o comportamento particular de cada um. Optamos por usar a arquitetura SEM, que é uma arquitetura de agentes, no lugar de uma funcional tradicional, ou seja, composta por módulos, que é característica dos STI, porque nela podemos tratar os estados mentais como agentes locais, e assim é possível modelar o comportamento individual de cada estado e as mudanças que a interação entre os agentes provoca em cada um Abordamos três situações de ensino/aprendizagem com peculiaridades diferentes nas quais os agentes globais interagem cooperativamente com o objetivo de um ensinar o outro. Para cada dialogo, estabelecemos objetivos específicos: no primeiro, nosso interesse é na maneira como o aluno ensina uma nova estratégia ao tutor; no segundo, analisamos as mudanças das crenças do tutor sobre o conhecimento do aluno; no terceiro, nos preocupamos com as estratégias de ensino utilizadas pelo tutor. O processo de ensino/aprendizagem que acontece no desenrolar da interação entre os agentes é realizado usando o método de aprendizagem simbólica automática EBL - Explanation-Based Learning - [MIT86],[COS90] Este método proporciona a generalização do exemplo de treinamento que é incorporado as crenças e as estratégias do agente que desempenha o papel daquele que aprende, enriquecendo-as. As estratégias, que são fundamentais para os STI, são tratadas como pianos de ensino, utilizadas para promover a aprendizagem, pois definem a maneira como determinado conteúdo deve ser ensinado. Tratamos aqui as estratégias de uma maneira inovadora e diferente da tratada anteriormente [COR94]. Elas são um conjunto de ações e possuem armazenados procedimentos que são usados pelos agentes durante a interação. São determinadas e controladas conforme a intenção e usadas de acordo com as crenças, no sentido de selecionar a mais adequada para cada situação. / This study focuses on the Intelligent Tutoring System (ITS) and aims at presenting a general view concerning what has been developed in this field as well as the coming trends which lead the ITS to deal with agents' architecture. In order to simulate the changes which occur in certain mental states of the agents, we linked ITS with Distributed Artificial Intelligence (DAI) and then we built the agents' modules based on ITS environment and on SEM - Sociedade dos Estados Mentais that means Mental States Society - architecture [COR94]. Such an architecture bases its formalism on the Situation Theory. We explored and adopted the idea of the ITS open architecture [OLI92] for, through it, it has been possible to create a cooperative learning environment in which both the tutor and the student are able to teach and learn. The two global agents we worked on - tutor and student - both of them are made up of four local agents which are their mental states. The mental states involved are: belief, desire, intention, and expectation. These mental states are treated individually and defined as local agents according to SEM architecture. Instead of using a functional architecture - characteristic of ITS - we chose an agent architecture, for this latter makes it possible to treat the mental states as subagents. It is possible, therefore, to model the individual behavior of each state as well as the changes resulted from the agents' interaction. We focused on three teaching/learning situations that present different situations in which the global agents interact co-operatively in such a way that they teach each other. Specific aims were meant to each dialogue, as follows: the first dialogue concern has to do with the way the student teaches the tutor a new strategy; the second dialogue aim is to explore the tutor's "belief revision" about the student's knowledge; the third dialo gue goal has to do with the teaching strategies used by the tutor. The teaching/learning process brought about as the interaction between the agents happens is applied by using the Explanation-Based Learning (EBL) method [MIT86],[COS 90]. This method makes it possible to generalize the test example which is added to the learning agent's beliefs and strategies, making them more complete. The strategies, which are vital to the ITS, are treated as teaching plans and used to bring about learning, for they define the way in which a certain content is supposed to be taught. The strategies are treated here in a new manner, differently from the way they had formerly been [COR94]. They are a set of actions and present procedures on file that are used by the agents during the interaction. Also, the strategies are chosen and controlled by the intention and consulted by the beliefs so as to select the most suitable one, according to the situation.

Este volume descreve o trabalho multi e interdisciplinar de pesquisa realizado na área de Inteligência Artificial aplicada à Educação, tendo como objetivo principal à proposta de uma metodologia de trabalho suportada por um ambiente na Web como recurso de apoio à aprendizagem almejada pela subárea de Programação Computacional, nos cursos de graduação em Informática. O trabalho investigativo e experimental foi desenvolvido com base na teoria proposta por Ausubel e o ambiente desenvolvido possui arquitetura baseada nos Sistemas Tutores Inteligentes, onde se buscou criar um Assistente Virtual de Ensino Inteligente, cujo comportamento é modelado a partir de um conjunto de regras oriundas da Lógica Fuzzy. Esta base de regras busca estabelecer uma nova forma de análise e assistência no acompanhamento da evolução de aprendizagem do aluno, tendo por base os pressupostos da teoria ausubeliana. A organização dos conteúdos na base de domínio utiliza uma estrutura organizada na forma de Mapas Conceituais, os quais possuem relação com as funções de pertinência associadas aos objetivos destes conteúdos. O trabalho experimental foi realizado na universidade onde o autor trabalha como docente, permitindo que fossem realizadas as análises e entrevistas de forma facilitada. O protótipo, criado para validar alguns aspectos da tese, obteve sucesso e constatou um aproveitamento médio melhor na aprendizagem em Programação Computacional. / This volume describe the multi and interdisciplinary work of made research in the Artificial Intelligence area applied to Education, with the main objective to the purpose of one methodology of work that has the support by Web environment as a way of support for the expected learning by the Computational Programming sub area in the graduation courses of Informatics. The investigative and experimental work was developed based in the theory proposed by Ausubel and the developed environment has the architecture based in the Intelligent Tutoring Systems, looking for a creation of one Intelligent Teaching Assistant, which the behavior is model from the rule group with origin in Fuzzy Logic. This base of rules search to establish one new form of analysis and assistance to go along with evolution of student knowledge, basing in the presuppositions of Subsumption Theory. The organization of contents in the base of dominion use one organized structure in Conceptual Maps form, that have the relation with functions of associated relevant to the objectives of these contents. The experimental work was realized in university where the author works as a teacher, giving the permission to realize the analysis and interviews in the easy way. The prototype, created for some aspect validation of the thesis it was obtained success and it was consisted a medium development better than the learning in the Computacional Programming.

abstract: Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on the student’s previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from Amazon Mechanical Turk, it turned out that the two types of questions performed very closely on all the three measures. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

abstract: Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics. / Dissertation/Thesis / Masters Thesis Computer Science 2015

abstract: EMBRACE (Enhanced Moved By Reading to Accelerate Comprehension in English) is an IPad application that uses the Moved By Reading strategy to help improve the reading comprehension skills of bilingual (Spanish speaking) English Language Learners (ELLs). In EMBRACE, students read the text of a story and then move images corresponding to the text that they read. According to the embodied cognition theory, this grounds reading comprehension in physical experiences and thus is more engaging. In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm. I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm. / Dissertation/Thesis / Masters Thesis Computer Science 2016

abstract: This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems. The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time. The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques. The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states. The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN. The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2017