As the field of computer science continues to grow, the number of students enrolled in related programs will grow as well. Though one-on-one tutoring is one of the more effective means of teaching, computer science instructors will have less and less time to devote to individual students. To address this growing concern, many tools that automate parts of an instructor’s job have been proposed. These tools can assist instructors in presenting concepts and grading student work, and they can help students learn to program more effectively. A growing group of intelligent tutoring systems attempts to tie all of this functionality into a single tool that is meant to be used throughout an entire CS course or series of courses. To contribute to this emerging area, the Factoring Problem Generator (FPG) is presented in this work. The FPG creates and grades problems in C in which students search for and extract blocks of repeated code into individual functions, learning to utilize parameters and return values as they do so. The problems created by the FPG are highly configurable by instructors such that the difficulty can be finely tuned to suit students’ individual needs. Instructors can choose whether or not to include arrays, pointers, certain elemental data types, certain operators, or certain kinds of statements, among other things. The FPG is additionally capable of generating a set of test cases for each generated problem. These test cases fully exercise students’ solutions by covering all branches of execution, and they ensure that program functionality does not change as students factor code into functions. Initial experimentation with the system has suggested that the FPG can be integrated into a beginning CS curriculum and with further refinement could become a standard tool in the CS classroom.

Le module de l'apprenant est l'une des composantes les plus importantes d’un Système Tutoriel Intelligent (STI). L'extension du modèle de l'apprenant n'a pas cessé de progresser. Malgré la définition d’un profil cognitif et l’intégration d’un profil émotionnel, le module de l’apprenant demeure non exhaustif. Plusieurs senseurs physiologiques sont utilisés pour raffiner la reconnaissance des états cognitif et émotionnel de l’apprenant mais l’emploi simultané de tous ces senseurs l’encombre. De plus, ils ne sont pas toujours adaptés aux apprenants dont les capacités sont réduites. Par ailleurs, la plupart des stratégies pédagogiques exécutées par le module du tuteur ne sont pas conçues à la base d’une collecte dynamique de données en temps réel, cela diminue donc de leur efficacité. L’objectif de notre recherche est d’explorer l’activité électrique cérébrale et de l’utiliser comme un nouveau canal de communication entre le STI et l’apprenant. Pour ce faire nous proposons de concevoir, d’implémenter et d’évaluer le système multi agents NORA. Grâce aux agents de NORA, il est possible d’interpréter et d’influencer l’activité électrique cérébrale de l’apprenant pour un meilleur apprentissage. Ainsi, NORA enrichit le module apprenant d’un profile cérébral et le module tuteur de quelques nouvelles stratégies neuropédagogiques efficaces. L’intégration de NORA à un STI donne naissance à une nouvelle génération de systèmes tutoriels : les STI Cérébro-sensibles (ou STICS) destinés à aider un plus grand nombre d’apprenants à interagir avec l’ordinateur pour apprendre à gérer leurs émotions, maintenir la concentration et maximiser les conditions favorable à l’apprentissage. / The learner module is the most important component within an Intelligent Tutoring System (ITS). The extension of the learner module is still in progress, despite the integration of the cognitive profile and the emotional profile, it is not yet exhaustive. To improve the prediction of the learner’s emotional and cognitive states, many physiological sensors have been used, but all of these sensors are cumbersome. In addition, they are not always adapted to the learners with reduced capacities. Beside, most of the pedagogical strategies that are executed by the tutor module are based on no-live collections of data. This fact reduces their efficiency. The objective of our research is to explore the electrical brain activity and use it as a communication channel between a learner and an ITS. To reach this aim, we suggest to conceive, to implement and to evaluate the multi-agent system NORA. Integrated to an ITS, this one became a Brain Sensitive Intelligent Tutoring System (BS-ITS). Agents of NORA interpret the learner’s brain electrical signal and react to it. The new BS-ITS is the extension of an ITS and enrich the learner module with the brain profile and the tutor module with a new Neuropedagogical Strategies. We aim to reach more categories of learners and help them to manage their stress, anxiety and maintain the concentration, the attention and the interest.

A Gamificação é uma técnica que a utiliza elementos de design de jogos em ambientes que não são jogos, visando aumentar a motivação e engajamento dos usuários e que vem ganhando espaço em diversos áreas como saúde, marketing e também na educação. Porém, ainda que o interesse pela técnica venha crescendo, os meios para sua aplicação nesses ainda não estão bem definidos e os resultados obtidos têm-se mostrado dependentes do contexto e da população alvo. Diversos autores atribuem essa inconstância nos resultados a problemas no design da gamificação, uma vez que a maior parte dos projetos tem utilizado abordagens one-size-fits-all, no qual todos os usuários utilizam o mesmo ambiente independente de suas preferências individuais. Diante desse cenário, tem-se proposto que a gamificação personalizada pode atender uma maior parcela dos usuários, adequando os ambientes gamificados ao perfil dos usuários. Uma das abordagens para a personalização da gamificação consiste no uso de tipologias de jogadores para determinar os elementos mais interessante para o usuário. No entanto, as tipologias utilizam estereótipos, criando constructos que ainda restringem as informações consideradas durante a personalização. Dessa forma, neste trabalho buscou-se investigar a personalização com base na teoria de motivações para se engajarem em jogos, que trata o perfil do usuário como um conjunto de diferentes subcomponentes motivacionais correlacionados, que se agrupam em macro-componentes. Para isso, adaptou-se a teoria para o contexto da gamificação e elaborou-se dois modelos o de Macro-Gamificação, o qual relaciona-se com a teoria de Autodeterminação e às necessidades de Competência, Relacionamento e Autonomia do usuário, e o de Micro-Gamificação, que relaciona os elementos de jogos a um determinado subcomponente motivacional e disponibilizá-lo mediante o interesse do usuário no subcomponente. Para avaliar então se a gamificação personalizada influencia no engajamento dos usuários quando comparada a gamificação não personalizada, os modelos foram implementados em um ambiente virtual de aprendizagem, preparado para criar os perfis de gamificação dos usuários dinamicamente e adaptar interface do em tempo real. Realizou-se então um estudo de caso com N=36, utilizando como domínio o estudo dos silabários do idioma japonês. Ao final do estudo foram identificados dois padrões de atuação no sistema com uma diferença de 65% de participação e que foi utilizado para segmentar os participantes. No segmento menos engajado, os participantes do grupo não personalizado apresentaram um engajamento aos grupos personalizados. Já no segmento dos usuários mais ativos o grupo utilizando o modelo Micro-Gamificado, apresentou-se mais engajado. Desse modo, não é possível afirmar que a gamificação personalizada proporcione um maior engajamento do que a gamificação sem personalização, embora os resultados sugiram que usuários que permanecem utilizando o sistema por mais tempo tem um maior engajamento no ambiente personalizado. Por fim, é possível afirmar que o desenvolvimento de sistemas gamificados com personalização ainda está em sua infância e por isso nesta pesquisa além de buscar evidencias sobre o impacto da gamificação personalizada no engajamento dos usuários, buscou-se também desenvolver ferramental para facilitar o processo para os membros da comunidade em ordem de impulsionar os avanços dessa área de pesquisa. / Gamification is a technique that uses game design elements in non-game context, to increase users motivation and engagement and that has been gaining space in several areas such as health, marketing and also in education. However, although the interest in the technique is growing, the means for its application are still not well defined and the results obtained have been shown to be dependent on the context and the population. Several authors attribute this resultsin the results to problems in gamification design, since most projects have been using an one-size-fitsall approach, in which all users uses the same environment independent of their preferences. Given this scenario, it has been proposed that the personalized gamification can adress a larger portion of users, adapting the gamified environments to users profiles One of the approaches to personalize the gamification is to use player typologies to determine which elements are most interesting to the user. However, typologies uses stereotypes, creating constructs that still restrict the information considered during customization. Thus, in this work, we sought to investigate personalization based on the theory of motivations to engage in games, which treats the user profile as a set of different correlated motivational subcomponents, which are grouped into macrocomponents. For this, the theory was adapted to the context of the gamification and two models were elaborated the Macro-Gamification, which is related to the theory of Self-determination and to the needs of Competence, Relationship and Autonomy of the user, and the Micro-Gamification, which relates the game elements to a particular motivational subcomponent and make it available through the users interest in the subcomponent. In order to evaluate whether personalized gamification influences user engagement when compared to non-personalized gamification, the models were implemented in a virtual learning environment, prepared to dynamically create users gamification profiles and adapt the interface in real time. A case study was then carried out with N = 36, using as a domain the study of syllabaries of the Japanese language. At the end of the study, two patterns of performance in the system with a difference of 65 % participation were identified and used to segment the participants. In the less engaged segment, the non-personalized group participants showed a higer engagement than the personalized groups. However, in the segment of the most active users the group using the Micro-Gamified model, presented itself more engaged. Thus, it can not be argued that personalized gamification provides greater engagement than non-personalized gamification, although the results suggest that users who remain using the system longer have a greater engagement in the personalized approach. Finally, it is possible to affirm that the development of personalized gamified systems is still in its infancy and for this reason, in this research, besides searching for evidence on the impact of personalized gamification on user engagement, we also sought to develop tooling to facilitate the process for the members of the community in order to boost the advances of this area of research.

The goal of Intelligent Tutoring systems (ITSs) is to engage the students in sustained reasoning activity and to interact with students based on a deep understanding of student behavior. In order to understand student behavior, ITSs rely on student modeling methods to observes student actions in the tutor and creates a quantitative representation of student knowledge, interests, affective states. Good student models are going to effectively help ITSs customize instructions, engage student's interest and then promote learning. Thus, the work of building ITSs and advancing student modeling should be considered as two interconnected components of one system rather than two separate topics. In this work, we utilized the theoretical support of a well-known learning science theory, the spacing effect, to guide the development of an ITS, called Automatic Reassessment and Relearning System (ARRS). ARRS not only validated the effectiveness of spacing effect, but it also served as a testing field which allowed us to find out new approaches to improve student learning by conducting large-scale randomized controlled trials (RCTs). The rich data set we gathered from ARRS has advanced our understanding of robust learning and helped us build student models with advanced data mining methods. At the end, we designed a set of API that supports the development of ARRS in next generation ASSISTments platform and adopted deep learning algorithms to further improve retention performance prediction. We believe our work is a successful example of combining theory and practice to advance science and address real- world problems.

Submitted by Maicon Juliano Schmidt (maicons) on 2015-06-09T17:46:58Z No. of bitstreams: 1 Henrique Manfron Seffrin_.pdf: 4996070 bytes, checksum: facf64690edf2c78dfd329c9ec67d18c (MD5) / Made available in DSpace on 2015-06-09T17:46:58Z (GMT). No. of bitstreams: 1 Henrique Manfron Seffrin_.pdf: 4996070 bytes, checksum: facf64690edf2c78dfd329c9ec67d18c (MD5) Previous issue date: 2015-02-20 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / Pesquisas têm mostrado que os alunos apresentam ganhos mais significativos de aprendizagem através do ensino individualizado, pois o professor pode se focar nas dificuldades de cada um. Por ser uma estratégia de custo elevado, os Sistemas Tutores Inteligentes (STI) oferecem uma alternativa mais viável. Esses sistemas, através de técnicas de Inteligência Artificial, são capazes de se adaptar às características de cada aluno, provendo assistência individualizada. Esta adaptação personalizada é fornecida pelo componente Modelo de Aluno, que é capaz de avaliar e mapear o conhecimento de cada estudante. Na literatura, são encontrados diversos trabalhos que lidam com a questão de avaliação de conhecimento do aluno, dentre os quais encontram-se alguns trabalhos relacionados ao domínio de álgebra. Estes trabalhos, geralmente, apresentam modelagens com redes Bayesianas, que são estruturas probabilísticas amplamente utilizadas por apresentarem resultados muito interessantes no que se refere à avaliação do conhecimento dos estudantes. No entanto, nestes trabalhos, estas estruturas relacionam apenas os conceitos algébricos, ou modelam relações entre operações algébricas, com suas principais propriedades e falsas concepções. Esses trabalhos não buscam definir as relações entre os conceitos algébricos e as respectivas operações, e como os primeiros podem estar interferindo, positivo ou negativamente, na aprendizagem dos segundos. Por exemplo, na álgebra, há conceitos chave, como incógnita e a igualdade entre os lados da equação, que interferem diretamente na compreensão de certas operações algébricas. Se um estudante não os compreende, dificilmente ele será capaz de aplicar corretamente as operações relacionadas em todas as situações. Desse modo, é desejável que os modelos de inferência sejam capazes de identificar se o estudante compreende tais conceitos. Além disso, outra limitação dos trabalhos relacionados de modelos de alunos voltados para a álgebra se refere a como eles tratam as evidências. Como estes trabalhos utilizam os itens de avaliação para isto, a cada novo exercício, é necessário inserir um novo nodo na rede, e estabelecer as relações com cada conceito abordado por este item. Isso torna o projeto da rede trabalhoso e dependente de cada exercício aplicado no STI. Nesse contexto, este trabalho propõe um modelo de aluno algébrico que além de inferir o conhecimento algébrico dos estudantes de conceitos (como incógnita, igualdades, operações inversas), habilidades (operações algébricas) e falsas concepções, busca definir as relações entre conceitos e habilidades. Como foco inicial deste trabalho serão utilizadas as equações de 1o grau. Para a inferência, será empregada a estrutura de Redes Bayesianas Dinâmicas (RBD), usando como evidência a operação aplicada pelo aluno em cada passo da resolução de uma equação. Nesta estrutura de RBD, cada time slice corresponde à resolução de um passo, o que torna o modelo proposto independente dos exercícios aplicados pelo STI. Dessa forma, o modelo de inferência proposto pode ser utilizado em qualquer equação algébrica, sem a necessidade de qualquer alteração na rede, como ocorre nos outros trabalhos relacionados. Visando verificar a capacidade de inferência desta rede, foram conduzidas avaliações. A partir dos históricos dos alunos, que utilizaram o PAT2Math, foram obtidas as evidências para a rede; e a partir dos dados dos pós-testes, realizados pelos mesmos alunos, formam obtidos os percentuais a serem comparados com a inferência da rede. Como os resultados não foram satisfatórios, empregou-se a regra do limiar, instanciando toda a variável que o ultrapassasse. Avaliada sob os limiares de 96% e 98%, a rede demostrou resultados mais precisos com o limiar de 96%, no qual as diferenças entre os resultados da rede e os percentuais dos pós-testes permaneceram, em sua maioria, em até 5%. / Students learn more through personalized instruction, because the teacher can focus on each learner. Being a impracticable strategy in terms of cost, Intelligent Tutoring Systems (ITS) offers a feasible alternative. By using Artificial Intelligence techniques, these systems are able to adapt themselves to the students, providing individualized instruction. Such adaptation is provided by the Student Model, which is able to assess and map the knowledge of each student. In the literature there are several studies that deal with knowledge evaluation in ITS, some of them are related to algebra. These studies present a Bayesian Network modeling, probabilistic structures that are widely used because of their interesting results concerning the evaluation of the student knowledge. However, in this studies, the network structure only models algebraic concepts, or only model a relationship between algebraic operations and its main properties and common misconceptions. These studies do not aim to represent the relationship between concepts and algebraic operations and how the former can be interfering, in a positive or negative way, on the learning of the second one. For example, in algebra, there are key concepts, such as the unknown and equality among sides of the equation, which directly interferes with the understanding of some algebraic operations. If a student does not understand these concepts, he would hardly be able to apply correctly the related operations in every situation. Thus, it is desirable that the inference model be able to identify if the student understands such concepts. In addition, another limitation of the related work of algebraic student models refers to how they deal with the evidence. As these studies use the assessment items for evidence, for each new exercise, it is necessary to insert a new node in the network, and establish relationships with each concept addressed by this item. This makes the network design laborious and dependent on each ITS exercise. In this context, this work proposes an algebraic student model that, in addition to infer the student knowledge of algebraic concepts (as unknown, equality, inverse operation), skills (algebraic operations) and common misconceptions, defines the relationship between concepts and skill. An initial focus of this study will be the 1st degree equations. For the inference model we use the Dynamic Bayesian Networks (DBN), in which the evidences are the operations applied by the student to solve each equation step. In this structure of DBN, each time slice corresponds to a resolution step, which makes the proposed model independent of the ITS exercises. Thus, the proposed inference model can be used in every algebraic equation, without need to make changes in the network, as occurs with other works.In order to verify the inference capacity of the network, evaluations were conducted. From the resolution history of the students, that interact with PAT2Math, the evidences for the network were obtained; and from the post-test data, solved by the same students, the percentages to compare with the results of the network were obtained. As the results aren’t very satisfactory, we applied the threshold rule, every variable that exceeded this value are instantiated. The network were evaluated under the threshold of 96% and 98%. The proposed DBN has shown more accurate inference with the 96% threshold, in which the differences between the results of the network and the percentages of the post-test remained mostly with ceiling of 5%.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2015-10-27T12:41:16Z No. of bitstreams: 1 Tiago Roberto Kautzmann_.pdf: 8317737 bytes, checksum: 0d711a62eccda40c8169142262126010 (MD5) / Made available in DSpace on 2015-10-27T12:41:16Z (GMT). No. of bitstreams: 1 Tiago Roberto Kautzmann_.pdf: 8317737 bytes, checksum: 0d711a62eccda40c8169142262126010 (MD5) Previous issue date: 2015-07-28 / Nenhuma / Alunos conscientes de seus processos cognitivos apresentam melhores desempenhos e são mais estratégicos do que alunos que não possuem essa consciência. O conhecimento de uma pessoa sobre os próprios processos cognitivos é chamado de metacognição. É um construto fundamental para a aprendizagem autorregulada, em que o próprio aluno define suas metas de aprendizagem, planeja e seleciona estratégias de estudo, monitora e avalia o seu desempenho e controla sua aprendizagem. Mais especificamente, a habilidade metacognitiva fundamental para as demais habilidades é a de monitoramento do conhecimento, a capacidade de uma pessoa de identificar o que sabe e o que não sabe. Os processos metacognitivos podem ser melhorados através de treinamento. Os trabalhos relacionados treinam habilidades metacognitivas, porém, apresentam, no mínimo, alguma das seguintes lacunas: não incitar o aluno, explicitamente, a refletir sobre seu conhecimento; não explicitar a importância da habilidade de monitoramento do conhecimento; não avaliar a habilidade de monitoramento do conhecimento. Além disso, nenhum dos trabalhos adapta ao aluno a etapa da instrução que incita o monitoramento do conhecimento. O presente trabalho propõe um modelo de agente pedagógico para treinar, explicitamente, a habilidade do aluno de monitorar o seu conhecimento. O modelo adapta a quantidade e o conteúdo da instrução metacognitiva ao aluno, a fim de fazê-lo: ter uma atitude menos reativa, refletindo sobre seu conhecimento antes de resolver uma tarefa; refletir sobre o conhecimento já demonstrado; refletir sobre tarefas similares resolvidas anteriormente. O modelo pode ser integrado a Sistemas Tutores Inteligentes do tipo step-based que forneçam informações sobre o conhecimento do aluno no domínio, o histórico de resolução de tarefas e o conhecimento possível de ser aplicado em um próximo passo de tarefa. O agente foi implementado e integrado ao STI de álgebra PAT2Math para uma avaliação experimental com 63 alunos. Os resultados da avaliação apresentaram evidências indicando que a instrução do agente pode melhorar a habilidade de monitoramento do conhecimento do aluno. Os resultados também indicaram que a instrução do agente pode melhorar o desempenho do aluno no domínio. Além disso, foi encontrada uma alta correlação entre a habilidade metacognitiva e o desempenho do aluno no domínio. / Students who are aware of their metacognitive processes have better performance and are more strategic than students who do not have this awareness. Metacognition is the knowledge of a person on their own cognitive processes. It is a fundamental construct for self-regulated learning, in which the students define their learning goals, plans and select study strategies, monitor and evaluate their performance and control their learning. More specifically, a fundamental metacognitive skill is knowledge monitoring, that is, a person's ability to identify what she knows and what she does not know. The metacognitive processes can be improved through training. Some related works have sought to train learner’s metacognitive skills, however, they have at least some of the following shortcomings: do not incite the student to reflect on their knowledge explicitly; do not explain the importance of metacognitive skills; do not evaluate the knowledge monitoring skill. In addition, none of the work adapts the instruction step that incites the monitoring of knowledge to student’s knowledge and metacognitive skills. The present work proposes a pedagogical agent model to train, explicitly, the student’s ability to monitor his knowledge. The model adapts the quantity and content of metacognitive instruction to the student, so that: he would have a less reactive attitude, he would reflect on their knowledge before solving a task; he would reflect on the knowledge already demonstrated; he would reflect on similar tasks resolved earlier. The model can be integrated with step-based Intelligent Tutoring Systems that provide information about the student's knowledge in the domain, the solving task history and the possible knowledge to be applied in a next step. The agent was implemented and integrated into an algebra STI for an experimental evaluation with 63 students. Evaluation results presented evidences indicating that the instruction of the agent can improve the student's knowledge monitoring ability. The results also indicated that the agent's instruction can improve student performance in the domain. In addition, a high correlation between metacognitive level and performance in the domain was found.

Le module de l'apprenant est l'une des composantes les plus importantes d’un Système Tutoriel Intelligent (STI). L'extension du modèle de l'apprenant n'a pas cessé de progresser. Malgré la définition d’un profil cognitif et l’intégration d’un profil émotionnel, le module de l’apprenant demeure non exhaustif. Plusieurs senseurs physiologiques sont utilisés pour raffiner la reconnaissance des états cognitif et émotionnel de l’apprenant mais l’emploi simultané de tous ces senseurs l’encombre. De plus, ils ne sont pas toujours adaptés aux apprenants dont les capacités sont réduites. Par ailleurs, la plupart des stratégies pédagogiques exécutées par le module du tuteur ne sont pas conçues à la base d’une collecte dynamique de données en temps réel, cela diminue donc de leur efficacité. L’objectif de notre recherche est d’explorer l’activité électrique cérébrale et de l’utiliser comme un nouveau canal de communication entre le STI et l’apprenant. Pour ce faire nous proposons de concevoir, d’implémenter et d’évaluer le système multi agents NORA. Grâce aux agents de NORA, il est possible d’interpréter et d’influencer l’activité électrique cérébrale de l’apprenant pour un meilleur apprentissage. Ainsi, NORA enrichit le module apprenant d’un profile cérébral et le module tuteur de quelques nouvelles stratégies neuropédagogiques efficaces. L’intégration de NORA à un STI donne naissance à une nouvelle génération de systèmes tutoriels : les STI Cérébro-sensibles (ou STICS) destinés à aider un plus grand nombre d’apprenants à interagir avec l’ordinateur pour apprendre à gérer leurs émotions, maintenir la concentration et maximiser les conditions favorable à l’apprentissage. / The learner module is the most important component within an Intelligent Tutoring System (ITS). The extension of the learner module is still in progress, despite the integration of the cognitive profile and the emotional profile, it is not yet exhaustive. To improve the prediction of the learner’s emotional and cognitive states, many physiological sensors have been used, but all of these sensors are cumbersome. In addition, they are not always adapted to the learners with reduced capacities. Beside, most of the pedagogical strategies that are executed by the tutor module are based on no-live collections of data. This fact reduces their efficiency. The objective of our research is to explore the electrical brain activity and use it as a communication channel between a learner and an ITS. To reach this aim, we suggest to conceive, to implement and to evaluate the multi-agent system NORA. Integrated to an ITS, this one became a Brain Sensitive Intelligent Tutoring System (BS-ITS). Agents of NORA interpret the learner’s brain electrical signal and react to it. The new BS-ITS is the extension of an ITS and enrich the learner module with the brain profile and the tutor module with a new Neuropedagogical Strategies. We aim to reach more categories of learners and help them to manage their stress, anxiety and maintain the concentration, the attention and the interest.

The dissertation examines the problem of students’ training and testing. To solve the problem, new model, algorithms and system have been developed and adapted. The developed system enables organizing of individual and group train-ing and self-control process. Integrated intelligent search subsystem helps to de-velop the study plan for the themes of interest. The system continuously moni-tors the user and can respond to his failure. The developed built environment computer learning subsystem performs practical calculations to assess the built environment projects. The main problem solved by development of the subsystem is reduction of time needed for users to create their own systems. Knowledge database created during the system devel-opment can be a good source of knowledge for new or similar projects evalua-tions. Calculation results can be saved for further research or shared with other users of the system. The dissertation consists of Introduction, 4 Chapters, Conclusions, Refer-ences, and List of Author’s Publications. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the dissertation, research methodology, scientific novelty, the practical significance of results examined in the dissertation and defended statements. The introduction ends in presenting the author’s publications on the subject of the defended dissertation, offering the material of made presentations in conferences... [to full text] / Disertacijoje nagrinėjama studentų mokymo ir testavimo problema. Problemai spręsti buvo sukurti ir pritaikyti nauji modeliai, algoritmai ir sistema. Sukurta sistema leidžia organizuoti individualų ir grupinį mokymo ir savikontrolės procesą. Integruota intelektinė paieškos posistemė leidžia sudaryti dominančios tematikos mokymosi planą. Sistema nuolat stebi vartotoją ir gali reaguoti į jo mokymosi nesėkmes. Sukurta kompiuterinio mokymo posistemė, skirta statiniams ir jų aplinkai, atlieka praktinį statinių ir jų aplinkos projektų vertinimą. Pagrindinė išspręsta problema kuriant posistemę yra laiko sąnaudų mažinimas, kurių reikėtų kuriant atskiras sistemas. Žinių bazė, kuri buvo sudaryta kuriant sistemą gali tapti pagrindu naujiems ar panašiems projektams vertinti. Skaičiavimo rezultatai gali būti išsaugoti tolesniems tyrimams atlikti arba jais galima dalintis su kitais sistemos vartotojais. Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros sąrašas ir autoriaus publikacijų disertacijos tema sąrašas. Įvade aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos bei pranešimai konferencijose ir disertacijos struktūra. Pirmame disertacijos skyriuje atliekama literatūros apžvalga. Nagrinėjama... [toliau žr. visą tekstą]

Disertacijoje nagrinėjama studentų mokymo ir testavimo problema. Problemai spręsti buvo sukurti ir pritaikyti nauji modeliai, algoritmai ir sistema. Sukurta sistema leidžia organizuoti individualų ir grupinį mokymo ir savikontrolės procesą. Integruota intelektinė paieškos posistemė leidžia sudaryti dominančios tematikos mokymosi planą. Sistema nuolat stebi vartotoją ir gali reaguoti į jo mokymosi nesėkmes. Sukurta kompiuterinio mokymo posistemė, skirta statiniams ir jų aplinkai, atlieka praktinį statinių ir jų aplinkos projektų vertinimą. Pagrindinė išspręsta problema kuriant posistemę yra laiko sąnaudų mažinimas, kurių reikėtų kuriant atskiras sistemas. Žinių bazė, kuri buvo sudaryta kuriant sistemą gali tapti pagrindu naujiems ar panašiems projektams vertinti. Skaičiavimo rezultatai gali būti išsaugoti tolesniems tyrimams atlikti arba jais galima dalintis su kitais sistemos vartotojais. Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros sąrašas ir autoriaus publikacijų disertacijos tema sąrašas. Įvade aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos bei pranešimai konferencijose ir disertacijos struktūra. Pirmame disertacijos skyriuje atliekama literatūros apžvalga. Nagrinėjama... [toliau žr. visą tekstą] / The dissertation examines the problem of students’ training and testing. To solve the problem, new model, algorithms and system have been developed and adapted. The developed system enables organizing of individual and group train-ing and self-control process. Integrated intelligent search subsystem helps to de-velop the study plan for the themes of interest. The system continuously moni-tors the user and can respond to his failure. The developed built environment computer learning subsystem performs practical calculations to assess the built environment projects. The main problem solved by development of the subsystem is reduction of time needed for users to create their own systems. Knowledge database created during the system devel-opment can be a good source of knowledge for new or similar projects evalua-tions. Calculation results can be saved for further research or shared with other users of the system. The dissertation consists of Introduction, 4 Chapters, Conclusions, Refer-ences, and List of Author’s Publications. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the dissertation, research methodology, scientific novelty, the practical significance of results examined in the dissertation and defended statements. The introduction ends in presenting the author’s publications on the subject of the defended dissertation, offering the material of made presentations in conferences... [to full text]

Learning to program is acknowledged to be difficult; programming is a complex intellectual activity and cannot be learnt without practice. Research has shown that first year IT students presently struggle with setting up compilers, learning how to use a programming editor and understanding abstract programming concepts. Large introductory class sizes pose a great challenge for instructors in providing timely, individualised feedback and guidance for students when they do their practice. This research investigates the problems and identifies solutions. An interactive and constructive web-based programming environment is designed to help beginning students learn to program in high-level, object-oriented programming languages such as Java and C#. The environment eliminates common starting hurdles for novice programmers and gives them the opportunity to successfully produce working programs at the earliest stage of their study. The environment allows students to undertake programming exercises anytime, anywhere, by "filling in the gaps" of a partial computer program presented in a web page, and enables them to receive guidance in getting their programs to compile and run. Feedback on quality and correctness is provided through a program analysis framework. Students learn by doing, receiving feedback and reflecting - all through the web. A key novel aspect of the environment is its capability in supporting small &quotfill in the gap" programming exercises. This type of exercise places a stronger emphasis on developing students' reading and code comprehension skills than the traditional approach of writing a complete program from scratch. It allows students to concentrate on critical dimensions of the problem to be solved and reduces the complexity of writing programs.