abstract: The mathematics test is the most difficult test in the GED (General Education Development) Test battery, largely due to the presence of story problems. Raising performance levels of story problem-solving would have a significant effect on GED Test passage rates. The subject of this formative research study is Ms. Stephens’ Categorization Practice Utility (MS-CPU), an example-tracing intelligent tutoring system that serves as practice for the first step (problem categorization) in a larger comprehensive story problem-solving pedagogy that purports to raise the level of story problem-solving performance. During the analysis phase of this project, knowledge components and particular competencies that enable learning (schema building) were identified. During the development phase, a tutoring system was designed and implemented that algorithmically teaches these competencies to the student with graphical, interactive, and animated utilities. Because the tutoring system provides a much more concrete rather than conceptual, learning environment, it should foster a much greater apprehension of a story problem-solving process. With this experience, the student should begin to recognize the generalizability of concrete operations that accomplish particular story problem-solving goals and begin to build conceptual knowledge and a more conceptual approach to the task. During the formative evaluation phase, qualitative methods were used to identify obstacles in the MS-CPU user interface and disconnections in the pedagogy that impede learning story problem categorization and solution preparation. The study was conducted over two iterations where identification of obstacles and change plans (mitigations) produced a qualitative data table used to modify the first version systems (MS-CPU 1.1). Mitigation corrections produced the second version of the MS-CPU 1.2, and the next iteration of the study was conducted producing a second set of obstacle/mitigation tables. Pre-posttests were conducted in each iteration to provide corroboration for the effectiveness of the mitigations that were performed. The study resulted in the identification of a number of learning obstacles in the first version of the MS-CPU 1.1. Their mitigation produced a second version of the MS-CPU 1.2 whose identified obstacles were much less than the first version. It was determined that an additional iteration is needed before more quantitative research is conducted. / Dissertation/Thesis / Doctoral Dissertation Educational Technology 2018

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 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.

In the classroom, teachers make use of different combinations of social planes (e.g., individual, collaborative) to support learning. However, little is known about the complementary strengths of individual and collaborative learning or how to combine them so that they are more effective than either social plane alone. One roadblock to this investigation is an ability to orchestrate, or manage, more complex, but theoretically effective, combinations of collaborative and individual learning in the classroom. Prior research has created orchestration tools that support the planning and real-time management of classroom activities, which reduces the cognitive load and time needed for instructors to support the activity, allowing for more complex activities to become more manageable. Current orchestration tools do not, however, support a wide range of combinations of collaborative and individual learning activities in a flexible manner. To fully investigate the combinations of collaborative and individual learning, orchestration tools need to be developed that can support the researcher in a way that can be integrated into the classroom by accounting for teachers’ values. My thesis work addresses two related goals. First, my work addresses the questions: Do collaborative and individual learning have complementary strengths and is a combination of the two social planes better than either alone? In my work, I developed an intelligent tutoring system (ITS) to support collaborative and individual learning. Through three studies, using this ITS, with over 500 4th and 5th grade students, I demonstrate that a collaborative ITS can be used to effectively support learning with elementary school students and that a combination of collaborative and individual learning is more effective than either alone. However, my studies did not find any support for complementary strengths and many other combinations of social planes are left to investigate. Additionally, during my experiments, I encountered challenges in orchestration that, along with the need to research more complex combinations of collaborative and individual learning, informed the next steps of my research. The second question my thesis work addresses is: How does an orchestration tool that supports researchers in exploring this space need to be designed to align with teachers’ values for easy integration in the classroom? Specifically, I aimed to support fluid transitions between social planes where students do not all have to be working in sync, which is not currently supported in existing orchestration tools. To support the orchestration tool design, I present a framework that structures the space that a researcher can explore when combining individual and collaborative learning. The framework can act as the set of requirements to be met in the orchestration tool from the point of the researcher as well as a lens to analyze and design combined social plane activities. As a first step towards supporting fluid transitions as laid out in the framework, I present a set of statistical models that extend domain-level individual modeling into the space of collaborative environments. Finally, I developed an orchestration prototype built around my framework that can be used as a research tool to further explore combined collaborative and individual spaces. To develop the tool to be successful within the classroom, I worked with teachers through a co-design process and validation of the prototype to incorporate their values into the tool. Taken together, my dissertation has six primary contributions. My dissertation contributes to the learning sciences through advancing our knowledge of (1) the strengths of collaborative and individual learning, although I did not find any complementary strengths, and (2) if a combination is better than either alone, which I did find support for. It contributes to educational technology through (3) the design of an effective ITS that supports collaborative and individual learning for fractions and educational data mining through (4) the advancement of models that can more accurately predict individual learning within a collaborative setting than the existing individual models. Finally, it contributes to computer supported collaborative learning and human-computer interaction through (5) a framework, which provides a lens for designing and analyzing combined collaborative and individual learning spaces, and (6) an orchestration prototype that supports fluid transitions between social planes in a way that can be a useful to both researchers and teachers in the classroom.

As part of an effort to develop an intelligent tutoring system, a set of knowledge representation frameworks was proposed to represent expert domain knowledge. A general representation of time points and temporal relations was developed to facilitate temporal concept deductions as well as facilitating explanation capabilities vital in an intelligent advisor system. Conventional representations of time use a single-referenced timeline and assigns a single unique value to the time of occurrence of an event. They fail to capture the notion of events, such as changes in signal states in microcomputer systems, which do not occur at precise points in time, but rather over a range of time with some probability distribution. Time is, fundamentally, a relative quantity. In conventional representations, this relative relation is implicitly defined with a fixed reference, "time-zero", on the timeline. This definition is insufficient if an explanation of the temporal relations is to be constructed. The proposed representation of time solves these two problems by representing a time point as a time-range and making the reference point explicit. An architecture of the system was also proposed to provide a means of integrating various modules as the system evolves, as well as a modular development approach. A production rule EXPERT based on the rule framework used in the Graphic Interactive LISP tutor (GIL) [44, 45], an intelligent tutor for LISP programming, was implemented to demonstrate the inference process using this time point representation. The EXPERT is goal-driven and is intended to be an integral part of a complete intelligent tutoring system. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

This master thesis is focused on tutoring systems and their practical usage in education. In the theoretical part of this work is introducted architecture of this systems together with characteristics of types and examples. Next there is a description of systems in programming courses. Based on this knowledge application for teaching Python through feedback to student codes is proposed. Implementation and presentation of tests results, gained in interaction with real users, are introduced in the practical part of the work.

abstract: Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process. This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps. Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

The COVID-19 pandemic wiped off decades of educational gains in the developing world and added 24 million more children to 775 million illiterates in the world. To counteract such a huge predicament, human learning agility comes into action. This human characteristic of knowing what to do when one does not know what to do, invokes the Soft System Methodology (SSM) approach to analyze illiteracy as the worst of all pandemics since it infiltrates into generations. After evaluating different effective teaching methods and utilizing the SSM approach, this paper proposes suitable pedagogies to educate deprived students. It examines Massive Online Open Courseware (MOOC) as a viable solution for K-12 students and compares it with a more robust educational technology model of Intelligent Tutoring System (ITS). Using artificial intelligence, the ITS tailors the instructional content framework and teaching strategies after evaluating students' pre-existing knowledge, learning habits, & styles. The ITS engages the student with the lesson with a two-way dialog while providing customized instruction and immediate feedback. An ITS requires no human intervention and could be a suitable replacement for an inadequately qualified teacher or no teacher. Hence it could be a practical tool in tackling the global literacy catastrophe. A comprehensive literature review followed by a meta-analysis reveals the effectiveness of ITS as a feasible intervention. The major purpose of this study is to define the application of educational pedagogy behind AI-based tutoring and cognitive science in this learner-centered approach.

Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners' performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics.