Using rich data collected from e-learning systems, it may be possible to build up just in time dynamic learner models to analyze learners' behaviours and to evaluate learners' performance in online education systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to find patterns from which metrics can be derived from usage data. In this thesis, we propose a six layer model (raw data layer, fact data layer, data mining layer, measurement layer, metric layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, filter fact data from raw data, and then use clustering mining methods to create measurements and metrics.<p> In a pilot study, we used usage data collected from the iHelp system to create measurements and metrics to observe learners' behaviours in a real online system. The measurements and metrics relate to a learner's sociability, activity levels, learning styles, and knowledge levels. To validate the approach we designed two experiments to compare the metrics and measurements extracted from the iHelp system: expert evaluations and learner self evaluations. Even though the experiments did not produce statistically significant results, this approach shows promise to describe learners' behaviours through dynamically generated measurements and metric. Continued research on these kinds of methodologies is promising.

Using rich data collected from e-learning systems, it may be possible to build up just in time dynamic learner models to analyze learners' behaviours and to evaluate learners' performance in online education systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to find patterns from which metrics can be derived from usage data. In this thesis, we propose a six layer model (raw data layer, fact data layer, data mining layer, measurement layer, metric layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, filter fact data from raw data, and then use clustering mining methods to create measurements and metrics.<p> In a pilot study, we used usage data collected from the iHelp system to create measurements and metrics to observe learners' behaviours in a real online system. The measurements and metrics relate to a learner's sociability, activity levels, learning styles, and knowledge levels. To validate the approach we designed two experiments to compare the metrics and measurements extracted from the iHelp system: expert evaluations and learner self evaluations. Even though the experiments did not produce statistically significant results, this approach shows promise to describe learners' behaviours through dynamically generated measurements and metric. Continued research on these kinds of methodologies is promising.

By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".

By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".

Using electronic portfolios (e-portfolios) to assist learning is an important component of future educational models. A portfolio is a purposeful collection of student work that exhibits the student's efforts, progress and achievements in one or more areas. An e-portfolio contains a variety of information about a person's learning outcomes, such as artifacts, assertions from others, self-reflective information and presentation for different purposes. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. This thesis investigates using the information contained in e-portfolios to initialize the learner model for an intelligent tutoring system. We examine the information model from the e-portfolio standardized specification and present a method that may assist users in initializing learner models using e-portfolios as evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system for testing how accurately a learner model can be built and how beneficial this approach can be for reflective and personalized learning. Experimental results are presented aiming at testing whether accurate learner models can be created through this approach and whether learners can gain benefits in reflective and personalized learning. Monitoring this process can also help ITS developers and experts identify how an initial learner model can automatically arise from an e-portfolio. Additionally, a well-structured learner model, generated by an intelligent tutoring system also can be attached to an e-portfolio for further use by the owner and others.

Using electronic portfolios (e-portfolios) to assist learning is an important component of future educational models. A portfolio is a purposeful collection of student work that exhibits the student's efforts, progress and achievements in one or more areas. An e-portfolio contains a variety of information about a person's learning outcomes, such as artifacts, assertions from others, self-reflective information and presentation for different purposes. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. This thesis investigates using the information contained in e-portfolios to initialize the learner model for an intelligent tutoring system. We examine the information model from the e-portfolio standardized specification and present a method that may assist users in initializing learner models using e-portfolios as evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system for testing how accurately a learner model can be built and how beneficial this approach can be for reflective and personalized learning. Experimental results are presented aiming at testing whether accurate learner models can be created through this approach and whether learners can gain benefits in reflective and personalized learning. Monitoring this process can also help ITS developers and experts identify how an initial learner model can automatically arise from an e-portfolio. Additionally, a well-structured learner model, generated by an intelligent tutoring system also can be attached to an e-portfolio for further use by the owner and others.

Many online learning technologies grant students great autonomy and control, which imposes high demands for self-regulated learning (SRL) skills. With the fast development of online learning technologies, helping students acquire SRL skills becomes critical to student learning. Theories of SRL emphasize that making problem selection decisions is a critical SRL skill. Research has shown that appropriate problem selection that fit with students’ knowledge level will lead to effective and efficient learning. However, it has also been found that students are not good at making problem selection decisions, especially young learners. It is critical to help students become skilled in selecting appropriate problems in different learning technologies that offer learner control. I studied this question using, as platform, a technology called Intelligent Tutoring Systems (ITSs), a type of advanced learning technology that has proven to be effective in supporting students’ domain level learning. It has also been used to help students learn SRL skills such as help-seeking and self-assessment. However, it is an open question whether ITS can be designed to support students’ learning of problem selection skills that will have lasting effects on their problem selection decisions and future learning when the tutor support is not in effect. ITSs are good at adaptively selecting problems for students based on algorithms like Cognitive Mastery. It is likely, but unproven, that ITS problem selection algorithms could be used to provide tutoring on students’ problem selection skills through features like explicit instructions and instant feedback. Furthermore, theories of SRL emphasize the important role of motivations in facilitating effective SRL processes, but not much prior work in ITS has integrated designs that could foster the motivations (i.e., motivational design) to stimulate and sustain effective problem selection behaviors. Lastly, although students generally appreciate having learner control, prior research has found mixed results concerning the effects of learner control on students’ domain level learning outcomes and motivation. There is need to investigate how learner control over problem selection can be designed in learning technologies to enhance students’ learning and motivation. My dissertation work consists of two parts. The first part focuses on creating and scaffolding shared student/system control over problem selection in ITSs by redesigning an Open Learner Model (OLM, visualizations of learning analytics that show students’ learning progress) and integrating gamification features to enhance students’ domain level learning and enjoyment. I conducted three classroom experiments with a total of 566 7th and 8th grade students to investigate the effectiveness of these new designs. The results of the experiments show that an OLM can be designed to support students’ self-assessment and problem selection, resulting in greater learning gains in an ITS when shared control over problem selection is enabled. The experiments also showed that a combination of gamification features (rewards plus allowing re-practice of completed problems, a common game design pattern) integrated with shared control was detrimental to student learning. In the second part of my dissertation, I apply motivational design and user-centered design techniques to extend an ITS with shared control over problem selection so that it helps students learn problem selection skills, with a lasting effect on their problem selection decisions and future learning. I designed a set iv of tutor features that aim at fostering a mastery-approach orientation and learning of a specific problem selection rule, the Mastery Rule. (I will refer to these features as the mastery-oriented features.) I conducted a fourth classroom experiment with 200 6th – 8th grade students to investigate the effectiveness of shared control with mastery-oriented features on students’ domain level learning outcomes, problem selection skills and enjoyment. This experiment also measured whether there were lasting effects of the mastery-oriented shared control on students’ problem selection decisions and learning in new tutor units. The results of the experiment show that shared control over problem selection accompanied by the mastery-oriented features leads to significantly better learning outcomes, as compared to full system-controlled problem selection in the ITS. Furthermore, the mastery-oriented shared control has lasting effects on students’ declarative knowledge of problem selection skills. Nevertheless, there was no effect on future problem selection and future learning, possibly because the tutor greatly facilitated problem selection (through its OLM and badges). My dissertation contributes to the literatures on the effects of learner control on students’ domain level learning outcomes in learning technologies. Specifically, I have shown that a form of learner control (i.e., shared control over problem selection, with mastery-oriented features) can lead to superior learning outcomes than system-controlled problem selection, whereas most prior work has found results in favor of system control. I have also demonstrated that Open Learner Models can be designed to enhance student learning when shared control over problem selection is provided. Further, I have identified a specific combination of gamification features integrated with shared control that may be detrimental to student learning. A second line of contributions of my dissertation concerns research on supporting SRL in ITSs. My work demonstrates that supporting SRL processes in ITSs can lead to improved domain level learning outcomes. It also shows that the shared control with mastery-oriented features have lasting effects on improving students’ declarative knowledge of problem selection skills. Regarding using ITSs to help students learn problem selection skill, the user-centered motivational design identifies mastery-approach orientation as important design focus plus tutor features that can support problem selection in a mastery-oriented way. Lastly, the dissertation contributes to human-computer interaction by generating design recommendations for how to design learner control over problem selection in learning technologies that can support students’ domain level learning, motivation and SRL.

O desenvolvimento de sistemas de ensino a distância (EaD) adaptativos vêm sendo o alvo de pesquisa nos últimos anos, porém uma das carências mais importantes é que estes sistemas não possuem dados suficientes que descrevam o aluno, de modo a realizar a adaptação adequada. Uma das grandes dificuldades no processo é a aquisição desses dados. Normalmente para a realização de um curso em um sistema de EaD na Web, o aluno, entre outras atividades, deve cadastrar-se no sistema e informar seus dados pessoais. Alguns sistemas possuem mecanismos para descoberta das preferências do aluno, seu estilo de aprendizagem ou estilo cognitivo, visando oferecer um ensino personalizado. Porém, se este mesmo aluno se matricular em outro curso que utilize outro sistema de EaD na Web, todas essas informações não são repassadas de um sistema para o outro e acabam sendo informadas ou descobertas novamente. Portanto, os sistemas não colaboram entre si no sentido de tornar as informações sobre os alunos mais completas. Quanto mais variada for a informação que os sistemas tiverem sobre os alunos, o modelo do aluno em cada sistema estará mais completo e, conseqüentemente, a adaptabilidade do conteúdo ao seu perfil, mais eficiente. O objetivo principal desta tese é solucionar a problemática associada ao gerenciamento de dados contidos no modelo de aluno quando compartilhadas entre vários ambientes de ensino a distância (EaD). Como solução, esta tese propõe o modelo LPEM (Learner Profile Exchange Model) que define as estratégias que regem o compartilhamento de dados de modelos de aluno entre diversos sistemas. O diferencial do modelo está no uso de uma ontologia (OntoLearner), baseada em padrões, para a troca de dados. A especificação da ontologia OntoLearner também consiste em uma contribuição da tese, e pode ser utilizada no contexto de qualquer sistema desse mesmo domínio. Um subconjunto relativo as principais funcionalidades do modelo foi implementado, para isso também foi definida nesta tese a especificação física do modelo, que oferece as funcionalidades do modelo na forma de Web services. Uma arquitetura orientada a serviços também é descrita na tese e serve de referencia para implementação do modelo LPEM. O protótipo serviu de indicativo para comprovar que a solução proposta é possível de ser implementada, gerando os resultados esperados quanto ao compartilhamento dos dados. / The development of adaptive systems has been the target of some research works over the last years. However, an important lack to be considered is that these systems do not have enough information about the student in order to provide an adequate adaptation. One of the most important drawbacks for this lack is the difficulty of acquiring such information. New learners of an e-learning system are normally required to update their personal information before proceeding in one of the offered courses. Some systems are able to adapt its course content presentation using some techniques that discover the learner’s preferences, level of previous knowledge, and cognitive style. However, this important information about learners is not shared among different e-learning systems. This forces the learner to fill cumbersome forms in each new system, and also forces each new systems to analyze and process new learner’s behavioral information. By consequence, current systems do not collaborate with each other in order to enrich the information related to users of different e-learning systems. Considering these problems, the main objective of this thesis is to address the problem of data management within the learner’s model when data are shared among different e-learning systems. In this thesis we propose the LPEM (Learner Profile Exchange Model) model, which defines the strategies to share the learner’s data model among the different systems. The main contribution of the proposed model is the use of an ontology, named OntoLearner, which is based on standards for data exchange. The ontology OntoLerarner is a contribution as well, since it has been developed for LPEM but can be used by any other system in this domain. A subset of the main functionalities of the proposed model was implemented. For that purpose, the physical model was defined in order to provide a functionalities model for Web Services. The prototype shows that the proposed solution can be implemented, generating the results expected for data sharing.

O desenvolvimento de sistemas de ensino a distância (EaD) adaptativos vêm sendo o alvo de pesquisa nos últimos anos, porém uma das carências mais importantes é que estes sistemas não possuem dados suficientes que descrevam o aluno, de modo a realizar a adaptação adequada. Uma das grandes dificuldades no processo é a aquisição desses dados. Normalmente para a realização de um curso em um sistema de EaD na Web, o aluno, entre outras atividades, deve cadastrar-se no sistema e informar seus dados pessoais. Alguns sistemas possuem mecanismos para descoberta das preferências do aluno, seu estilo de aprendizagem ou estilo cognitivo, visando oferecer um ensino personalizado. Porém, se este mesmo aluno se matricular em outro curso que utilize outro sistema de EaD na Web, todas essas informações não são repassadas de um sistema para o outro e acabam sendo informadas ou descobertas novamente. Portanto, os sistemas não colaboram entre si no sentido de tornar as informações sobre os alunos mais completas. Quanto mais variada for a informação que os sistemas tiverem sobre os alunos, o modelo do aluno em cada sistema estará mais completo e, conseqüentemente, a adaptabilidade do conteúdo ao seu perfil, mais eficiente. O objetivo principal desta tese é solucionar a problemática associada ao gerenciamento de dados contidos no modelo de aluno quando compartilhadas entre vários ambientes de ensino a distância (EaD). Como solução, esta tese propõe o modelo LPEM (Learner Profile Exchange Model) que define as estratégias que regem o compartilhamento de dados de modelos de aluno entre diversos sistemas. O diferencial do modelo está no uso de uma ontologia (OntoLearner), baseada em padrões, para a troca de dados. A especificação da ontologia OntoLearner também consiste em uma contribuição da tese, e pode ser utilizada no contexto de qualquer sistema desse mesmo domínio. Um subconjunto relativo as principais funcionalidades do modelo foi implementado, para isso também foi definida nesta tese a especificação física do modelo, que oferece as funcionalidades do modelo na forma de Web services. Uma arquitetura orientada a serviços também é descrita na tese e serve de referencia para implementação do modelo LPEM. O protótipo serviu de indicativo para comprovar que a solução proposta é possível de ser implementada, gerando os resultados esperados quanto ao compartilhamento dos dados. / The development of adaptive systems has been the target of some research works over the last years. However, an important lack to be considered is that these systems do not have enough information about the student in order to provide an adequate adaptation. One of the most important drawbacks for this lack is the difficulty of acquiring such information. New learners of an e-learning system are normally required to update their personal information before proceeding in one of the offered courses. Some systems are able to adapt its course content presentation using some techniques that discover the learner’s preferences, level of previous knowledge, and cognitive style. However, this important information about learners is not shared among different e-learning systems. This forces the learner to fill cumbersome forms in each new system, and also forces each new systems to analyze and process new learner’s behavioral information. By consequence, current systems do not collaborate with each other in order to enrich the information related to users of different e-learning systems. Considering these problems, the main objective of this thesis is to address the problem of data management within the learner’s model when data are shared among different e-learning systems. In this thesis we propose the LPEM (Learner Profile Exchange Model) model, which defines the strategies to share the learner’s data model among the different systems. The main contribution of the proposed model is the use of an ontology, named OntoLearner, which is based on standards for data exchange. The ontology OntoLerarner is a contribution as well, since it has been developed for LPEM but can be used by any other system in this domain. A subset of the main functionalities of the proposed model was implemented. For that purpose, the physical model was defined in order to provide a functionalities model for Web Services. The prototype shows that the proposed solution can be implemented, generating the results expected for data sharing.

O desenvolvimento de sistemas de ensino a distância (EaD) adaptativos vêm sendo o alvo de pesquisa nos últimos anos, porém uma das carências mais importantes é que estes sistemas não possuem dados suficientes que descrevam o aluno, de modo a realizar a adaptação adequada. Uma das grandes dificuldades no processo é a aquisição desses dados. Normalmente para a realização de um curso em um sistema de EaD na Web, o aluno, entre outras atividades, deve cadastrar-se no sistema e informar seus dados pessoais. Alguns sistemas possuem mecanismos para descoberta das preferências do aluno, seu estilo de aprendizagem ou estilo cognitivo, visando oferecer um ensino personalizado. Porém, se este mesmo aluno se matricular em outro curso que utilize outro sistema de EaD na Web, todas essas informações não são repassadas de um sistema para o outro e acabam sendo informadas ou descobertas novamente. Portanto, os sistemas não colaboram entre si no sentido de tornar as informações sobre os alunos mais completas. Quanto mais variada for a informação que os sistemas tiverem sobre os alunos, o modelo do aluno em cada sistema estará mais completo e, conseqüentemente, a adaptabilidade do conteúdo ao seu perfil, mais eficiente. O objetivo principal desta tese é solucionar a problemática associada ao gerenciamento de dados contidos no modelo de aluno quando compartilhadas entre vários ambientes de ensino a distância (EaD). Como solução, esta tese propõe o modelo LPEM (Learner Profile Exchange Model) que define as estratégias que regem o compartilhamento de dados de modelos de aluno entre diversos sistemas. O diferencial do modelo está no uso de uma ontologia (OntoLearner), baseada em padrões, para a troca de dados. A especificação da ontologia OntoLearner também consiste em uma contribuição da tese, e pode ser utilizada no contexto de qualquer sistema desse mesmo domínio. Um subconjunto relativo as principais funcionalidades do modelo foi implementado, para isso também foi definida nesta tese a especificação física do modelo, que oferece as funcionalidades do modelo na forma de Web services. Uma arquitetura orientada a serviços também é descrita na tese e serve de referencia para implementação do modelo LPEM. O protótipo serviu de indicativo para comprovar que a solução proposta é possível de ser implementada, gerando os resultados esperados quanto ao compartilhamento dos dados. / The development of adaptive systems has been the target of some research works over the last years. However, an important lack to be considered is that these systems do not have enough information about the student in order to provide an adequate adaptation. One of the most important drawbacks for this lack is the difficulty of acquiring such information. New learners of an e-learning system are normally required to update their personal information before proceeding in one of the offered courses. Some systems are able to adapt its course content presentation using some techniques that discover the learner’s preferences, level of previous knowledge, and cognitive style. However, this important information about learners is not shared among different e-learning systems. This forces the learner to fill cumbersome forms in each new system, and also forces each new systems to analyze and process new learner’s behavioral information. By consequence, current systems do not collaborate with each other in order to enrich the information related to users of different e-learning systems. Considering these problems, the main objective of this thesis is to address the problem of data management within the learner’s model when data are shared among different e-learning systems. In this thesis we propose the LPEM (Learner Profile Exchange Model) model, which defines the strategies to share the learner’s data model among the different systems. The main contribution of the proposed model is the use of an ontology, named OntoLearner, which is based on standards for data exchange. The ontology OntoLerarner is a contribution as well, since it has been developed for LPEM but can be used by any other system in this domain. A subset of the main functionalities of the proposed model was implemented. For that purpose, the physical model was defined in order to provide a functionalities model for Web Services. The prototype shows that the proposed solution can be implemented, generating the results expected for data sharing.