Augmented Reality (AR) has become a popular interactive technique in the last few years. One of the critical challenges is to identify the real-life objects. Further, how to fully exert the advantages of the AR technique under the limited resources available on the mobile devices is another critical challenge. To resolve the above issue, firstly this thesis reviewed the real-life object tagging and identification techniques. Secondly this thesis studied the Human Computer Interaction (HCI) Interface and the environmental sensors on the mobile phones. Lastly this thesis implemented a Multiple Real-life Object Identification Algorithm along with the development of the Multi Object Identification Augmented Reality (MOIAR) application. Subsequently, the MOIAR application has been implemented in the location-based mobile learning environment, where the Legislative Assembly of Alberta is included as an example real-life learning object. This MOIAR implementation has applied the tagging and identification technique review as well as the HCI and sensors study, to prove the usability and practicability of the MOIAR application. / 2012-01

abstract: This study was conducted to assess the performance of 176 students who received algebra instruction through an online platform presented in one of two experimental conditions to explore the effect of personalized learning paths by comparing it with linearly flowing instruction. The study was designed around eight research questions investigating the effect of personalized learning paths on students’ learning, intrinsic motivation and satisfaction with their experience. Quantitative results were analyzed using Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA) and split-plot ANOVA methods. Additionally, qualitative feedback data were gathered from students and teachers on their experience to better explain the quantitative findings as well as improve understanding of how to effectively design an adaptive personalized learning platform. Quantitative results of the study showed no statistical difference between students assigned to treatments that compared linear and adaptive personalized instructional flows. The lack of significant differences was explained by two main factors: (a) low usage and (b) platform and content related issues. Low usage may have prevented students from being exposed to the platforms long enough to create a potential for differences between the groups. Additionally, the reasons for low usage may in part be explained by the qualitative findings, which indicated that unmotivated and tired teachers and students were not very enthusiastic about the study because it occurred near the end of school year. Further, computer access was a challenging issue at the school throughout the study. On the other hand, platform and content related issues worked to inhibit the potential beneficial effects of the platforms. The three prominent issues were: (a) the majority of the students found the content boring or difficult, (b) repeated recommendations from the adaptive platform created frustration, and (c) a barely moving progress bar caused disappointment among participants. / Dissertation/Thesis / Doctoral Dissertation Educational Technology 2015

Syftet med denna studie är att karaktärisera det arbetsorganisatoriska lärandet på företag inom tjänstesektorn ur ett genusperspektiv. Den yrkesklass som fokuseras är tjänstemän. För att besvara denna övergripande problematik studeras män och kvinnor och deras tillgång till och upplevelse av lärandet i både formella och informella former. Frågeställningarna handlar om huruvida männen och kvinnornas tillgång till lärande skiljer sig, både vad gäller utbud av formella läraktiviteter som förutsättningar för informellt lärande, samt vilka skillnader det finns mellan könen vad gäller karaktären på det arbetsorganisatoriska lärandet, huruvida det verkar vara tal om ett reproduktivt eller utvecklingsinriktat lärande. Studien bygger på en kvantitativ statistisk enkätstudie om 53 frågor som har besvarats av 73 personer från fem olika företag. Resultaten visar att männen och kvinnornas tillgång till lärande ser olika ut, både vad gäller utbud av formella läraktiviteter som förutsättningar för informellt lärande, på så sätt att männens tillgång till lärande är större; de deltar i större utsträckning i formella läraktiviteter och har bättre förutsättningar för informellt lärande. Det finns även tendenser om skillnader mellan könen vad gäller karaktären på det arbetsorganisatoriska lärandet, på så sätt att det bland kvinnorna i högre grad indikeras ett reproduktivt lärande medan det bland männen i högre grad indikeras ett utvecklingsinriktat lärande. Slutsatsen är att det finns anledning att misstänka att vårt ojämställda arbetsliv återspeglas även på det arbetsorganisatoriska lärandet.

Today, climate change, has become one of the greatest societal challenges of our time. This challenge requires an accurate understanding of climate change for making informed decisions regarding the environmental issues. The carbon cycle is one of the earth’s complicated cycles that has a critical role in the planet’s climate. Developing a thorough perception about this complex cycle uncovers how human activities impact the planet and reveals the connection between multiple environmental issues.Perceiving this complex cycle requires systems thinking skills that enable students to recognize components of the carbon cycle and understand the interrelating dynamic relationship between them. Establishing systems thinking skills and developing a thorough perception about the carbon cycle is a difficult matter for students. Adaptive visualisation-based tutoring systems have a great potential for facilitating teaching and learning cyclical models and systems thinking in schools. Such systems consider the students’ needs and provide personalised feedback that can guide individuals more effectively throughout the learning process. This thesis project intends to use diagrammatic visualizations, systems thinking, and adaptive tutoring systems as three technical approaches for conceptualising a learning system that aims to teach the carbon cycle. The framework of this thesis project is formed in relation to a research project called ‘Tracing Carbon’ focusing on science education for pupils on grade 7-9.

[pt] O forward premium puzzle é um dos puzzles mais notáveis no mercado cambial. Seus estudos tiveram início na primeira metade da década de 1980 e desde então diversas metodologias, ao longo dos anos, foram elaboradas e utilizadas para tentar explicar a sua ocorrência. Entretanto, apesar dos esforços dos pesquisadores, ainda não há uma solução inequívoca. O objetivo desta dissertação é analisar esta anomalia no contexto macroeconômico dos países emergentes, considerando-se a abordagem das expectativas não-racionais dos agentes. Para isso, foram aplicadas técnicas de aprendizado econométrico, conforme as metodologias propostas por Chakraborty e Evans (2008) e Reed (2019). Segundo estas, as expectativas dos agentes acerca da taxa de câmbio spot futura, são modeladas de acordo com algoritmos de aprendizado e se mantêm próximas à solução de expectativas racionais, porém com desvios gerados por erros de previsão passados. Os resultados alcançados corroboram aqueles encontrados na literatura relacionada, demonstrando que o aprendizado econométrico pode fornecer uma explicação para o forward premium puzzle também quando se considera a taxa cambial das moedas dos países emergentes frente ao Dólar americano, tanto para o modelo com um único estado quanto para o modelo com dois estados. As simulações realizadas reproduzem as principais características empíricas encontradas na amostra analisada e exprimem a importância da persistência dos fundamentos monetários para explicar o viés negativo no coeficiente do forward premium. / [en] The forward premium puzzle is one of the most notable puzzles in the foreign exchange market. Seminal studies in the field began in the first half of the 1980s. Since then, several methodologies have been proposed and tested, aiming to explain the occurrence of forward premium puzzle. However, despite the researchers efforts, no unequivocal solution has been fund. The objective of this dissertation is to analyze this anomaly in the macroeconomic context of emerging countries considering the approach of the non-rational expectations. To that end, adaptive learning techniques were applied, following the methodologies proposed by Chakraborty and Evans (2008) and Reed (2019), where the modeling of agents expectations about the future spot exchange rate is conducted using learning algorithms and remain close to the rational expectations solution, but with deviations generated from past forecast errors. The results corroborate those found in the current body of literature, suggesting that adaptive learning can provide an explanation for the forward premium puzzle also when considering the exchange rate of emerging market currencies against the US dollar, both for the single-state model and for the two-state model. The simulations results reproduce the main empirical features found in the analyzed sample and express the importance of the monetary fundamentals persistence to explain the negative bias in the forward premium coefficient.

Sistemas de Aprendizagem Adaptativos e Inteligentes, tema de pesquisa recente no mundo, são ambientes com arquitetura e algoritmos específicos, que consideram as características individuais de cada estudante para selecionar o objeto de aprendizagem mais adequado a ser oferecido ao aluno. O rápido desenvolvimento da infraestrutura sem fio e o amplo uso de dispositivos móveis na vida diária das pessoas motivam as pesquisas relativas ao uso desses dispositivos na educação, proporcionando o m-learning. Assim, relacionado a essas linhas de pesquisa, este trabalho propõe a arquitetura AdaptMLearning, elaborada para prover a aprendizagem em plataformas móveis e não móveis, considerando a seleção de objetos de aprendizagem que melhor se adaptam a diversos aspectos, tais como: dados sobre a tecnologia utilizada para acesso; informações sobre o estilo de aprendizagem de um estudante; desempenho e tempo associados à interação do estudante com o objeto de aprendizagem; conhecimentos adquiridos pelo estudante em consonância ao conteúdo do curso; e a garantia de que não só o professor possa configurar as adaptações a serem oferecidas ao seu curso, como também o aluno tenha a possibilidade de informar sua preferência pelos tipos de mídia. Essa arquitetura é baseada no modelo de referência AHAM para sistemas adaptativos AEHS, contemplando a quádrupla: espaço do conhecimento, modelo do usuário, observações e modelo de adaptação, referente à definição lógica desses sistemas. Na AdaptMLearning, foram desenvolvidos alguns algoritmos, utilizando-se o modelo FSLSM, relacionado aos estilos de aprendizagem de um estudante e o padrão IEEE 1484 para catalogação dos objetos de aprendizagem e uso de alguns atributos de suas categorias, associados às dimensões dos estilos de aprendizagem do modelo FSLSM. O algoritmo calcula um peso para um objeto catalogado em cada dimensão e permite uma busca pelo objeto mais adequado ao estilo do estudante, além de usar a computação fuzzy, para avaliar se o estudante pode sofrer mudanças no seu estilo, deve receber reforço ou necessita de um reestudo em determinado assunto de um curso, por meio de resultados obtidos com o tempo de estudo e desempenho. Também, este trabalho apresenta o desenvolvimento e a avaliação de um simulador para a arquitetura AdaptMLearning e seus algoritmos, realizada utilizando diversos cenários de simulação, envolvendo estudantes, cursos e tecnologias com diferentes configurações. Assim sendo, com base nos resultados obtidos por meio da avaliação, foi possível discutir, analisar e identificar o potencial de uso da AdaptMLearning e de seus algoritmos em uma situação real para elaboração de um ambiente de aprendizagem ou agregação a um ambiente existente. / Intelligent and Adaptive Learning Systems, subject of recent research in the world, are environments with specific architectures and algorithms, designed considering the individual characteristics of each student. The rapid development of wireless infrastructures and wide use of mobile devices in people\'s everyday life encourage research about the use of these devices in education, providing the mlearning. In the context of such research, this work proposes the AdaptMLearning architecture that was designed to be a learning infrastructure for mobile and nonmobile platforms. This architecture provides a selection of learning objects that takes into account as adaptation criteria the following data: the mobile device\'s technological specification; the student\'s learning style information, his/her performance and spent time associated to the student\'s interaction with the learning object; previously acquired knowledge by the student related to the course\'s content. In addition, it also allows the teacher to interfere in the adaptation criteria used during the study simulation, and allows the student to indicate his/her preferences for media types. This architecture is based on AHAM reference model for adaptive systems AEHS and uses the quadruple: the knowledge space, the user model, the observations and the model adaptation, referring to the logical definition of these systems. To implement the AdaptMLearning architecture some algorithms using the FSLSM model related to the student\'s learning styles were developed. The algorithms use the IEEE 1484 for cataloging learning objects and some of its categories and attributes associated with dimensions of learning styles FSLSM model, are used to compute a weight of an object in each dimension allowing a search of the most appropriate object according to the student\'s learning styles; and the use of fuzzy computing, considering that the student\'s learning style can change, determines if the student has to receive reinforcement or need a new study in a particular subject of a course, when the student gets unsatisfactory results in terms of timing and performance in a course\'s subject. Also, this work also presents the development and evaluation of a simulator for the AdaptMLearning architecture and their algorithms. The evaluation of the simulator was done by means of many simulations scenarios, considering students, courses and technologies with different settings. Based on the results obtained from the evaluation it was possible to discuss, analyze and identify the potential use of AdaptMLearning architecture and their algorithms in a real situation for developing a learning environment or its aggregation to an existing environment.

Sistemas de Aprendizagem Adaptativos e Inteligentes, tema de pesquisa recente no mundo, são ambientes com arquitetura e algoritmos específicos, que consideram as características individuais de cada estudante para selecionar o objeto de aprendizagem mais adequado a ser oferecido ao aluno. O rápido desenvolvimento da infraestrutura sem fio e o amplo uso de dispositivos móveis na vida diária das pessoas motivam as pesquisas relativas ao uso desses dispositivos na educação, proporcionando o m-learning. Assim, relacionado a essas linhas de pesquisa, este trabalho propõe a arquitetura AdaptMLearning, elaborada para prover a aprendizagem em plataformas móveis e não móveis, considerando a seleção de objetos de aprendizagem que melhor se adaptam a diversos aspectos, tais como: dados sobre a tecnologia utilizada para acesso; informações sobre o estilo de aprendizagem de um estudante; desempenho e tempo associados à interação do estudante com o objeto de aprendizagem; conhecimentos adquiridos pelo estudante em consonância ao conteúdo do curso; e a garantia de que não só o professor possa configurar as adaptações a serem oferecidas ao seu curso, como também o aluno tenha a possibilidade de informar sua preferência pelos tipos de mídia. Essa arquitetura é baseada no modelo de referência AHAM para sistemas adaptativos AEHS, contemplando a quádrupla: espaço do conhecimento, modelo do usuário, observações e modelo de adaptação, referente à definição lógica desses sistemas. Na AdaptMLearning, foram desenvolvidos alguns algoritmos, utilizando-se o modelo FSLSM, relacionado aos estilos de aprendizagem de um estudante e o padrão IEEE 1484 para catalogação dos objetos de aprendizagem e uso de alguns atributos de suas categorias, associados às dimensões dos estilos de aprendizagem do modelo FSLSM. O algoritmo calcula um peso para um objeto catalogado em cada dimensão e permite uma busca pelo objeto mais adequado ao estilo do estudante, além de usar a computação fuzzy, para avaliar se o estudante pode sofrer mudanças no seu estilo, deve receber reforço ou necessita de um reestudo em determinado assunto de um curso, por meio de resultados obtidos com o tempo de estudo e desempenho. Também, este trabalho apresenta o desenvolvimento e a avaliação de um simulador para a arquitetura AdaptMLearning e seus algoritmos, realizada utilizando diversos cenários de simulação, envolvendo estudantes, cursos e tecnologias com diferentes configurações. Assim sendo, com base nos resultados obtidos por meio da avaliação, foi possível discutir, analisar e identificar o potencial de uso da AdaptMLearning e de seus algoritmos em uma situação real para elaboração de um ambiente de aprendizagem ou agregação a um ambiente existente. / Intelligent and Adaptive Learning Systems, subject of recent research in the world, are environments with specific architectures and algorithms, designed considering the individual characteristics of each student. The rapid development of wireless infrastructures and wide use of mobile devices in people\'s everyday life encourage research about the use of these devices in education, providing the mlearning. In the context of such research, this work proposes the AdaptMLearning architecture that was designed to be a learning infrastructure for mobile and nonmobile platforms. This architecture provides a selection of learning objects that takes into account as adaptation criteria the following data: the mobile device\'s technological specification; the student\'s learning style information, his/her performance and spent time associated to the student\'s interaction with the learning object; previously acquired knowledge by the student related to the course\'s content. In addition, it also allows the teacher to interfere in the adaptation criteria used during the study simulation, and allows the student to indicate his/her preferences for media types. This architecture is based on AHAM reference model for adaptive systems AEHS and uses the quadruple: the knowledge space, the user model, the observations and the model adaptation, referring to the logical definition of these systems. To implement the AdaptMLearning architecture some algorithms using the FSLSM model related to the student\'s learning styles were developed. The algorithms use the IEEE 1484 for cataloging learning objects and some of its categories and attributes associated with dimensions of learning styles FSLSM model, are used to compute a weight of an object in each dimension allowing a search of the most appropriate object according to the student\'s learning styles; and the use of fuzzy computing, considering that the student\'s learning style can change, determines if the student has to receive reinforcement or need a new study in a particular subject of a course, when the student gets unsatisfactory results in terms of timing and performance in a course\'s subject. Also, this work also presents the development and evaluation of a simulator for the AdaptMLearning architecture and their algorithms. The evaluation of the simulator was done by means of many simulations scenarios, considering students, courses and technologies with different settings. Based on the results obtained from the evaluation it was possible to discuss, analyze and identify the potential use of AdaptMLearning architecture and their algorithms in a real situation for developing a learning environment or its aggregation to an existing environment.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

This study aimed to examine the effectiveness of video self-modelling (VSM) and the iPad application (Fast Fact Math, FFM) interventions on a group of Year 5 students to increase their knowledge of basic number facts. This study also aimed to measure the intervention group’s self-efficacy levels (Patterns of Adaptive Learning Scales, PALS) before and following the interventions. Participants were drawn from a decile 9 primary school in a suburban area (teaches Year 1 to Year 6). The Test (pre-, mid-, and post-test phases) were administered by a class teacher to all Year 5 students. Following consultation with the teacher, eight students whose scores fell below the 25th percentile were invited to participate in the study. The intervention group took a specific level test to ascertain their basic number facts performance on all four operations (addition, subtraction, multiplication, and division). The videos and the FFM app were personalised to each intervention group’s members in an effort to elicit from the errors that they made on specific level test. At the completion of each intervention sessions, session probes were conducted. Meanwhile, the researcher gave a self-efficacy test (PALS) to the participants before and following intervention phases. Results showed that, although more than half of the intervention group increased their basic number fact performance level following the interventions, their overall self-efficacy rating on PALS did not change. Results also showed that VSM is a time-efficient and rapid learning method to use with the intervention group as opposed to the iPad app, which took two times longer to complete a session. Further areas of study are suggested.