Towards Evaluating and Modelling the Impacts of Mobile-Based Augmented Reality Applications on Learning and Engagement

Mobile augmented reality applications are increasingly utilized as a medium for enhancing learning and engagement in history education. Although these digital devices facilitate learning through immersive and appealing experiences, their design should be driven by theories of learning and instruction. We provide an overview of an evidence-based approach to optimize the development of mobile augmented reality applications that teaches students about history. Our research aims to evaluate and model the impacts of design parameters towards learning and engagement. The research program is interdisciplinary in that we apply techniques derived from design-based experiments and educational data mining. We outline the methodological and analytical techniques as well as discuss the implications of the anticipated findings.

The expression and development of teachers' capacities within two learning communities : a participant-observer case study

The learning community model has been an integral component of teacher development in Ontarian schools and beyond. This research was conducted to understand how teachers' personal capacity and professional, interpersonal, and organizational competencies are developed and expressed within this context. Nineteen elementary teachers and administrators participated in the study from November through January 2007. A qualitative case study methodology was used to investigate the role ofteachers' capacities and competencies in learning communities. Combined data sources from semistructured interviews, research journals, and document review were used to gather data about teachers' capacities and competencies. The study included 3 phases of analysis. In the final phase the analysis provided 3 qualities of the teachers at Jude and Mountain Schools (pseudonyms): identification as professionals, investment in others, and institutional affiliation that may explain how they differed from other educators. The data revealed these three themes, which provided an understanding of educators at Jude and Mountain Schools as dedicated professionals pushing practices to contribute to school life and address student learning needs, and as teachers who reflected on practices to continue expanding their skills. Teachers were heavily invested in creating a caring culture and in students' and team members' learning. Educators actively participated in solving problems and coplanning throughout the school levels and beyond, assumed collective responsibility for all pupils, and focused on generating school-wide consistent practices. These qualities and action patterns revealed teachers who invested time and effort in their colleagues, who committed to develop as professionals, and who affiliated closely with every aspect of school living.

Teacher professional growth in an authentic learning environment

The last several decades have been marked by tremendous changes in education - technological, pedagogical, administrative, and social. These changes have led to considerable increments in the budgets devoted to professional development for teachers ~ with the express purpose of helping them accommodate their practices to the new realities oftheir classrooms. However, research has suggested that, in spite of the emphasis placed on encouraging sustained change in teaching practices, little has been accomplished. This begs the question of what ought to be done to not only reverse this outcome, but contribute to transformational change. The literature suggests some possibilities including: a) considering teachers as learners and applying what, is known about cognition and learning; b) modifying the location and nature ofprofessional development so that it is authentic, based in the classroom and focusing on tasks meaningful to the teacher; c) attending to the infrastructure underlying professional development; and d) ensuring opportunities for reflective practice. This dissertation looks at the impact of each ofthese variables through an analysis ofthe learning journeys of a group ofteachers engaged in a program called GrassRoots in one midsized school board in Ontario. Action research was conducted by the researcher in his role as consultant facilitating teacher professional growth around the use of Web sites as culminating performance tasks by students. Research focused on the pedagogical approach to the learning of the teachers involved and the infrastructure underlying their learning. Using grounded theory, a model for professional development was developed that can be used in the future to inform practices and, hopefully, lead to sustained transformational school change.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Exploring Reciprocity in International Service Learning Programs

Using aspects of grounded theory methodology, this study explored the perceptions and practical implementation of reciprocity in International Service Learning (ISL) Programs. Data were collected through interviews with nine ISL practitioners representing a variety of organizations offering international service learning programs. Findings suggest that multiple conceptualizations of ISL programs exist. ISL programs are interdisciplinary in nature and that using reciprocity as a guiding framework is problematic. Further attention is needed in relation to shifting the guiding framework of ISL programs from reciprocity to interdependence.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.