Enhancing Student Learning: Study of a Motivational Resource for Educators

This study surveyed practicing classroom teacher’s perceptions of a proposed educational resource “Avatar Academy” designed to enhance students’, particularly young boys, motivation and general attitude towards learning. The Avatar Academy resource is an instructional guide for implementing a classroom reward system based on common game mechanics. The resource emphasizes the modification of current pedagogies to exploit the use of game design to engage boys. A survey of recent literature indicated an opportunity to study teachers’ perceptions of the possible applications of game design mechanics to support the enhancement of student motivation and learning in the classroom. As a result the Avatar Academy handbook and blog resource were developed to assist teachers with the integration and administration of a program designed to enhance student motivation, especially boys, using avatars and a point based reward system. The resources were initially distributed to several practicing teachers for their review, and their feedback formed the basis for revisions of the Avatar Academy resource. After implementing changes to the resource based on initial teacher feedback, an updated Avatar Academy was redistributed and teacher opinions and perceptions of the tool’s possible impacts on classroom learning were collected.

Towards Equitable Access to Postsecondary Education: Learning From the Voices of First Generation Students

This qualitative, phenomenological study investigated first generation students’ perceptions of the challenges they experienced in the process of accessing higher education and the type of school-based support that was received. Particular emphasis was placed on the impact of parental education level on access to postsecondary education (PSE) and how differences in support at the primary and secondary levels of schooling influenced access. Purposeful, homogenous sampling was used to select 6 first generation students attending a postsecondary institution located in Ontario. Analysis of the data revealed that several interrelated factors impact first generation students’ access to postsecondary education. These include familial experiences and expectations, school streaming practices, secondary school teachers’ and guidance counselors’ representations of postsecondary education, and the nature of school-based support that participants received. The implications for theory, research, and practice are discussed and recommendations for enhancing school-based support to ensure equitable access to postsecondary education for first generation students are provided.

The Implementations of Professional Learning Communities (PLCs) in Beijing and Ontario Schools

Since the knowledge-based economy has become a fashion over the last few decades, the concept of the professional learning community (PLC) has started being accepted by educational institutions and governments as an effective framework to improve teachers’ collective work and collaboration. The purpose of this research was to compare and contrast the implementations of PLCs between Beijing schools and Ontario schools from principals’ personal narratives. In order to discover the lessons and widen the scope to understand the PLC, this research applied qualitative design to collect the data from two principal participants in each location by semistructured interviews. Four themes emerged: (a) structure and technology, (b) identity and climate, (c) task and support, and (d) change and challenge. This research found that the root of the characteristics of the PLCs in Beijing and Ontario was the different existing teaching and learning systems as well as the test systems. Teaching Research Groups (TRGs) is one of the systems that help Chinese to organize routine time and input resources to improve teachers’ professional development. However, Canadian schools lack a similar system that guarantees the time and resources. Moreover, standardized test plays different roles in China and Canada. In China, standardized tests, such as the college entrance examination, are regarded as the important purpose of education, whereas Ontario principals saw the Education Quality and Accountability Office (EQAO) as a tool rather than a primary purpose. These two main differences influenced principals’ beliefs, attitudes, strategies, and practices. The implications based on this discovery provide new perspectives for principals, teachers, policy makers, and scholars to widen and deepen the research and practice of the PLC.

How Immigrant Mothers Contribute to Their Children’s Learning Inside and Outside of School

Abstract The main focus of this qualitative research was to explore how parents from different national backgrounds see their role in their children’s education inside and outside of school. Although greater recruitment was described and sought after, this qualitative research gathered data from two immigrant female parents from a community parents’ group located in Ontario, Canada. Data were collected through face-to-face interviews with each participant using open-ended questions asking about the different ways these mothers, along with their spouses, were involved in their children’s education. Moreover, questions were designed to find out what alternatives parents use to support their children’s learning. The main question driving this research was “How are immigrant families currently involved with their children’s education inside and outside of school?” NVivo, 10 was used to code the transcripts giving rise to themes which could then be utilized to explain and explore the research question. The findings of this research are congruent with past research and demonstrate that immigrant mothers are more involved than the fathers are in their children’s education (Grolnick & Slowiaczek 1994; Peters, Seeds, Goldstein, & Coleman, 2008). A specifically important finding in this research is that schools are perceived by the immigrant mothers in this study as not doing enough to actively engage immigrant parents in their children’s education. On the other hand, findings also show that parents are eager to find different avenues to get involved and help their children succeed.

Coping with a Learning Disability: A Case Study

The purpose of this qualitative case study was to understand a child’s experience with a learning disability (LD) through the way that they cope with it, and how self-esteem, self-efficacy, attribution style, and social support contribute to this process. Qualitative interviews were conducted with one child, his parents, and his teacher, accompanied by a content analysis of the child’s psychosocial assessment report. It was found that the child copes well with having a learning disability, employing a problem-focused/approach coping style by seeking help and practicing for skills he struggles with, an emotion-focused coping style by implementing strategies to alleviate frustration, and compartmentalizing his disability. Further, self-esteem, self-efficacy, attribution style, social support and sports and leisure engagement were found to contribute positively to the coping process. These findings offer useful implications for parents, teachers, and practitioners to support other students with LD.

L'utilisation de la musique comme support de nouveaux apprentissages dans le vieillissement normal et la maladie d'Alzheimer

L’objectif de ce travail de thèse est d’évaluer le potentiel de la musique comme support mnémotechnique pour l’acquisition de nouvelles informations chez des personnes âgées saines et atteintes de la maladie d’Alzheimer (MA). Les bénéfices de la musique sur la cognition ont souvent été mis en évidence, y compris chez des populations âgées ou atteintes de démence. Parallèlement, chez des sujets jeunes, l’idée que la musique peut servir de support pour la mémoire a été largement débattue. Pourtant, très peu d’études ont posé cette question auprès de populations âgées ou dans la démence, malgré le besoin persistant de stratégies d’intervention dans ce domaine. Dans le présent travail, deux études sont menées dans une cohorte de 8 participants atteints d’un stade léger de la maladie d’Alzheimer, et 7 participants âgés sains appariés en âge et niveau de scolarité. La première étude porte sur la mémoire verbale, et compare l’apprentissage et la rétention de paroles (textes inconnus) présentées de manière récitée ou chantée. Lorsque les paroles sont chantées, différents degrés de familiarité de la mélodie sont contrastés. Aussi, l’action motrice étant intimement liée à l’écoute musicale, nous contrastons deux procédures d’apprentissage impliquant (ou non) la production synchronisée des paroles à mémoriser pendant l’encodage : le participant est invité à chanter à l’unisson avec un modèle (ou à écouter simplement sans chanter). Les résultats de cette étude sont présentés et discutés dans les deux premiers articles de la partie expérimentale. Ils suggèrent globalement que la musique n’aide pas l’apprentissage en rappel immédiat ; un effet délétère est même observé lorsque la mélodie utilisée est non familière. Par contre, la musique favorise la rétention à long terme des paroles, principalement pour les participants MA. Elle ne semble cependant pas interagir avec la procédure d’apprentissage impliquant le chant à l’unisson. La seconde étude porte sur l’apprentissage de séquences de gestes. Suivant la même logique que dans la première étude, nous explorons l’influence d’un accompagnement musical (versus apprentissage en silence) et d’une procédure d’apprentissage avec production synchronisée (versus observation) des gestes durant l’encodage. Les résultats (article 3) ne montrent pas non plus d’interaction entre l’accompagnement et la procédure d’apprentissage, mais différents effets de chaque composante sur les deux groupes de participants. Effectuer les gestes en synchronie avec un modèle lors de l’encodage est bénéfique pour les sujets Contrôles, mais plutôt délétère pour les participants MA. Par contre, l’accompagnement musical favorise davantage l’apprentissage chez les sujet MA que chez les Contrôles. En discussion générale, nous discutons les implications de ces résultats pour la neuropsychologie fondamentale et clinique, et proposons notamment différentes recommandations visant à maximiser ces effets et à les rendre pertinents pour l’usage thérapeutique en stimulation cognitive.

Academic and social adjustment of teenagers in social rehabilitation : the role of intrinsic need satisfaction and autonomy support

Les études antérieures ont démontré les bénéfices de la satisfaction des besoins intrinsèques et du soutien à l’autonomie dans le domaine de l’éducation. Or, l’applicabilité des tenants principaux de la Théorie de l’Auto-Détermination (TAD; Deci & Ryan, 2000) n’a pas été investiguée auprès d’une population clinique d’adolescents. L’objectif de cette thèse doctorale est de faire la lumière sur la façon dont l'adaptation scolaire et sociale peut être favorisée par les agents de socialisation dans le contexte de la réadaptation sociale. Cette thèse est composée de deux études s’intéressant à l’application des tenants clés de la TAD auprès de deux échantillons d’adolescents vivant des problèmes d’adaptation et recevant des services d’éducation spécialisée et de réadaptation sociale. Les relations entre les concepts motivationnels de base sont étudiés afin de déterminer si, comme la TAD le propose, la satisfaction des besoins intrinsèques des jeunes peut être soutenue par le style interpersonnel des agents de socialisation (c.-à-d., le soutien à l’autonomie, l’implication et la structure). Il est aussi vérifié si ces concepts motivationnels améliorent la motivation ainsi que d’autres conséquences qui résultent de leur expérience, proposées par la TAD. La première étude a évalué si le style interpersonnel des enseignants peut favoriser la satisfaction des besoins des élèves, leur style de motivationl, tout comme leur ajustement scolaire. Les élèves en difficulté d’adaptation (N = 115) inscrits aux écoles internes des Centres de Réadaptation en raison de leurs problématiques émotionnelles et comportementales ont rempli les questionnaires à deux reprises, au début et à la fin de l’année scolaire. Les analyses de modèles d’équations structurelles révèlent que l’augmentation du soutien à l’autonomie et de l’implication (mais pas de la structure) des enseignants pendant l’année est associée à une augmentation de la satisfaction des besoins des élèves qui, conséquemment, conduit à une motivation scolaire plus auto-déterminée et à une diminution d’intentions de décrochage à la fin de l’année scolaire. De plus, l’amélioration de la satisfaction des besoins mène directement à une meilleure expérience affective à l’école. La deuxième étude consiste en une recherche expérimentale conduite auprès d’adolescentes en difficulté d’adaptation (N = 29). Le devis expérimental a permis de comparer l’impact de la présence (c. absence) du soutien à l’autonomie sur l’internalisation d’une tâche et sur les conséquences motivationnelles et expérientielles des jeunes. La tâche, fastidieuse mais importante, consistait à de la résolution de problèmes interpersonnels (activité clinique). Les résultats suggèrent qu’un style interpersonnel soutenant l’autonomie a augmenté la motivation auto-déterminée, la perception de la valeur de la tâche et son appréciation, ainsi que diminué les affects négatifs comparativement à la condition sans soutien à l’autonomie. Les résultats sont discutés en lien avec les implications théoriques et pratiques d’étendre la portée de la TAD à une population clinique d’adolescents aux prises avec des difficultés d’adaptation.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.