Analyzing the learning of the taking personal and social responsibility model within a new physical education undergraduate degree program in El Salvador

EI Salvador presents an unfortunate history that includes a military regime and a civil war that together created a legacy of violence in which the country still struggle nowadays. Salud Escolar Integral (SEI) was created in 2005 as a program to combat youth violence throughout the re-formulation of physical education (PE) classes in public schools, promoting life skills learning that supports the resolution of conflicts with nonviolent ways. In 2007, SEI supported the creation of a physical e~ucation teacher education (PETE) degree at the Universidad Pedag6gica de EI Salvador (UPES), having the goal to assist pre-service teachers with a better understanding of humanistic principles. The present research analyzed if after attending all three years ofUPES PETE program, students presented high self-perception levels of competence and confidence related to attitude, skills and knowledge to teach PE within humanistic principles. Taking Personal and Social Responsibility (TPSR) was the theoretical framework used to analyze the development of humanistic principles. The study had a mixed-method longitudinal design that included questionnaires, reflection templates and interviews. In conclusion, although it is suggested that UPES should provide better support for the development of the teaching principles of empowering students and transfer learning, most of the humanistic principles were highly promoted by the program. At last, it is suggested that future research should track teachers' progress while teaching in schools, in order to analyze if the theory of promoting humanistic principles have also become a daily practice.

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.

Creating a Space for Self-Transformation: Factors of Success for Adult Literacy Learners with Specific Learning Difficulties

A significant number of adults in adult literacy programs in Ontario have specific learning difficulties. This study sought to examine the holistic factors that contributed to these learners achieving their goals. Through a case study design, the data revealed that a combination of specific learning methods and strategies, along with particular characteristics of the instructor, participant, and class, and the evidence of self-transformation all seemed to contribute to the participant's success in the program. Instructor-directed teaching and cooperative learning were the main learning methods used in the class. General learning strategies employed were the use of core curriculum and authentic documents, and using phonics, repetition, assistive resources, and using activities that appealed to various learning styles. The instructor had a history of both professional development in the area of learning disabilities as well as experience working with learners who had specific learning difficulties. There also seemed to be a goodness of fit between the participant and the instructor. Several characteristics of the participant seemed to aid in his success: his positive self-esteem, self-advocacy skills, self-determination, self-awareness, and the fact that he enjoyed learning. The size (3-5 people) and type of class (small group) also seemed to have an impact. Finally, evidence that the participant went through a self-transformation seemed to contribute to a positive learner identity. These results have implications for practice, theory, and further research in adult education.

A Living Educational Theory of Knowledge Translation: Improving Practice, Influencing Learners, and Contributing to the Professional Knowledge Base

This paper captured our joint journey to create a living educational theory of knowledge translation (KT). The failure to translate research knowledge to practice is identified as a significant issue in the nursing profession. Our research story takes a critical view of KT related to the philosophical inconsistency between what is espoused in the knowledge related to the discipline of nursing and what is done in practice. Our inquiry revealed “us” as “living contradictions” as our practice was not aligned with our values. In this study, we specifically explored our unique personal KT process in order to understand the many challenges and barriers to KT we encountered in our professional practice as nurse educators. Our unique collaborative action research approach involved cycles of action, reflection, and revision which used our values as standards of judgment in an effort to practice authentically. Our data analysis revealed key elements of collaborative reflective dialogue that evoke multiple ways of knowing, inspire authenticity, and improve learning as the basis of improving practice related to KT. We validated our findings through personal and social validation procedures. Our contribution to a culture of inquiry allowed for co-construction of knowledge to reframe our understanding of KT as a holistic, active process which reflects the essence of who we are and what we do.

Service Learning in Higher Education: A Road Map

As institutions of higher education struggle to stay relevant, competitive, accessible, and flexible, they are scrambling to attend to a shift in focus for new students. This shift involves experiential learning. The purpose of this major research paper was to examine the existing structures, to seek gaps in the experiential learning programs, and to devise a framework to move forward. The specific focus was on experiential learning at Brock University in the Faculty of Applied Health Sciences. The methodology was underscored with cognitive constructivism and appreciative theory. Data collection involved content analysis steps established by Krippendorff (2004) and Weber (1985). Data analysis involved the four dimensions of reflection designed by LaBoskey, including the purpose, context, content, and procedures. The results developed understandings on the state of formal processes and pathways within service learning. A tool kit was generated that defines service learning and offers an overview of the types of service learning typically employed. The tool kit acts as a reference guide for those interested in implementing experiential learning courses. Importantly, the results also provided 10 key points in experiential learning courses by Emily Allan. A flow chart illustrates the connections among each of the 10 points, and then they are described in full to establish a strategy for the way forward in experiential learning.

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.

Experiential Learning: Creating Meaningful Opportunities

This study examined the practice and implementation of undergraduate student internships in Ontario, Canada. A literature review revealed that implementation of internships at the undergraduate level in Ontario varies within campuses by faculty and department and also across the university spectrum, partly due to a lack of consistency and structure guiding internship practice in Ontario. Moreover, a lack of general consensus among participating stakeholders concerning the philosophy and approach to internship further complicates and varies its practice. While some departments and universities have started to embrace and implement more experiential learning opportunities into their curriculum, the practice of undergraduate internships is struggling to gain acceptance and validity in others. Using the theory of experiential learning as presented by Dewey (1938) and Kolb (1984) as theoretical frameworks, this research project developed an internship implementation strategy to provide structure and guidance to the practice of internships in Ontario’s undergraduate university curriculum.

Postsecondary Teaching and Learning Development Needs: Motivators and Barriers Associated With Participation in Educational Development

This study investigated instructor perceptions of motivators and barriers that exist with respect to participation in educational development in the postsecondary context. Eight instructors from a mid-size, research intensive university in south-western Ontario participated in semistructured interviews to explore this particular issue. Data were analyzed using a qualitative approach. Motivation theory was used as a conceptual framework in this study, referring primarily to the work of Ryan and Deci (2000), Deci and Ryan (1985), and Pink (2009). The identified motivators and barriers spanned all 3 levels of postsecondary institutions: the micro (i.e., the individual), the meso (i.e., the department or Faculty), and the macro (i.e., the institution). Significant motivators to participation in educational development included desire to improve one’s teaching (micro), feedback from students (meso), and tenure and promotion (macro). Significant barriers to participation included lack of time (micro), the perception that an investment towards one’s research was more important than an investment to enhancing teaching (meso), and the impression that quality teaching was not valued by the institution (macro). The study identifies connections between the micro, meso, macro framework and motivation theory, and offers recommendations for practice.

Advances in scaling deep learning algorithms

Les algorithmes d'apprentissage profond forment un nouvel ensemble de méthodes puissantes pour l'apprentissage automatique. L'idée est de combiner des couches de facteurs latents en hierarchies. Cela requiert souvent un coût computationel plus elevé et augmente aussi le nombre de paramètres du modèle. Ainsi, l'utilisation de ces méthodes sur des problèmes à plus grande échelle demande de réduire leur coût et aussi d'améliorer leur régularisation et leur optimization. Cette thèse adresse cette question sur ces trois perspectives. Nous étudions tout d'abord le problème de réduire le coût de certains algorithmes profonds. Nous proposons deux méthodes pour entrainer des machines de Boltzmann restreintes et des auto-encodeurs débruitants sur des distributions sparses à haute dimension. Ceci est important pour l'application de ces algorithmes pour le traitement de langues naturelles. Ces deux méthodes (Dauphin et al., 2011; Dauphin and Bengio, 2013) utilisent l'échantillonage par importance pour échantilloner l'objectif de ces modèles. Nous observons que cela réduit significativement le temps d'entrainement. L'accéleration atteint 2 ordres de magnitude sur plusieurs bancs d'essai. Deuxièmement, nous introduisont un puissant régularisateur pour les méthodes profondes. Les résultats expérimentaux démontrent qu'un bon régularisateur est crucial pour obtenir de bonnes performances avec des gros réseaux (Hinton et al., 2012). Dans Rifai et al. (2011), nous proposons un nouveau régularisateur qui combine l'apprentissage non-supervisé et la propagation de tangente (Simard et al., 1992). Cette méthode exploite des principes géometriques et permit au moment de la publication d'atteindre des résultats à l'état de l'art. Finalement, nous considérons le problème d'optimiser des surfaces non-convexes à haute dimensionalité comme celle des réseaux de neurones. Tradionellement, l'abondance de minimum locaux était considéré comme la principale difficulté dans ces problèmes. Dans Dauphin et al. (2014a) nous argumentons à partir de résultats en statistique physique, de la théorie des matrices aléatoires, de la théorie des réseaux de neurones et à partir de résultats expérimentaux qu'une difficulté plus profonde provient de la prolifération de points-selle. Dans ce papier nous proposons aussi une nouvelle méthode pour l'optimisation non-convexe.

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

Identification of Problem-based learning components and their effects on graduates' competencies

This study investigated the relationship between higher education and the requirement of the world of work with an emphasis on the effect of problem-based learning (PBL) on graduates' competencies. The implementation of full PBL method is costly (Albanese & Mitchell, 1993; Berkson, 1993; Finucane, Shannon, & McGrath, 2009). However, the implementation of PBL in a less than curriculum-wide mode is more achievable in a broader context (Albanese, 2000). This means higher education institutions implement only a few PBL components in the curriculum. Or a teacher implements a few PBL components at the courses level. For this kind of implementation there is a need to identify PBL components and their effects on particular educational outputs (Hmelo-Silver, 2004; Newman, 2003). So far, however there has been little research about this topic. The main aims of this study were: (1) to identify each of PBL components which were manifested in the development of a valid and reliable PBL implementation questionnaire and (2) to determine the effect of each identified PBL component to specific graduates' competencies. The analysis was based on quantitative data collected in the survey of medicine graduates of Gadjah Mada University, Indonesia. A total of 225 graduates responded to the survey. The result of confirmatory factor analysis (CFA) showed that all individual constructs of PBL and graduates' competencies had acceptable GOFs (Goodness-of-fit). Additionally, the values of the factor loadings (standardize loading estimates), the AVEs (average variance extracted), CRs (construct reliability), and ASVs (average shared squared variance) showed the proof of convergent and discriminant validity. All values indicated valid and reliable measurements. The investigation of the effects of PBL showed that each PBL component had specific effects on graduates' competencies. Interpersonal competencies were affected by Student-centred learning (β = .137; p < .05) and Small group components (β = .078; p < .05). Problem as stimulus affected Leadership (β = .182; p < .01). Real-world problems affected Personal and organisational competencies (β = .140; p < .01) and Interpersonal competencies (β = .114; p < .05). Teacher as facilitator affected Leadership (β = 142; p < .05). Self-directed learning affected Field-related competencies (β = .080; p < .05). These results can help higher education institution and educator to have informed choice about the implementation of PBL components. With this information higher education institutions and educators could fulfil their educational goals and in the same time meet their limited resources. This study seeks to improve prior studies' research method in four major ways: (1) by indentifying PBL components based on theory and empirical data; (2) by using latent variables in the structural equation modelling instead of using a variable as a proxy of a construct; (3) by using CFA to validate the latent structure of the measurement, thus providing better evidence of validity; and (4) by using graduate survey data which is suitable for analysing PBL effects in the frame work of the relationship between higher education and the world of work.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

Online Learning of Non-stationary Sequences

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate a class of universal learning algorithms for this problem by expressing them as simple Bayesian algorithms operating on models analogous to Hidden Markov Models (HMMs). We derive a new performance bound for such algorithms which is considerably simpler than existing bounds. The bound provides the basis for learning the rate at which the identity of the optimal expert switches over time. We find an analytic expression for the a priori resolution at which we need to learn the rate parameter. We extend our scalar switching-rate result to models of the switching-rate that are governed by a matrix of parameters, i.e. arbitrary homogeneous HMMs. We apply and examine our algorithm in the context of the problem of energy management in wireless networks. We analyze the new results in the framework of Information Theory.

Models of Learning and Motivation

A brief skim through educational theory intended for students registered on a single module in Technology Enhanced Learning. Startes with Blooms taxonomy, travles through instructivism and constructivism and on to theories of motivation/

Actitudes de los directivos en torno a la adopción del e-learning como herramienta de trabajo en las empresas

El desarrollo del presente documento constituye una investigación sobre las actitudes de los directivos frente a la adopción del e-learning como herramienta de trabajo en las organizaciones de Bogotá. Para ello se realizó una encuesta a 101 directivos, tomando como base el tipo de muestreo de conveniencia; esto con el objetivo de identificar sus actitudes frente al uso del e-learning y su influencia dentro de la organización. Como resultado se obtuvo que las actitudes de los directivos influencian en el uso de herramientas e-learning, así como también en las acciones que promueven su uso y en las actitudes de los empleados; por otro lado se identificó que las creencias relacionadas con la apropiación de herramientas e-learning y los factores facilitadores del uso de estas, influencian en las actitudes de los directivos. Lo anterior, corresponde a los análisis llevados a cabo a partir de los resultados contrastados con los estudios empíricos hallados y el marco teórico desarrollado.