Informal learning recognition through a cloud ecosystem

Learning and teaching processes, like all human activities, can be mediated through the use of tools. Information and communication technologies are now widespread within education. Their use in the daily life of teachers and learners affords engagement with educational activities at any place and time and not necessarily linked to an institution or a certificate. In the absence of formal certification, learning under these circumstances is known as informal learning. Despite the lack of certification, learning with technology in this way presents opportunities to gather information about and present new ways of exploiting an individual’s learning. Cloud technologies provide ways to achieve this through new architectures, methodologies, and workflows that facilitate semantic tagging, recognition, and acknowledgment of informal learning activities. The transparency and accessibility of cloud services mean that institutions and learners can exploit existing knowledge to their mutual benefit. The TRAILER project facilitates this aim by providing a technological framework using cloud services, a workflow, and a methodology. The services facilitate the exchange of information and knowledge associated with informal learning activities ranging from the use of social software through widgets, computer gaming, and remote laboratory experiments. Data from these activities are shared among institutions, learners, and workers. The project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out.

ESEIG Mobile: an m-learning approach in a superior school

In recent years, mobile learning has emerged as an educational approach to decrease the limitation of learning location and adapt the teaching-learning process to all type of students. However, the large number and variety of Web-enabled devices poses challenges for Web content creators who want to automatic get the delivery context and adapt the content to mobile devices. In this paper we study several approaches to adapt the learning content to mobile phones. We present an architecture for deliver uniform m-Learning content to students in a higher School. The system development is organized in two phases: firstly enabling the educational content to mobile devices and then adapting it to all the heterogeneous mobile platforms. With this approach, Web authors will not need to create specialized pages for each kind of device, since the content is automatically transformed to adapt to any mobile device capabilities from WAP to XHTML MP-compliant devices.

A survey of e-learning content aggregation standards

As e-learning gradually evolved many specialized and disparate systems appeared to fulfil the needs of teachers and students, such as repositories of learning objects, authoring tools, intelligent tutors and automatic evaluators. This heterogeneity raises interoperability issues giving the standardization of content an important role in e-learning. This article presents a survey on current e-learning content aggregation standards focusing on their internal organization and packaging. This study is part of an effort to choose the most suitable specifications and standards for an e-learning framework called Ensemble defined as a conceptual tool to organize a network of e-learning systems and services for domains with complex evaluation.

Odin: a service for gamification of learning activities

Existing gamification services have features that preclude their use by e-learning tools. Odin is a gamification service that mimics the API of state-of-the-art services without these limitations. This paper describes Odin, its role in an e-learning system architecture requiring gamification, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) specification.

Collaborative reflection : supporting one practitioner's development of online learning communities /

This is a study of one participant's reflective practice as she worked to develop online communities in a face-to-face science course. Her process of reflective practice was examined in order to address factors that influenced her learning path, and the benefits and challenges of collaborative action research. These research goals were pursued using a collaborative action research methodology, initially chosen for its close match with Schon's (1983) model of reflective practice. The participant's learning fit vnth Mezirow's (1991) model of transformative learning. She began with beliefs that matched her goals, and she demonstrated significant learning in three areas. First, she demonstrated instrumental learning around the constraints of workload and time, and achieving online learning community indicators. Second, she demonstrated communicative learning that helped her to see her own needs for feedback and communication more clearly, and how other process partners had been a support to her. Third, her emancipatory learning saw her revisiting and questioning her goals. It was through the reflective conversation during the planned meetings and the researcher's reframing and interrogation of that reflection that the participant was able to clarify and extend her thinking, and in so doing, critically reflect on her practice as she worked to develop online learning communities. In this way, the collaborative action research methodology was an embodiment of co-constructivism through collaborative reflective practice. Schon's (1983) model of reflective practice positions a lone practitioners moving through cycles ofplan-act-observe-reflect. The results fi"om this study suggest that collaboration is an important piece of the reflective practice model.

Writing and science connections : integrated instruction and assessment

Learning to write is a daunting task for many young children. The purpose of this study was to examine the impact of a combined approach to writing instruction and assessment on the writing performance of students in two grade 3 classes. Five forms and traits of writing were purposefully connected during writing lessons while exhibiting links to the four strands of the grade 3 Ontario science curriculum. Students then had opportunities to engage in the writing process and to self-assess their compositions using either student-developed (experimental group/teacher-researcher's class) or teachercreated (control group/teacher-participant's class) rubrics. Paired samples t-tests revealed that both the experimental and control groups exhibited statistically significant growth from pretest to posttest on all five integrated writing units. Independent samples t-tests showed that the experimental group outperformed the control group on the persuasive + sentence fluency and procedure + word choice writing tasks. Pearson product-moment correlation r tests revealed significant correlations between the experimental group and the teacher-researcher on the recount + ideas and report + organization tasks, while students in the control group showed significant correlations with the teacher-researcher on the narrative + voice and procedure + word choice tasks. Significant correlations between the control group and the teacher-participant were evident on the persuasive + sentence fluency and procedure + word choice tasks. Qualitative analyses revealed five themes that highlighted how students' self-assessments and reflections can be used to guide teachers in their instructional decision making. These findings suggest that educators should adopt an integrated writing program in their classrooms, while working with students to create and utilize purposeful writing assessment tools.

Introduction to engineering : a case study of an interdisciplinary course in Mathematics, Science and Technology

This thesis research was a qualitative case study of a single class of Interdisciplinary Studies: Introduction to Engineering taught in a secondary school. The study endeavoured to explore students' experiences in and perceptions of the course, and to investigate the viability of engineering as an interdisciplinary theme at the secondary school level. Data were collected in the form of student questionnaires, the researcher's observations and reflections, and artefacts representative of students' work. Data analysis was performed by coding textual data and classifying text segments into common themes. The themes that emerged from the data were aligned with facets of interdisciplinary study, including making connections, project-based learning, and student engagement and affective outcomes. The findings of the study showed that students were positive about their experiences in the course, and enjoyed its project-driven nature. Content from mathematics, physics, and technological design was easily integrated under the umbrella of engineering. Students felt that the opportunity to develop problem solving and teamwork skills were two of the most important aspects of the course and could be relevant not only for engineering, but for other disciplines or their day-to-day lives after secondary school. The study concluded that engineering education in secondary school can be a worthwhile experience for a variety of students and not just those intending postsecondary study in engineering. This has implications for the inclusion of engineering in the secondary school curriculum and can inform the practice of curriculum planners at the school, school board, and provincial levels. Suggested directions for further research include classroom-based action research in the areas of technological education, engineering education in secondary school, and interdisciplinary education.

How Learning Styles, Chemistry Attitudes and Experiences, Confidence, and Demographics Correlate with Academic Success in First and Second Year Chemistry Courses

At Brock University, the Faculty of Mathematics and Science currently has one of the highest percentages of students on academic probation, with many students reporting the most difficulty with Introductory Chemistry in first year and Organic Chemistry in second year. To identify strategies to improve students' performance and reduce the number of students on academic probation, a multi-year research project was undertaken involving several chemistry courses. Students were asked to complete three questionnaires, and provide consent to obtain their final Chemistry grade from the Registrar's Office. Research began at the end of the 2007-08 academic year with CHEM IPOO, and in the 2008-09 academic year, students in the larger CHEM IF92 Introductory Chemistry course were invited to participate in this research near the beginning of the academic year. Students who went on to take second year Organic and Analytical Chemistry were asked to complete these questionnaires in each second year course. The three questionnaires included the Kolb Learning Styles Inventory (Kolb, 1984) modified to include specific reference to Chemistry in each question, Dalgety, ColI, and Jones' (2002) Chemistry Attitudes and Experiences Questionnaire (CAEQ), and lastly, a demographic survey. Correlations were found between learning style and academic success; concrete learners were not as successful as abstract learners. Differences were noted between females and males with respect to learning styles, academic success, and confidence. Several differences were also noted between those who are the First in the Family to attend university and those who are not First in the Family to attend university.

Impact of a Youth Development Camp on Participants’ Skills, Attitudes, and Views Towards Science and Scientific Inquiry

This mixed-methods research study sought to determine the impact of an informal science camp—the Youth Science Inquiry Development Camp (YSIDC)—on participants’ science inquiry skills, through self-assessment, as well as their views and attitudes towards science and scientific inquiry. Pre and post data were collected using quantitative surveys (SPSI, CARS), a qualitative survey (VOSI-E), interviews, and researcher’s observations. Paired sample t-tests from the quantitative surveys revealed that the YSIDC positively impacted participants’ science inquiry skills and attitudes towards science. Interviews supported these findings and provided contextual reasons for these impacts. Implications from this research would suggest that informal and formal educational institutions can increase science inquiry skills and promote positive views and attitudes towards science and scientific inquiry by using non-competitive cooperative learning strategies with a mixture of guided and open inquiry. Suggested directions for further research include measuring science inquiry skills directly and conducting longitudinal studies to determine the lasting effects of informal and formal science programs.

Evolution of Various Library Instruction Strategies: Using Student Feedback to Create and Enhance Online Active Learning Assignments

This case study traces the evolution of library assignments for biological science students from paper-based workbooks in a blended (hands-on) workshop to blended learning workshops using online assignments to online active learning modules which are stand-alone without any face-to-face instruction. As the assignments evolved to adapt to online learning supporting materials in the form of PDFs (portable document format), screen captures and screencasting were embedded into the questions as teaching moments to replace face-to-face instruction. Many aspects of the evolution of the assignment were based on student feedback from evaluations, input from senior lab demonstrators and teaching assistants, and statistical analysis of the students’ performance on the assignment. Advantages and disadvantages of paper-based and online assignments are discussed. An important factor for successful online learning may be the ability to get assistance.

Web2.0, knowledge sharing and privacy in E-learning

Quand le E-learning a émergé il ya 20 ans, cela consistait simplement en un texte affiché sur un écran d'ordinateur, comme un livre. Avec les changements et les progrès dans la technologie, le E-learning a parcouru un long chemin, maintenant offrant un matériel éducatif personnalisé, interactif et riche en contenu. Aujourd'hui, le E-learning se transforme de nouveau. En effet, avec la prolifération des systèmes d'apprentissage électronique et des outils d'édition de contenu éducatif, ainsi que les normes établies, c’est devenu plus facile de partager et de réutiliser le contenu d'apprentissage. En outre, avec le passage à des méthodes d'enseignement centrées sur l'apprenant, en plus de l'effet des techniques et technologies Web2.0, les apprenants ne sont plus seulement les récipiendaires du contenu d'apprentissage, mais peuvent jouer un rôle plus actif dans l'enrichissement de ce contenu. Par ailleurs, avec la quantité d'informations que les systèmes E-learning peuvent accumuler sur les apprenants, et l'impact que cela peut avoir sur leur vie privée, des préoccupations sont soulevées afin de protéger la vie privée des apprenants. Au meilleur de nos connaissances, il n'existe pas de solutions existantes qui prennent en charge les différents problèmes soulevés par ces changements. Dans ce travail, nous abordons ces questions en présentant Cadmus, SHAREK, et le E-learning préservant la vie privée. Plus précisément, Cadmus est une plateforme web, conforme au standard IMS QTI, offrant un cadre et des outils adéquats pour permettre à des tuteurs de créer et partager des questions de tests et des examens. Plus précisément, Cadmus fournit des modules telles que EQRS (Exam Question Recommender System) pour aider les tuteurs à localiser des questions appropriées pour leur examens, ICE (Identification of Conflits in Exams) pour aider à résoudre les conflits entre les questions contenu dans un même examen, et le Topic Tree, conçu pour aider les tuteurs à mieux organiser leurs questions d'examen et à assurer facilement la couverture des différent sujets contenus dans les examens. D'autre part, SHAREK (Sharing REsources and Knowledge) fournit un cadre pour pouvoir profiter du meilleur des deux mondes : la solidité des systèmes E-learning et la flexibilité de PLE (Personal Learning Environment) tout en permettant aux apprenants d'enrichir le contenu d'apprentissage, et les aider à localiser nouvelles ressources d'apprentissage. Plus précisément, SHAREK combine un système recommandation multicritères, ainsi que des techniques et des technologies Web2.0, tels que le RSS et le web social, pour promouvoir de nouvelles ressources d'apprentissage et aider les apprenants à localiser du contenu adapté. Finalement, afin de répondre aux divers besoins de la vie privée dans le E-learning, nous proposons un cadre avec quatre niveaux de vie privée, ainsi que quatre niveaux de traçabilité. De plus, nous présentons ACES (Anonymous Credentials for E-learning Systems), un ensemble de protocoles, basés sur des techniques cryptographiques bien établies, afin d'aider les apprenants à atteindre leur niveau de vie privée désiré.

La science historique et le rapport aux savoirs des manuels québécois du cours collégial «Histoire de la civilisation occidentale» : analyse épistémologique et critique

Alors que l’enseignement de l’histoire au Québec visait jadis à promouvoir l’acquisition par les élèves de contenus spécifiques, nationalistes et religieux, depuis plusieurs décennies des volontés scientifiques et politiques cherchent à réorienter la discipline. L’histoire enseignée doit dorénavant orienter son approche sur celle de la science histoire. D’une focalisation sur la transmission de contenus, on vise maintenant l’apprentissage d’un mode spécifique d’appréhension du réel : la pensée historienne. Cette étude soulève la question de la correspondance entre l’histoire enseignée et l’historiographie savante actuelle dans un contexte de changement de paradigme épistémologique et des rapports au savoir que cela implique. L’hypothèse posée est que l’histoire enseignée – telle qu’elle apparait dans les manuels d’histoire occidentale à l’usage des cégépiens et cégépiennes – n’a pas suivi l’évolution historiographique des dernières années, précisément quant à leur posture épistémologique.

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.

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.