The link between the learning organisation and curriculum innovation:A comparative study of public and private primary schools in Greece

This article examines the relationship between the learning organisation and the implementation of curriculum innovation within schools. It also compares the extent of innovative activity undertaken by schools in the public and the private sectors. A learning organisation is characterised by long-term goals, participatory decision-making processes, collaboration with external stakeholders, effective mechanisms for the internal communication of knowledge and information, and the use of rewards for its members. These characteristics are expected to promote curriculum innovation, once a number of control factors have been taken into account. The article reports on a study carried out in 197 Greek public and private primary schools in the 1999-2000 school year. Structured interviews with school principals were used as a method of data collection. According to the statistical results, the most important determinants of the innovative activity of a school are the extent of its collaboration with other organisations (i.e. openness to society), and the implementation of development programmes for teachers and parents (i.e. communication of knowledge and information). Contrary to expectations, the existence of long-term goals, the extent of shared decision-making, and the use of teacher rewards had no impact on curriculum innovation. The study also suggests that the private sector, as such, has an additional positive effect on the implementation of curriculum innovation, once a number of human, financial, material, and management resources have been controlled for. The study concludes by making recommendations for future research that would shed more light on unexpected outcomes and would help explore the causal link between variables in the research model.

Did children’s perceptions of an after-school social learning program predict change in their behavior?

This research explored the influence of children’s perceptions of a pro-social behavior after-school program on actual change in the children’s behavioral outcomes over the program’s duration. Children’s perceptions of three program processes were collected as well as self-reported pro-social and anti-social behavior before and after the program. Statistical models showed that: Positive perceptions of the program facilitators’ dispositions significantly predicted reductions in anti-social behavior; and positive perceptions with the program activities significantly predicted gains in pro-social behavior. The children’s perceptions of their peers’ behavior in the sessions were not found to a significant predictor of behavioral change. The two significant perceptual indicators predicted a small percentage of the change in the behavioral outcomes. However, as after-school social learning programs have a research history of problematic implementation children’s perceptions should be considered in future program design, evaluation and monitoring.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Toward Characterization Of The Type IIP Supernova Progenitor Population: A Statistical Sample Of Light Curves From Pan-STARRS1

In recent years, wide-field sky surveys providing deep multi-band imaging have presented a new path for indirectly characterizing the progenitor populations of core-collapse supernovae (SN): systematic light curve studies. We assemble a set of 76 grizy-band Type IIP SN light curves from Pan-STARRS1, obtained over a constant survey program of 4 years and classified using both spectroscopy and machine learning-based photometric techniques. We develop and apply a new Bayesian model for the full multi-band evolution of each light curve in the sample. We find no evidence of a sub-population of fast-declining explosions (historically referred to as "Type IIL" SNe). However, we identify a highly significant relation between the plateau phase decay rate and peak luminosity among our SNe IIP. These results argue in favor of a single parameter, likely determined by initial stellar mass, predominantly controlling the explosions of red supergiants. This relation could also be applied for supernova cosmology, offering a standardizable candle good to an intrinsic scatter of 0.2 mag. We compare each light curve to physical models from hydrodynamic simulations to estimate progenitor initial masses and other properties of the Pan-STARRS1 Type IIP SN sample. We show that correction of systematic discrepancies between modeled and observed SN IIP light curve properties and an expanded grid of progenitor properties, are needed to enable robust progenitor inferences from multi-band light curve samples of this kind. This work will serve as a pathfinder for photometric studies of core-collapse SNe to be conducted through future wide field transient searches.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Desenvolvimento de um ambiente virtual de aprendizagem em regime de b-learning num curso de educação e formação de operadores de informática

Trabalho de projeto de mestrado, Educação (Área de Especialização em Educação e Tecnologias Digitais), Universidade de Lisboa, Instituto de Educação, 2014

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Interoperability frameworks for competencies and learning outcomes: databases vs ontologies

The changes introduced into the European Higher Education Area (EHEA) by the Bologna Process, together with renewed pedagogical and methodological practices, have created a new teaching-learning paradigm: Student-Centred Learning. In addition, the last few years have been characterized by the application of Information Technologies, especially the Semantic Web, not only to the teaching-learning process, but also to administrative processes within learning institutions. On one hand, the aim of this study was to present a model for identifying and classifying Competencies and Learning Outcomes and, on the other hand, the computer applications of the information management model were developed, namely a relational Database and an Ontology.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

An exploration of the experience of implementing the second step violence prevention curriculum in one primary general learning disabilities class /

Violence has always been a part of the human experience, and therefore, a popular topic for research. It is a controversial issue, mostly because the possible sources of violent behaviour are so varied, encompassing both biological and environmental factors. However, very little disagreement is found regarding the severity of this societal problem. Most researchers agree that the number and intensity of aggressive acts among adults and children is growing. Not surprisingly, many educational policies, programs, and curricula have been developed to address this concern. The research favours programs which address the root causes of violence and seek to prevent rather than provide consequences for the undesirable behaviour. But what makes a violence prevention program effective? How should educators choose among the many curricula on the market? After reviewing the literature surrounding violence prevention programs and their effectiveness, The Second Step Violence Prevention Curriculum surfaced as unique in many ways. It was designed to address the root causes of violence in an active, student-centred way. Empathy training, anger management, interpersonal cognitive problem solving, and behavioural social skills form the basis of this program. Published in 1992, the program has been the topic of limited research, almost entirely carried out using quantitative methodologies.The purpose of this study was to understand what happens when the Second Step Violence Prevention Curriculum is implemented with a group of students and teachers. I was not seeking a statistical correlation between the frequency of violence and program delivery, as in most prior research. Rather, I wished to gain a deeper understanding of the impact ofthe program through the eyes of the participants. The Second Step Program was taught to a small, primary level, general learning disabilities class by a teacher and student teacher. Data were gathered using interviews with the teachers, personal observations, staff reports, and my own journal. Common themes across the four types of data collection emerged during the study, and these themes were isolated and explored for meaning. Findings indicate that the program does not offer a "quick fix" to this serious problem. However, several important discoveries were made. The teachers feU that the program was effective despite a lack of concrete evidence to support this claim. They used the Second Step strategies outside their actual instructional time and felt it made them better educators and disciplinarians. The students did not display a marked change in their behaviour during or after the program implementation, but they were better able to speak about their actions, the source of their aggression, and the alternatives which were available. Although they were not yet transferring their knowledge into positive action,a heightened awareness was evident. Finally, staff reports and my own journal led me to a deeper understanding ofhow perception frames reality. The perception that the program was working led everyone to feel more empowered when a violent incident occurred, and efforts were made to address the cause rather than merely to offer consequences. A general feeling that we were addressing the problem in a productive way was prevalent among the staff and students involved. The findings from this investigation have many implications for research and practice. Further study into the realm of violence prevention is greatly needed, using a balance of quantitative and qualitative methodologies. Such a serious problem can only be effectively addressed with a greater understanding of its complexities. This study also demonstrates the overall positive impact of the Second Step Violence Prevention Curriculum and, therefore, supports its continued use in our schools.

An examination of the effects on learning seen in children afforded the opportunity to control the order of repetitions for three novel spatiotemporal sequences

Children were afforded the opportunity to control the order of repetitions for three novel spatiotemporal sequences. The following was predicted: a) children and adults in the self-regulated (SELF) groups would produce faster movement (MT) and reaction times (R T) and greater recall success (RS) during retention compared to the age-matched yoked (YOKE) groups; b) children would choose to switch sequences less often than adults; c) adults would produce faster MT and RT and greater RS than the children during acquisition and retention, independent of experimental group. During acquisition, no effects were seen for RS, however for MT and RT there was a main effect for age as well as block. During retention a main effect for practice condition was seen for RS and failed to reach statistical significance for MT and RT, thus partially supporting our first and second hypotheses. The third hypothesis was not supported.

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.

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.