Supporting interdisciplinary case studies: development and implementation of a joint learning environment for students in forensic science and criminal law

Scientific reporting and communication is a challenging topic for which traditional study programs do not offer structured learning activities on a regular basis. This paper reports on the development and implementation of a web application and associated learning activities that intend to raise the awareness of reporting and communication issues among students in forensic science and law. The project covers interdisciplinary case studies based on a library of written reports about forensic examinations. Special features of the web framework, in particular a report annotation tool, support the design of various individual and group learning activities that focus on the development of knowledge and competence in dealing with reporting and communication challenges in the students' future areas of professional activity.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Invisible disability, visible people: a closer look at the experiences of teachers with learning disabilities /

Throughout their schooling experiences, students with learning disabilities (LD) face numerous academic and socioemotional challenges. Some of these individuals rise above these obstacles to obtain a postsecondary education and become professionals. Recently, there have been a number of individuals with learning disabilities who have chosen a career in teaching. There is a lack of research that documents the experiences of teachers with learning disabilities. The purpose of this qualitative study is to gain an understanding of the challenges that the teachers with learning disabilities strive to overcome and the supports that they receive ^^^ch facilitate their inception into teaching. Four teachers with learning disabilities were the participants in this collective case study research. Data were collected through semistructured interviews. These data were coded, collapsed into themes, and the results were presented in a narrative form. The resultant 9 themes are: (a) Perspectives on School Experiences, (b) Identification and Effective Accommodations, (c) Isolation, Frustration, and Support, (d) Awareness of Learning Disability at Age 18, (e) Disclosure of Learning Disability, (f) Negative Impact of the Learning Disability Label, (g) Desire, Drive, and Obstacles, (h) Empathy, Compassion, and Self-Concept, and (i) Critical Views of Colleagues. The themes reflect the common experiences among participants. The discussion brings forth new information that is not found in other research. The impHcations of this research will interest teacher federations, parents of students with LD, teachers, and educational researchers.

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.

Inspiring lecturers: sharing e-learning practice through european case studies

What motivates a university lecturer to consider introducing a new e-learning approach to their educational practice? Accounts of e-learning practice can invite discussion and reflection on the approaches taken, reinforcement of a particular model, connection with the experience of others, vicarious learning opportunities and glimpses into tacit knowledge. If these examples provoke thinking, could they have the ‘sticky qualities’, the memorable inspiration and ideas that move us to action, when we observe the practice of others? (Szulanski, 2003) “Case studies have the capacity to inspire but also to provoke and to challenge.” (JISC, 2004) This paper will discuss a process followed for sharing best practices of e-learning. It will explain how good practices were identified and gathered by the EUNIS E-Learning Task Force collaboration, using a database and a weblog (EUNIC, 2008). It will examine the methods used for the developing and compiling of the practices and the communication of these. Actual examples of some of the case studies gathered will be included in an appendix. Suggestions of ways to develop this process further and the tangible benefits identified will be examined to ask if effective practice can also become embedded practice.

Disabling a family? Emotional dilemmas experienced in becoming a parent of a child with learning disabilities

Becoming the parent of a child diagnosed with learning disabilities can have a dramatic impact. Chrissie Rogers, the author of this article, is both a lecturer in education studies at Keele University and the mother of a daughter who has learning disabilities. She argues here that the pressures on mothers to produce ‘perfect’ babies and to meet all their needs are immense. These pressures arise from both internalised norms and societal expectations and, in the face of these pressures, parents may feel shock, loss and disappointment. These feelings may lead, in turn, to denial, anxiety and conflict affecting both the parents and the professionals involved with the family. Drawing on a series of in-depth interviews and personal narratives, Chrissie Rogers makes a powerful case for the importance of support, whether that support is formal or informal. She suggests that, without the right levels of support and understanding, having a child with a diagnosis of learning disability can disable the whole family.

The Effect of Active Student Responding in Computer-Assisted Instruction on Social-Studies Learning by Students with Learning Disabilities: A Brief Report

An alternating treatment design was used to compare the effects of three student response conditions (Clicking, Repeating, and Listening) during computer-assisted instruction on social-studies facts learning and maintenance. Results showed that all students learned and maintained more social-studies facts taught in the Repeating condition followed by the Clicking condition.

The effect of active student responding in computer -assisted instruction on social -studies learning by students with learning disabilities

The purpose of this study was to compare the effects of three student response conditions during computer-assisted instruction on the acquisition and maintenance of social-studies facts. Two of the conditions required active student responding (ASR), whereas the other required an on-task (OT) response. Participants were five fifth-grade students, with learning disabilities enrolled in a private school. An alternating treatments design with a best treatments phase was used to compare the effects of the response procedures on three major dependent measures: same-day tests, next-day tests, and maintenance tests. ^ Each week for six weeks, participants were provided daily one-to-one instruction on sets of 21 unknown social-studies facts using a hypermedia computer program, with a new set of facts being practiced each week. Each set of 21 facts was divided randomly into three conditions: Clicking-ASR, Repeating-ASR, and Listening-OT. Hypermedia lesson began weekly with the concept introduction lesson, followed by practice and testing. Practice and testing occurred four days per week, per set. During Clicking-ASR, student practice involved the selection of a social-studies response by clicking on an item with the mouse on the hypermedia card. Repeating-ASR instruction required students to orally repeat the social-studies facts when prompted by the computer. During Listening-OT, students listened to the social-studies facts being read by the computer. During weeks seven and eight, instruction occurred with seven unknown facts using only the best treatment. ^ Test results show that all for all 5 students, the Repeating-ASR practice procedure resulted in more social-studies facts stated correctly on same-day tests, next-day tests, and one-and two-week maintenance tests. Clicking-ASR was the next most effective procedure. During the seventh and eighth week of instruction when only the best practice condition was implemented, Repeating-ASR produced higher scores than all conditions (including Repeating-ASR) during the first six weeks of the study. ^ The results lend further support to the growing body of literature that demonstrates the positive relation between ASR and student achievement. Much of the ASR literature has focused on the effects of increased ASR during teacher-led or peer-mediated instruction. This study adds a dimension to that research in that it demonstrated the importance of ASR during computer-assisted instruction and further suggests that the type of ASR used during computer-assisted instruction may influence learning. Future research is needed to investigate the effectiveness of other types of ASR during computer-assisted instruction and to identify other fundamental characteristics of an effective computer-assisted instruction. ^

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Reasserting curriculum design through virtual learning environments : The case of MAPE

II European Conference on Curriculum Studies. "Curriculum studies: Policies, perspectives and practices”. Porto, FPCEUP, October 16th - 17th.

Structuring and moodleing a course: case studies at the polytechnic of Porto - School of engineering

This work presents a comparative study covering four different courses lectured at the Polytechnic of Porto - School of Engineering, in respect to the usage of a particular Learning Management System, i.e. Moodle, and its impact on students' results. Even though positive correlation factors exist, e.g. between the number of Moodle accesses versus the final exam grade obtained by each student, the explanation behind it may not be straightforward. Mapping this particular factor to course numbers reveals that the quality of the resources might be preponderant and not only their quantity. This paper also addresses teachers who used this platform as a complement to their courses (b-learning) and identifies some particular issues they should be aware in order to potentiate students' engagement and learning.

Engaging students by moodleing a course? Case studies at the Polytechnic of Porto–School of Engineering

This work presents a comparative study covering four different courses lectured at the Polytechnic of Porto - School of Engineering, regarding the usage of a particular Learning Management System, i.e. Moodle, and its impact on students' results. This study addresses teachers who used this platform as a complement to their courses (b-learning) and identifies some particular issues in order to potentiate students' engagement and learning. Even though positive correlation factors exist, e.g. between the number of Moodle accesses versus the final exam grade obtained by each student, the explanation behind it may not be straightforward. Mapping this particular factor to course numbers reveals that the quality of the resources might be preponderant and not only their quantity. These results point to the fact that some dynamic resources might enlarge students' engagement.

Workshop: Capacity building for national public health advocacy: case studies from national associations

The first statement of the EUPHA on the Future of Public Health in Europe refers to the need for going 'to policymakers, politicians and practitioners in all sectors of society and advise them on how to promote public health throughout society'. WHO-EURO Director General Marc Danzon, quoted in the second EUPHA statement on the responsibility of policy makers indicates that 'learning is not systematically applied in health policy development in our continent'. Statement 3 calls for the integration of public health into the political agenda in all sectors. The first EUPHA president, Louise Gunning-Schepers, quoted in Statement 10 called on EUPHA to become 'a powerful advocate of the public health community'. In addition to the above, the EU is now actively seeking ways to build capacity to implement its health strategy. Learning and building the capacity to achieve our aims The aims and objectives to promote the public's health as reflected in EUPHA's 10 statements are also mirrored in the national public health associations. However, many of EUPHA's national associations have little or limited experience in promoting public health policy at the national level. To assist in the learning of advocacy for public health policies, case studies presenting experiences of national public health organizations in lobbying for national public health policy will be presented and discussed. In addition to sharing experiences, the presentations will identify successful approaches to public health advocacy as well as lessons learned from unsuccessful attempts.