Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

Awareness and Learning in Participatory Noise Sensing

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

To Succeed Is to Believe: Comparison of Self-Efficacy Beliefs in Language Learning between Learner Groups of English and Swedish in Finland

In this thesis, I studied self-efficacy in the learning of English and Swedish in Finland. The theory of self-efficacy, which was created by Albert Bandura, suggests that the beliefs a person has of his or her capabilities in a certain task affect the person’s performance in the task. My aim was to study whether there are differences in self-efficacy beliefs between the learners of English and Swedish, and whether these beliefs correlate with the performance in the language in question. My hypotheses were that the learners of English have higher self-efficacy beliefs than the learners of Swedish and that self-efficacy beliefs correlate with language performance. The study was quantitative, and it consisted of a self-efficacy questionnaire and a language test which were distributed to students of English and Swedish in an upper secondary school in Rovaniemi. The study was answered by 137 students, of whom 93 were learners of English and 44 were learners of Swedish. The results indicated that the learners of English had a higher sense of efficacy than the learners of Swedish. The analysis proved that there was a significant correlation between English students’ self-efficacy and their performance in the language measured by the test and the grades. In addition, a significant correlation existed between Swedish students’ self-efficacy and their grades. However, there was no correlation between the Swedish students’ self-efficacy and their test results. The difference in the self-efficacy beliefs of the two language groups indicates that people in Finland are more confident in using English than Swedish, which also implies that English is more valued in Finnish society than Swedish. It is important to acknowledge the lower self-efficacy beliefs in Swedish because various studies have proven that self-efficacy affects academic achievement. As a suggestion for further research, the self-efficacy beliefs of different language groups could be compared in a qualitative study in order to understand the development of self-efficacy more profoundly.

Finnish Preclinical and Clinical Medical Students as Strategic L2 English Learners: Language Learning Strategies and Communicating with Patients

This thesis focused on medical students’ language learning strategies for patient encounters. The research questions concerned the types of learning strategies that medical students use and the differences between the preclinical students and the clinical students, two groups who have had varying amounts of experience with patients. Additionally, strategy use was examined through activity systems to gain information on the context of language learning strategy use in order to learn language for patient encounters. In total, 130 first-year medical students (preclinical) and 39 fifth-year medical students (clinical) participated in the study by filling in a questionnaire on language learning strategies. In addition, two students were interviewed in order to create activity systems for the medical students at different stages of their studies. The study utilised both quantitative and qualitative research methods; the analysis of the results relies on Oxford’s Strategic Self-Regulation Model in the quantitative part and on activity theory in the qualitative part. The theoretical sections of the study introduced earlier research and theories regarding English for specific purposes, language learning strategies and activity theory. The results indicated that the medical students use affective, sociocultural-interactive and metasociocultural-interactive strategies often and avoid using negative strategies, which hinder language learning or cease communication altogether. Slight differences between the preclinical and clinical students were found, as clinical students appear to use affective and metasociocultural-interactive strategies more frequently compared to the preclinical students. The activity systems of the two students interviewed were rather similar. The students were at different stages of their studies, but their opinions were very similar. Both reported the object of learning to be mutual understanding between the patient and the doctor, which in part explains the preference for strategies that support communication and interaction. The results indicate that the nature of patient encounters affects the strategy use of the medical students at least to some extent.

Assessing a Methodological Proposal for the Design, Creation and Evaluation of Learning Objects Oriented to Educators with Diverse Educational and Technological Competencies

This paper describes a methodological proposal for the design, creation and evaluation of Learning Objects (LOs). This study arises from the compilation and analysis of several LO design methodologies currently used in Ibero-America. This proposal, which has been named DICREVOA, defines five different phases: analysis, design (instructional and multimedia), implementation (LO and metadata), evaluation (from the perspective of both the producer and the consumer of the LO), and publishing. The methodology focuses not only on the teaching inexperienced, but also on those having a basic understanding of the technological and educational aspects related to LO design; therefore, the study emphasizes LO design activities centered around the Kolb cycle and the use of the ExeLearning tool in order to implement the LO core. Additionally, DICREVOA was used in a case study, which demonstrates how it provides a feasible mechanism for LO design and implementation within different contexts. Finally, DICREVOA, the case study to which it was applied, and the results obtained are presented

The introduction of e-learning in European universities: models and strategies

The aim of this paper is to present the models and the strategies of adoption of e-learning in a group of European universities, most of them located in the regions called “the four motors of Europe” (Baden-Württenberg, Catalunya, Lombardy and Rhône-Alpes) and in Switzerland. Our analysis focuses on four dimensions: the rationale behind the introduction of e-learning, the organisation of the activities and, in particular, the existence of a university centre for e-learning, the type of activities, and, finally, the type of public reached by e-learning. The majority of campus universities in our sample introduced e-learning to improve the quality of education of their students and, for the most part, as a support for existing courses. Some of the campus universities went even further insofar as they have introduced some online courses into their curricula. This has led to forms of cooperation where different universities share some of their courses. Finally, a small number of campus universities have included as part of their educational offer full distance degree programs which can be attended also by non residential students. The above cases show that there is no general move from campus universities towards distance education, but rather a more selective behaviour. Thus we conclude that e-learning, although it is undoubtedly spreading in both distance and presence universities, is not yet bringing fundamental changes in the institutions themselves. E-learning is at the moment integrated into the existing organization and educational offer. (DIPF/Orig.)

Predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil

Background: Learning styles are cognitive, emotional, and physiological traits, as well as indicators of how learners perceive, interact, and respond to their learning environments. According to Honey-Mumford, learning styles are classified as active, reflexive, theoretical, and pragmatic. Objective: The purpose of this study was to identify the predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil. Methods: An observational, cross-sectional, and descriptive study was conducted using the Honey-Alonso Learning Style Questionnaire. Students in the Bachelor of Pharmacy program were invited to participate in this study. The questionnaire comprised 80 randomized questions, 20 for each of the four learning styles. The maximum possible score was 20 points for each learning style, and cumulative scores indicated the predominant learning styles among the participants. Honey-Mumford (1986) proposed five preference levels for each style (very low, low, moderate, high, and very high), called a general interpretation scale, to avoid student identification with one learning style and ignoring the characteristics of the other styles. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 20.0. Results: This study included 297 students (70% of all pharmacy students at the time) with a median age of 21 years old. Women comprised 77.1% of participants. The predominant style among pharmacy students at the Federal University of Paraná was the pragmatist, with a median of 14 (high preference). The pragmatist style prevails in people who are able to discover techniques related to their daily learning because such people are curious to discover new strategies and attempt to verify whether the strategies are efficient and valid. Because these people are direct and objective in their actions, pragmatists prefer to focus on practical issues that are validated and on problem situations. There was no statistically significant difference between genders with regard to learning styles. Conclusion: The pragmatist style is the prevailing style among pharmacy students at the Federal University of Paraná. Although students may have a learning preference that preference is not the only manner in which students can learn, neither their preference is the only manner in which students can be taught. Awareness of students’ learning styles can be used to adapt the methodology used by teachers to render the teaching-learning process effective and long lasting. The content taught to students should be presented in different manners because varying teaching methods can develop learning skills in students.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Learning Management Systems (LMS) and e-learning management: an integrative review and research agenda

Information Technology (IT) can be an important component for innovation since enabling e-learning it can provide conditions to which the organization can work with new business and improved processes. In this regard, the Learning Management Systems (LMS) allows communication and interaction between teachers and students in virtual spaces. However the literature indicates that there are gaps in the researches, especially concerning the use of IT for the management of e-learning. The purpose of this paper is to analyze the available literature about the application of LMS for the e-learning management, seeking to present possibilities for researches in the field. An integrative literature review was performed considering the Web of Science, Scopus, Ebsco and Scielo databases, where 78 references were found, of which 25 were full papers. This analysis derives interesting characteristics from scientific studies, highlighting gaps and guidelines for future research.

Modelling the development of an online learning resource by health care professionals

The aim of this study was to model the process of development for an Online Learning Resource (OLR) by Health Care Professionals (HCPs) to meet lymphoedema-related educational needs, within an asset-based management context. Previous research has shown that HCPs have unmet educational needs in relation to lymphoedema but details on their specific nature or context were lacking. Against this background, the study was conducted in two distinct but complementary phases. In Phase 1, a national survey was conducted of HCPs predominantly in community, oncology and palliative care services, followed by focus group discussions with a sample of respondents. In Phase 2, lymphoedema specialists (LSs) used an action research approach to design and implement an OLR to meet the needs identified in Phase 1. Study findings were analysed using descriptive statistics (Phase 1), and framework, thematic and dialectic analysis to explore their potential to inform future service development and education theory. Unmet educational need was found to be specific to health care setting and professional group. These resulted in HCPs feeling poorly-equipped to diagnose and manage lymphoedema. Of concern, when identified, lymphoedema was sometimes buried for fear of overwhelming stretched services. An OLR was identified as a means of addressing the unmet educational needs. This was successfully developed and implemented with minimal additional resources. The process model created has the potential to inform contemporary leadership theory in asset-based management contexts. This doctoral research makes a timely contribution to leadership theory since the resource constraints underpinning much of the contribution has salience to current public services. The process model created has the potential to inform contemporary leadership theory in asset-based management contexts. Further study of a leadership style which incorporates cognisance of Cognitive Load Theory and Self-Determination Theory is suggested. In addition, the detailed reporting of process and how this facilitated learning for participants contributes to workplace education theory

Playing for Real: Designing Alternate Reality Games in Learning Contexts

Alternate Reality Game (ARG) represent a new genre of transmedia practice where players hunt for scattered clues, make sense of disparate information, and solve puzzles to advance an ever-evolving storyline. Players participate in ARGs using multiple communications technologies, ranging from print materials to mobile devices. However, many interaction design challenges must be addressed to weave these everyday communication tools together into an immersive, participatory experience. Transmedia design is not an everyday process. Designers must create and connect story bits across multiple media (video, audio, text) and multiple platforms (phones, computers, physical spaces). Furthermore, they must engage with players of varying skill levels. Few studies to-date have explored the design process of ARGs in learning contexts. Fewer still have focused on challenges involved in designing for youth (13-17 years old). In this study, I explore the process of designing ARGs as vehicles for promoting information literacy and participatory culture for adolescents (13-17 years old). Two ARG design scenarios, distinguished by target learning environment (formal and informal context) and target audience (adolescents), comprise the two cases that I examine. Through my analysis of these two design cases, I articulate several unique challenges faced by designers who create interactive, transmedia stories for – and with – youth. Drawing from these design challenges, I derive a repertoire of design strategies that future designers and researchers may use to create and implement ARGs for teens in learning contexts. In particular, I propose a narrative design framework that allows for the categorization of ARGs as storytelling constructs that lie along a continuum of participation and interaction. The framework can serve as an analytic tool for researchers and a guide for designers. In addition, I establish a framework of social roles that designers may employ to craft transmedia narratives before live launch and to promote and scaffold player participation after play begins. Overall, the contributions of my study include theoretical insights that may advance our understanding of narrative design and analysis as well as more practical design implications for designers and practitioners seeking to incorporate transmedia features into learning experiences that target youth.

Construção da plataforma de e-Learning do Instituto Politécnico de Santarém

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.

Construção da plataforma de e-Learning do Instituto Politécnico de Santarém

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.