Implementing Web 2.0 in secondary schools: impacts, barriers and issues

One of the reports from the Web 2.0 technologies for learning at KS3 and KS4 project. This report explored Impact of Web 2.0 technologies on learning and teaching and drew upon evidence from multiple sources: field studies of 27 schools across the country; guided surveys of 2,600 school students; 100 interviews and 206 online surveys conducted with managers, teachers and technical staff in these schools; online surveys of the views of 96 parents; interviews held with 18 individual innovators in the field of Web 2.0 in education; and interviews with nine regional managers responsible for implementation of ICT at national level

A Pedagogical Application Framework for Synchronous Collaboration

Designing successful collaborative learning activities is a new focus of research within the E-Learning community. The social dimension inside the traditional face-to-face collaborative learning is important and must be included in the online learning designs. In this thesis, we introduce the concept of Pedagogical Application Frameworks, and describe Beehive, a pedagogical application framework for synchronous collaborative learning. Beehive guides teachers in reusing online collaborative learning activities based on well-known pedagogical designs, to accomplish their educational objectives within a certain educational setting, and also simplifies the development of new pedagogical collaboration designs. Beehive’s conceptual model has four abstraction layers: Pedagogical Techniques, Collaboration Task patterns, CSCL Components, and CSCL script. By following the framework’s guidelines and specifications, developers will place the control of designing pedagogical collaboration tools in the teacher’s hand rather than in the software designer’s

Pervasive learning analytics for fostering learners' self-regulation

Today's tertiary STEM (Science, Technology, Engineering and Mathematics) education in Europe poses problems to both teachers and students. With growing enrolment numbers, and numbers of teaching staff that are outmatched by this growth, student-teacher contact becomes more and more difficult to provide. Therefore, students are required to quickly adopt self-regulated and autonomous learning styles when entering European universities. Furthermore, teachers are required to divide their attention between large numbers of students. As a consequence, classical teaching formats of STEM education which often encompass experimentation or active exploration, become harder to implement. Educational software holds the promise of easing these problems, or, if not fully solving, at least of making them less acute: Learning Analytics generated by such software can foster self-regulation by providing students with both formative feedback and assessments. Educational software, in form of collaborative social media, makes it easier for teachers to collaborate, allows to reduce their workload and enables learning and teaching formats otherwise infeasible in large classes. The contribution of this thesis is threefold: Firstly, it reports on a social medium for tertiary STEM education called "Backstage2 / Projects" aimed specifically at these points: Improving learners' self-regulation by providing pervasive Learning Analytics, fostering teacher collaboration so as to reduce their workload, and providing means to deploy a variety of classical and novel learning and teaching formats in large classes. Secondly, it reports on several case studies conducted with that medium which point at the effectiveness of the medium and its provided Learning Analytics to increase learners' self-regulation, reduce teachers' workload, and improve how students learn. Thirdly, this thesis reports on findings from Learning Analytics which could be used in the future in designing further teaching and learning formats or case studies, yielding a rich perspective for future research and indications for improving tertiary STEM education

The development of a track and field management manual for local organising committees in South Africa

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Cognitive processes in Design Thinking: Optimization of perception, processing and reasoning

This dissertation documents a research endeavour into the cognitive processes in Design Thinking. The goal was to identify the optimal way to think in the various phases of a Design Thinking project. The research draws on the findings in design, positive psychology, cognitive psychology, and neuroscience to analyse the Design Thinking process and tomap and match thinking modes with the phases of the process. The fundamental literature review covers three topics: The research into Design Thinking provides a comprehensive insight into the method and its scientific fundament. Then, creativity as a social product and the cognitive processes relevant to creativity are documented. Thirdly, emotion and its relation to creativity and the Limbic® Map approach are presented. Finally, automatic emotion recognition with deep learning based artificial intelligence algorithms are introduced. The first stages of empirical research revealed that emotions and other affective states are unworkable for reliable research results. Similarly, it could be shown that “mindset” has no scientifically approved definition, making the concept unsuitable for robust research. Further research identified five pairs of cognitive functions needed in Design Thinking. Three pairs address information processing (Acquisition of Data, Alignment of Perception, and Assessment of Information and Ideas), and two address flow control of cognition (Attention to a specific task and Awareness of the Cognitive Process). The research further investigated methods to activate and guide the cognitive functions in a project. Moreover, the importance of including creative professionals in a Design Thinking process was revealed. Research in neuroscience indicates specific abilities of creative people identifiable in the very brain network connections. The research also discovered new insights into the “Groan Zone”. The findings indicate that a change in the attitude and approach to the “Groan Zone” could considerably change the outcome of a Design Thinking project.Esta dissertação documenta um esforço de pesquisa sobre os processos cognitivos em Design Thinking. Teve como objetivo identificar a forma ideal de pensar, nas várias fases de um projeto de Design Thinking. O design, a neurociência, as psicologias positiva e cognitiva, servem de base para analisar o processo de Design Thinking, mapeando e relacionando modos de pensamento com fases do processo. A revisão da literatura cobre: Uma visão mais abrangente do método do Design Thinking, suas origens e fundamentação científica. A criatividade como um produto social e os seus processos cognitivos mais relevantes. A relação entre emoção e criatividade e a abordagem Limbic® Map. Finalmente, são introduzidos métodos de reconhecimento automático da emoção com algoritmos de inteligência artificial, baseados em deep learning. Uma fase de investigação empírica, revelou que as emoções e os outros estados afetivos não são adequados para esta investigação. Pode demonstrar-se ainda que “mentalidade” não possui uma definição cientificamente consensual, tornando o conceito incredível para a investigação. Investigações semelhantes, identificam cinco pares de funções cognitivas necessárias. Três deles, abordam o processamento de informações (Aquisição de Dados, Alinhamento da Perceção e Avaliação de Informações e Ideias) e dois abordam o controle do fluxo cognitivo (Atenção a uma tarefa específica e Consciência do Processo Cognitivo). Aplicaram-se métodos para ativar e guiar as funções cognitivas, num projeto de Design Thinking. Revelou-se a importância de incluir profissionais criativos no processo, pois uma pesquisa em neurociência indica habilitações específicas de pessoas criativas, nas conexões neuronais do seu cérebro. Novos contributos na “Groan Zone”, indicaram que uma mudança de atitude no momento “Groan Zone”, poderá alterar consideravelmente o resultado de um projeto de Design Thinking

Information Diffusion and Summarization in Social Networks

Social networks are web-based services that allow users to connect and share information. Due to the huge size of social network graph and the plethora of generated content, it is difficult to diffuse and summarize the social media content. This thesis thus addresses the problems of information diffusion and information summarization in social networks. Information diffusion is a process by which information about new opinions, behaviors, conventions, practices, and technologies flow from person-to-person through a social network. Studies on information diffusion primarily focus on how information diffuses in networks and how to enhance information diffusion. Our aim is to enhance the information diffusion in social networks. Many factors affect information diffusion, such as network connectivity, location, posting timestamp, post content, etc. In this thesis, we analyze the effect of three of the most important factors of information diffusion, namely network connectivity, posting time and post content. We first study the network factor to enhance the information diffusion, and later analyze how time and content factors can diffuse the information to a large number of users. Network connectivity of a user determines his ability to disseminate information. A well-connected authoritative user can disseminate information to a more wider audience compared to an ordinary user. We present a novel algorithm to find topicsensitive authorities in social networks. We use the topic-specific authoritative position of the users to promote a given topic through word-of-mouth (WoM) marketing. Next, the lifetime of social media content is very short, which is typically a few hours. If post content is posted at the time when the targeted audience are not online or are not interested in interacting with the content, the content will not receive high audience reaction. We look at the problem of finding the best posting time(s) to get high information diffusion. Further, the type of social media content determines the amount of audience interaction, it gets in social media. Users react differently to different types of content. If a post is related to a topic that is more arousing or debatable, then it tends to get more comments. We propose a novel method to identify whether a post has high arousal content or not. Furthermore, the sentiment of post content is also an important factor to garner users’ attention in social media. Same information conveyed with different sentiments receives a different amount of audience reactions. We understand to what extent the sentiment policies employed in social media have been successful to catch users’ attention. Finally, we study the problem of information summarization in social networks. Social media services generate a huge volume of data every day, which is difficult to search or comprehend. Information summarization is a process of creating a concise readable summary of this huge volume of unstructured information. We present a novel method to summarize unstructured social media text by generating topics similar to manually created topics. We also show a comprehensive topical summary by grouping semantically related topics