No / This paper discusses the aspect of student support that emerged as a key component of a longitudinal study into the experiences of nurses studying through open learning in the UK. Students engaged in this study were mature learners who were practicing nurses and predominantly, but not exclusively, women. Participants perceived entering higher education as a considerable challenge.

Atualmente, as redes sociais estão ganhando cada vez mais destaque no dia-a-dia das pessoas. Nessas redes são estabelecidos diferentes relacionamentos entre entidades que compartilham alguma característica ou objetivo em comum. Diversas informações sobre a produção científica nacional podem ser encontradas na Plataforma Lattes, que é um sistema utilizado para o registro dos currículos dos pesquisadores no Brasil. A partir dessas informações é possível construir uma rede social acadêmica, na qual as relações entre os pesquisadores representam uma parceria na produção de uma publicação (coautoria) - um link. Na análise de redes sociais existe uma linha de pesquisa conhecida como predição de link ou de relacionamentos, que tem como objetivo identificar relacionamentos futuros. Essa tarefa pode favorecer a comunicação entre os usuários e otimizar o processo de produção científica identificando possíveis colaboradores. Este projeto analisou a influência de diferentes atributos encontrados na literatura e filtros de dados para prever relações de coautoria nas redes sociais acadêmicas. Foi abordado dois tipos de problemas na predição de relacionamentos, o problema geral que analisa todos os possíveis relacionamentos de coautoria e o problema de novas coautoria que refere-se aos relacionamentos de coautorias inéditas na rede. Os resultados dos experimentos foram promissores para o problema geral de predição com a combinação de atributos e filtros utilizados. Contudo, para o problema de novas coautorias, devido à sua maior complexidade, os resultados não foram tão bons. Os experimentos apresentados avaliaram diferentes estratégias e analisaram o custo e benefício de cada uma. Conclui-se que para lidar com o problema de predição de coautorias em redes sociais acadêmicas é necessário analisar as vantagens e desvantagens entre as estratégias, encontrando um equilíbrio entre a revocação da classe positiva e a acurácia geral / Nowadays, social networks are gaining prominence in the day-to-day lives. In these networks, different relationships are established between entities that share some characteristic or common goal. A huge amount of information about the Brazilian national scientific production can be found in the Lattes Platform, which is a system used to record the curricula of researchers in Brazil. From this information, it is possible to build an academic social network, where relations between researchers represent a partnership in the production of a publication - a link. In social network analysis there is a research area known as link prediction, which aims to identify future relationships. This task may facilitate communication among researchers and optimize the scientific production process identifying possible collaborators. This project analyzed the influence of different attributes found in the literature and data filters to predict co-authorship relationships in academic social networks. Was approached two types of problems in predicting relationships, the general problem that analyzes all possible co-authoring relationships and the problem of new co-authoring that relates to novel co-authorships relationships in the network. The experimental results were promising to the prediction general problem, combining attributes and using filters. However, for the new co-authorships problem the results were not as good. The experiments evaluated different strategies and analyzed the costs and benefits of each. We concluded that to deal with the co-authorships prediction problem in academic social networking it is necessary to analyze the advantages and disadvantages among the strategies, finding a balance between the recall of the positive class and the overall accuracy

Atualmente, as redes sociais estão ganhando cada vez mais destaque no dia-a-dia das pessoas. Nessas redes são estabelecidos diferentes relacionamentos entre entidades que compartilham alguma característica ou objetivo em comum. Diversas informações sobre a produção científica nacional podem ser encontradas na Plataforma Lattes, que é um sistema utilizado para o registro dos currículos dos pesquisadores no Brasil. A partir dessas informações é possível construir uma rede social acadêmica, na qual as relações entre os pesquisadores representam uma parceria na produção de uma publicação (coautoria) - um link. Na análise de redes sociais existe uma linha de pesquisa conhecida como predição de link ou de relacionamentos, que tem como objetivo identificar relacionamentos futuros. Essa tarefa pode favorecer a comunicação entre os usuários e otimizar o processo de produção científica identificando possíveis colaboradores. Este projeto analisou a influência de diferentes atributos encontrados na literatura e filtros de dados para prever relações de coautoria nas redes sociais acadêmicas. Foi abordado dois tipos de problemas na predição de relacionamentos, o problema geral que analisa todos os possíveis relacionamentos de coautoria e o problema de novas coautoria que refere-se aos relacionamentos de coautorias inéditas na rede. Os resultados dos experimentos foram promissores para o problema geral de predição com a combinação de atributos e filtros utilizados. Contudo, para o problema de novas coautorias, devido à sua maior complexidade, os resultados não foram tão bons. Os experimentos apresentados avaliaram diferentes estratégias e analisaram o custo e benefício de cada uma. Conclui-se que para lidar com o problema de predição de coautorias em redes sociais acadêmicas é necessário analisar as vantagens e desvantagens entre as estratégias, encontrando um equilíbrio entre a revocação da classe positiva e a acurácia geral / Nowadays, social networks are gaining prominence in the day-to-day lives. In these networks, different relationships are established between entities that share some characteristic or common goal. A huge amount of information about the Brazilian national scientific production can be found in the Lattes Platform, which is a system used to record the curricula of researchers in Brazil. From this information, it is possible to build an academic social network, where relations between researchers represent a partnership in the production of a publication - a link. In social network analysis there is a research area known as link prediction, which aims to identify future relationships. This task may facilitate communication among researchers and optimize the scientific production process identifying possible collaborators. This project analyzed the influence of different attributes found in the literature and data filters to predict co-authorship relationships in academic social networks. Was approached two types of problems in predicting relationships, the general problem that analyzes all possible co-authoring relationships and the problem of new co-authoring that relates to novel co-authorships relationships in the network. The experimental results were promising to the prediction general problem, combining attributes and using filters. However, for the new co-authorships problem the results were not as good. The experiments evaluated different strategies and analyzed the costs and benefits of each. We concluded that to deal with the co-authorships prediction problem in academic social networking it is necessary to analyze the advantages and disadvantages among the strategies, finding a balance between the recall of the positive class and the overall accuracy

Electronic networks services have become essential tools for the academic community. One of the services provided has been academic electronic bulletin boards (EBBs), and the use of EBBs has increased dramatically during the last decade. One question concerns the possible application of EBBs as a means both for communication and for remote training. A series of experiments were conducted during 1991, 1992, and 1993 with the aim of examining the use of EBBs for these purposes. The first experiment was carried out to investigate whether users experience problems in using EBBs. The next extended this to see how students evaluated EBBs for communication and training purposes. The main focus of the work was BUBL. After this second experiment, modifications were made to the BUBL data and a further experiment was carried out. A different group of students looked at the modified material, and also compared it with US data using different software. The fourth experiment compared the usability of a menu-based interface (dBase III +) and a hypertext interface (HyperCard) from a student's viewpoint. It was followed by an investigation of icons to find out how well different icons could be recognised and the possibility of using them for language-independent instructions. Finally, the characteristics and problems of GULFNET users were examined. The evaluation has demonstrated the general acceptability of EBBs and their likely value for training purposes. This leads to a discussion of how an EBB might best be developed for use in communication and training on GULFNET.

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / The computer networks of higher academic institutions play a significant role in the academic lives of students and staff in terms of offering them an environment for teaching and learning. These institutions have introduced several educational benefits such as the use of digital libraries, cluster computing, and support for distance learning. As a result, the use of networking technologies has improved the ability of students to acquire knowledge, thereby providing a supportive environment for teaching and learning. However, academic networks are constantly being attacked by viruses, worms, and the intent of malicious users to compromise perceived secured systems. Network security threats and cyber-attacks are significant challenges faced by higher academic institutions that may cause a negative impact on systems and Information and Communications Technology (ICT) resources. For example, the infiltration of viruses and worms into academic networks can destroy or corrupt data and by causing excessive network traffic, massive delays may be experienced. This weakens the ability of the institution to function properly, and results in prolonged downtime and the unavailability of Information Technology (IT) services. This research determines challenges faced by higher academic institutions, identifies the type of security measures used at higher academic institutions, and how network security could be addressed and improved to protect against network security threats and attacks. Two research approaches were adopted, namely a survey and an experiment. Survey questionnaires were distributed to IT technical staff at higher academic institutions in Gauteng province to determine the challenges they face in terms of securing their networks. It is crucial that network security takes on a prominent role when managing higher academic institutions‘ networks. The results of the study reveal several challenges such as budget constraints, inadequate security measures, lack of enforcing network security policies, and lack of penetration testing on systems and the network. The results also reveal that the implementation of security measures can and does address network security threats and attacks. It is therefore extremely important for higher academic institutions to implement proper security measures to help mitigate network security threats and attacks. The framework proposed is based on the results from the research study to help mitigate network security threats and attacks at higher academic institutions.