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This paper explores the role of Social Networking Sites (SNS) in e-learning by investigating the attitudes, behaviors, knowledge and views of computing students towards the use of SNS in e-learning. Data was collected from an online survey and interviews, and analyzed to discover the practices, tendencies and the current status of the use of SNS in e-learning as well as how these can be improved.Major factors that facilitate the usage of SNS in e-learning were identified as collaboration, communication, resource sharing, social influence, usefulness and ease of use. Facebook was identified as the most popular SNS. The role of SNS in e-learning is supportive and important.Although the participants were computing students with a high level of IT literacy, and were interested in using SNS for elearning, only a few were frequently using SNS for e-learning. Reasons for this minimal utilization of SNS for e-learning were identified as security and privacy concerns, reliability and currency of content and network issues such as speed of access, real time synchronization and efficiency.We believe that SNS can play a major supporting role in elearning and that the potential for using SNS in e-learning is not fully reached. The situation may be improved by providing increased guidance and training to students. Learning activities using SNS should be planned and organized.Brief guidelines on using SNS in e-learning are also included in this paper. These guidelines may be further refined and adapted for use in other institutions.

Abstract-We propose a web based intelligent student advising system using collaborative filtering, a technique commonly used in recommendation systems assuming that users with similar characteristics and behaviors will have similar preferences. With our advising system, students are sorted into groups and given advice based on their similarities to the groups. If a student is determined to be similar to a group students, a course preferred by that group might be recommended to the student. K-means algorithm has been used to determine the similarity of the students. This is an extremely efficient and simple algorithm for clustering analysis and widely used in data mining. Given a value of K, the algorithm partitions a data set into K clusters.Seven experiments on the whole data set and ten experiments on the training data set and testing data set were conducted. A descriptive analysis was performed on the experiment results. Based on these results, K=7 was identified as the most informative and effective value for the K-means algorithm used in this system. The high performance, merit performance and low performance student groups were identified with the help of the clusters generated by the K-means algorithm. Future work will make use of a two-phase approach using Cobweb to produce a balanced tree with sub-clusters at the leaves as in [11], and then applying K-means to the resulting sub-clusters. Possible improvements for the student model were identified. Limitation of this research is discussed.

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In this paper we deal with discovering a geographic location of a node in the Internet. Knowledge of location if fundamental element for many location based applications and web services. We focus on location finding without any assistance of the node being located -client-independent estimation. We estimate a location using communication latency measurements between nodes in the Internet. The latency measured is converted into a geographic distance which is used to derive a location by the multilateration (triangulation) principle. We analyse the latency-to-distance conversion with a consideration of location underestimation which is a product of multilateration failure. We demonstrate that location underestimations do not appear in experimental conditions. However with a real-world scenario, a number of devices cannot be located due to underestimations. Finally, we propose a modification to reduce the number of underestimations in real-world scenarios.