Virtuellt lärande på distans : en intervjustudie med finländska gymnasiestuderande.

The aim of this study was to gain a deeper understanding of the learning experiences of upper secondary school students in a virtual learning environment. The focus of the study is younger students aged 16–18. Virtual learning environments are defined as collaborative, interactive and communicative digital environments. The main research question was to distinguish the meaning of learning given by the participants. Did the participants perceive learning potential in the virtual learning environment, and if so, what signifies learning potential? Sub-questions were: What enhances learning? What might inhibit learning in a distance course? How do the participants relate to their role as distant learners? Four upper secondary schools in Finland took part in the study. Thirteen upper secondary students were interviewed after a distance course in social studies. During the analysis, four main categories were identified: responsibility, freedom, time and communication. A constructivist approach to learning was adopted while analysing the interviews, and the categories were understood through cognitive, affective and social dimensions of learning. The implications of the study are that a student-centred pedagogy and a social constructivist course design have the potential to motivate students to interact to learn, while the software, such as Second Life, Google+ and Wikibooks, offers them the possibility to do so. The study introduces an empirically supported concept, virtual learning. Virtual learning assumes an active learner who manages different learning spaces while communicating with people and metacognitively assessing the learning process. At the same time, students get used to the virtual and everchanging nature of information and knowledge.

International Partnering strategy for e-learning provider in emerging economies: Evidence from East Africa learning institutions

International partnership has received growing interest in the literature during the past decades due to globalization, increased technological approaches and rapid changes in competitive environments. The study specifically determines the support provided by international partners on promotion of e-learning in East Africa, assess the motives of partner selection criteria, the determinants of selecting partners, partner models and partner competence of e-learning provider. The study also evaluates obstacles of e-learning partnering strategy in East Africa learning institutions. The research adopts a descriptive survey design. Target population involved East Africa learning institutions with a list of potential institutions generated from the Ministry of Higher Education database. Through a targeted reduction of the initial database, consisting of all learning institutions, both public and private, the study created a target sample base of 200 learning institutions. Structured questionnaires scheduled were used to collect primary data. Study findings showed the approach way East African communities in selecting their e-learning partners depend on international reputation of partners, partner with ability to negotiate with foreign governments, partner with international and local experiences, nationality of foreign partner and partners with local market knowledge.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.