Abstract-Social networking media are becoming popular among students and teachers in higher education. Researchers have also started to explore the use of social networking media for teaching and learning in higher education. Social networking media have offered new opportunities for sharing, creating and interacting between students and teachers. However, to implement and adopt such technology, there is a need to investigate the factors that influence the acceptance of the students and the teachers using such technologies as a tool for learning and teaching. In this paper, a study based on the Technology Acceptance Model (TAM), which emphasizes on Perceived Ease of Use and Perceived Usefulness together with Behavior Intention to use new technologies, is used to test the factors of using social networking media for e-learning in Libyan higher education. A quantitative research method was employed utilizing survey method. Research data was collected from a sample of teachers and students from four universities in the Libyan higher educational sector. Structural Equation Modeling was carried out to examine the predictive behaviour of the proposed factors of the research model. It was discovered from the study that the Perceived Ease of Use and Perceived Usefulness are important factors for predicting a student's and teachers' behavioral intention to use social networking media for e-learning in Libyan higher education.Index Terms-Libyan higher education, social networking media, perceived ease of use, perceived usefulness, e-learning. I. INTRODUCTIONThe impact of the Internet on the education sector has taken both teachers' and students' attention in recent years. New generations of Web 2.0 and Web 3.0 have added more enthusiasm and excitement for people to spend many hours on internet based applications, especially social networking media [1]. A big portion of the social networking media users are youths who are mostly university students [2]. Many studies have reported that Facebook is the most common social networking tool used where 85-99% of the university students use it for all aspects of life, including for educational purposes [3]. A recent study of about 3000 college students, from USA, indicated that 90% of students utilize Facebook and 37% of them use Twitter to share information [4].Social networking media (e.g Facebook, Linkedin or Twitter) received a lot of attention due to the high take up rate across the world. Social networking media have made communication, collaboration and interaction possible and Manuscript received September 2, 2014; revised November 2, 2014. The authors are with Murdoch University, Australia (e-mail: a.elkaseh@murdoch.edu.au). more efficiently. Consequently, they have been introduced to support educational activities [5]. Social networking media have been able to create a revolution in the communication fields for information and knowledge sharing [5]. This revolution has changed the manner of how people interact and communicate with each other, including how they exchange,...

This study aims to verify the learning effectiveness of a desktop virtual reality (VR)-based learning environment, and to investigate the effects of desktop VR-based learning environment on learners with different spatial abilities. The learning outcome was measured cognitively through academic performance. A quasi pretest-posttest experimental design was employed for this study. A total of 431 high school students from four randomly selected schools participated in this study where they were randomly assigned to either experimental or control groups based on intact classes. Findings indicate a significant difference in the performance achievement between the two groups with students performed better using desktop virtual reality. A possible explanation is that the desktop virtual reality instructional intervention has helped to reduce extraneous cognitive load and engages learners in active processing of instructional material to increase germane cognitive load. A significant interaction effect was found between the learning mode and spatial ability with regard to the performance achievement. Further analysis shows a significant difference in the performance of low spatial ability learners in the experimental and control groups, but no statistically significant difference in the performance for high spatial learners in both groups. The results signify that low spatial ability learners' performance, compared with high spatial ability learners, appeared to be more positively affected by the desktop VRbased learning environment which is supported by the ability-as-compensator hypothesis, and can be explained by the cognitive load theory.

Augmented Reality (AR) is a technology that augments reality with either two or three dimensional computer generated imagery (CGI), objects and/or information, and allows users to interact with them. AR on mobile devices are evolving and offer a great deal of potential in terms of learning and training. This paper discusses the development process of a mobile prototype learning environment that utilises mobile-Augmented Reality (mAR). The prototype is called the Human Anatomy in Mobile Augmented Reality or HuMAR, and the selected learning topic is the anatomy of the human skeletal structure. The main objective of HuMAR is to aid students and it could potentially enhance their learning process. There has been a report stating that there is a decline in retaining and generating long lasting information longer when learning the abovementioned topic. This paper describes the theory, concept, prototype development and results of HuMAR taken from a pilot test. The pilot test used the experimental method with science's students from three different universities. The objectives of the pilot test were to consolidate users' experience from a didactic and technical point of view. Based on the results of the pilot test, it is concluded that students were satisfied with HuMAR in terms of its usability and features; which in turn could have a positive impact in their learning process.

Lee, E.A-L., Wong, K.W. and Fung, C.C. (2010)HowThis is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. HOW DOES DESKTOP VIRTUAL REALITY ENHANCE LEARNING OUTCOMES? A STRUCTURAL EQUATION MODELING APPROACH HOW DOES DESKTOP VIRTUAL REALITY ENHANCE LEARNING OUTCOMES? A STRUCTURAL EQUATION MODELING APPROACH AbstractThis study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.

Abstract.In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic Minority Over-sampling Technique (SMOTE) and Complementary Neural Network (CMTNN) to handle the problem of classifying imbalanced data. In order to demonstrate that the proposed technique can assist classification of imbalanced data, several classification algorithms have been used. They are Artificial Neural Network (ANN), kNearest Neighbor (k-NN) and Support Vector Machine (SVM). The benchmark data sets with various ratios between the minority class and the majority class are obtained from the University of California Irvine (UCI) machine learning repository. The results show that the proposed combination techniques can improve the performance for the class imbalance problem.