This study seeks to explore the factors that influence students’ usage behaviour of e-learning systems. Based on the strong theoretical foundation of the TAM, UTAM and using structural equation modeling (SEM) via AMOS 20.0, this research paper examines the impact of performance expectancy, effort expectancy, hedonic motivation, habit, social influence, and trust on student’s behavioural intention, which is later examined along with facilitating conditions on student’s usage behaviour of e-learning systems. Data was collected from students at two universities in Beirut (capital of Lebanon) using a cross-sectional questionnaire survey between January and March 2015. The results revealed direct positive effect of performance expectancy, hedonic motivation, habit, and trust on student’s behavioural intention to use e-learning explaining around 71% of overall behavioural intention. Meanwhile, behavioural intention and facilitating conditions accounted for 40% with strong positive effects on student’s usage behviour of e-learning systems. However, both effort expectancy and social influence did not impact student’s behavioural intention.

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Modeling Factors Affecting Student’s Usage Behaviour of E-Learning Systems in Lebanon

Nowadays, social networks incessantly influence the lives of young people. Apart from entertainment and informational purposes, social networks have penetrated many fields of educational practices and processes. This review tries to highlight the use of social networks in higher education, as well as points out some factors involved. Moreover, through a literature review of related articles, we aim at providing insights into social network influences with regard to (a) the learning processes (support, educational processes, communication and collaboration enhancement, academic performance) from the side of students and educators; (b) the users’ personality profile and learning style; (c) the social networks as online learning platforms (LMS—learning management system); and (d) their use in higher education. The conclusions reveal positive impacts in all of the above dimensions, thus indicating that the wider future use of online social networks (OSNs) in higher education is quite promising. However, teachers and higher education institutions have not yet been highly activated towards faster online social networks’ (OSN) exploitation in their activities.

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Social Media Use in Higher Education: A Review

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

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Academic performance and behavioral patterns

The aim of this research is to explore the role of Knowledge Management (KM) infrastructure (organizational culture, organizational structure and information technology) in enhancing job satisfaction at Aqaba five star hotels located in Jordan A total of 216 questionnaires containing 33 items were used to collect information from the respondents Multiple regression analysis was conducted to test the research hypotheses Results of the current study revealed that there is a significant positive impact of KM infrastructure on job satisfaction and these results are in line with the results of the previous studies mentioned in the literature, and also revealed that the dimensions of KM infrastructure that has the highest effect on job satisfaction are organizational culture followed by information technology; whereas the variable of organizational structure has not significant impact on it Results of T-test showed that there is no significant difference in the impact of KM infrastructure towards job satisfaction in favor of gender In addition, results of ANOVA test found that there are significant differences in the impact of KM infrastructure on job satisfaction that can be attributed to age, educational level, and personal income

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The Role of Knowledge Management Infrastructure in Enhancing Job Satisfaction at Aqaba Five Star Hotels in Jordan

Nowadays people use social media in order to create, share or exchange information, pictures or videos in virtual communities and networks. Furthermore, followers can ‘like’, ‘share’ and ‘comment’ on other people’s content or posts. Obviously, social media sites have an influence on the decision-making process, especially in the tourism sector, which has economic benefits for the country; people utilize social media channels in tourism promotional activities in order to encourage and promote domestic tourism. Hence, this Study aims to investigate how to use social media technology in order to encourage and improve domestic tourism in Oman. Data were collected using a self-administrated questionnaire from individuals who are currently using social media sites and have an interest in domestic tourism in Oman. There are three main findings: 1) there are a large number of people who use social media technology to obtain information about different attractive places they wish to visit in Oman, 2) the majority of the respondents claim that negative experiences posted on social media about a particular destination influence their travel decisions, and 3) many of the respondents indicate that the Ministry of Tourism in Oman should utilize more social media tools such as Instagram, Twitter, etc. in order to encourage domestic tourism. This research has implications not only to the Ministry of Tourism to promote domestic tourism in Oman, but also to Omani people and tourists from other countries who visit or like to visit Oman.

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Utilizing Social Media to Encourage Domestic Tourism in Oman

Social Networks Sites (SNSs) are dominating all internet users’ generations, especially the students’ communities. Consequently, academic institutions are increasingly using SNSs which leads to emerge a crucial question regarding the impact of SNSs on students’ academic performance. This research investigates how and to what degree the use of SNSs affects the students’ academic performance. The current research’s data was conducted by using drop and collect surveys on a large population from the University of Jordan. 366 undergraduate students answered the survey from different faculties at the university. In order to study the impact of SNSs on student’s academic performance, the research hypotheses was tested by using descriptive analysis, T-test and ANOVA. Research results showed that there was a significant impact of SNS on the student’s academic performance. Also, there was a significant impact of SNS use per week on the student’s academic performance, whereas no differences found in the impact of use of SNSs on academic performance due to age, academic achievement, and use per day to most used sites. The findings of this research can be used to suggest future strategies in enhancing student’s awareness in efficient time management and better multitasking that can lead to improving study activities and academic achievements.

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The Impact of Social Media Networks Websites Usage on Students’ Academic Performance

The vulnerability of digital audio signals for different types of risks requires an imperceptible and robust digital audio watermarking scheme. In this research, we propose creating one such imperceptible and robust hybrid watermarking scheme based on a discrete wavelet transform (DWT) and Schur decomposition hybrid method. The proposed scheme embeds the foreground bits of the watermarking image into the least significant bit of the diagonal coefficients of the triangular matrix S generated from Schur decomposition. Schur decomposition is applied on the second sub-band HL2 generated from applying a second-level 2D-Haar DWT on the first channel of the original audio signal. We analyze the proposed digital audio watermarking scheme’s performance in terms of signal to noise ratio (SNR), objective difference grades (ODG), and subjective difference grades (SDG) that resulting 81.43, 4.78 and 0.184, respectively. The resulting of payload capacity, NC, and BER are as high as 319.29 bps, 0.9911, and 0.0135, respectively. Experimental results confirm that the proposed scheme is inaudible and robust against common types of attacks such as Gaussian noise, re-quantization, re-sampling, low-pass filter, high-pass filter, echo, MP3 compression, and cropping. In comparison with state-of-the-art audio watermarking schemes, the proposed scheme’s performance is superior in term of imperceptibility, robustness, and data payload size.

A robust digital audio watermarking scheme based on DWT and Schur decomposition

The textual pieces in scene images might often provide vital semantic data for visual content understanding, indexing and analysis; as a result, text extraction had become a significant research area in image processing and computer vision. In this paper, we propose a new hybrid multilevel algorithm to extract text in various scene images. The algorithm converts the Red – Green –Blue (RGB) image into grayscale for color reduction. Next, it applies edge detection and mathematical morphological operations to extract edges in the image preprocessing phase. The resultant binary image passes through three subsequent levels in a multi layer behavior. Connected components labeling and text candidates' selection take place in each level through different criteria analysis. We used the structural features of connected components as basis criteria for selecting candidate texts, those features include: area, width, length and condense intensity mean of connected components. Afterwards, Horizontal projection profile analysis is used to further refine the candidate text areas and to eliminate non-text regions. The proposed algorithm is evaluated on a set of fifty images chosen from a well known text locating test dataset: KAIST. Extensive experiments show high robustness under different environments such as indoor, outdoor, shadow, night and light, and for different text properties such as various font size, style and complexities of backgrounds and textures. The algorithm effectively extracts textual contents from scenes images with high average of Precision, Recall, and F-Score which are 90.1, 99, and 94.3%, respectively. 

	 

	Key words: Multilevel text extraction, hybrid text extraction, edge detection, connected components, text candidates, morphological operations, horizontal projection profile.

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A hybrid multilevel text extraction algorithm in scene images

This study examined impact of a social media networks course on student use of SNSs performance. Moreover, it examined the associations among course design, course materials, learning experiences and a social media networks course. Survey instrument is used to examine the relationships in the proposed model. A total of 380 questionnaires have been collected from students at the University of Jordan who studied the social media networks course. A structural equation modelling approach based on AMOS 20.0 statistical software is used to study the causal relationships and test the hypotheses between the observed and latent constructs in the proposed research model. The analysis results revealed that course materials and learning experiences directly, positively and significantly impacted the social media networks course, which in turn had a significant impact on students’ use social networks sites performance. Course design, however, did not impact the social media networks course. Our findings have important implications as we demonstrated the validity of the joint two different models and provide information about impact of studying social media networks course on students’ academic performance.

Tahani Khatib

Papers

The Impact of Social Media Networks Websites Usage on Students’ Academic Performance

A robust digital audio watermarking scheme based on DWT and Schur decomposition

A hybrid multilevel text extraction algorithm in scene images

Social media networks and pedagogy at the University of Jordan