An Adoption of the TAM Model to Determine Factors Affecting Students' Acceptance of e-Learning in Institutions of Higher Education in Saudi Arabia

The purpose of this study was to investigate the influence of psychological, social, technical, cultural and institutional factors on the students' acceptance of E-learning in institutions of higher education in Saudi Arabia. Data was collected from 480 students at five universities in Saudi Arabia by using multi stage stratified random sampling. The questionnaire for this study was adapted from Pituch and Lee (2006), Curtis and Payne (2008), and IVgai, Poon and Chan (2007). Several statistical techniques were used including t-tests, one-way ANOVA, bivariate correlation, and multiple regression analyses. The t-test results showed statistically significant differences in students' E-learning acceptance based on their major and internet experience while students' gender, computer and E-learning experience did not indicate any significant differences. The correlation analysis indicated that the relationships between the psychological, social, technological, cultural and institutional factors were significant. The simple linear regression revealed that, technological, social and psychological factors significantly contributed to the students' acceptance of E-learning while the cultural factor did not. The results of the stepwise regression showed that the variables related to the psychological factor all significantly contributed to the students' E-learning acceptance. As for the social factors, only image and self-identity significantly contributed to students' E-learning acceptance. With regards to the technological factor, three variables namely system response, system functionality and system interactivity significantly contributed to students' E-learning acceptance while system performance did not. Finally, all the institutional factor variables significantly contributed to students' E-learning acceptance. Hierarchical regression results indicated that attitude significantly mediated the relationship between the TAM main constructs and the students' Elearning acceptance. Based on the findings, it is suggested that, among others, higher educational institutions should take into consideration the influence of technological, institutional, social and psychological factors in the process of implementing Elearning

An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e-learning: A case study from Saudi Arabian Governmental Universities

E-learning as an effective educational tool has been integrated into many offered courses provided by higher education institutions. Throughout eliminating the barriers of time and distance, students' lifelong learning can be achieved. Due to a broad global attention given to e-Learning, various studies had been conducted by academe, different organizations as well as the government of various nations (Rosenberg, 2001). Saudi Arabian universities are among those universal universities that implement E-learning system. However, recent research indicated that majority of students in Saudi's universities are still unwilling to use online system. Therefore, many factors need to be determined in order to enhance the students' intention to use E-learning tools and participate effectively in their courses using the accessible electronic channels. The current study has extended Technology Acceptance Model (TAM) to investigate the role of Enjoyment, computer anxiety, computer self-efficacy and Internet experience in influencing the students' intention to use E-learning in Saudi's universities. 402 governmental universities' students were participated to test the proposed hypotheses and to determine weather the proposed variables have an effect on the students' intention to use E-learning system. The results of step wise regression indicated that computer anxiety, computer self-efficacy and Enjoyment were significantly influence the students' intention to use E-learning while the Internet experience was insignificantly influence them. Furthermore, the importance of Attitude in mediating the relationship between perceived usefulness, perceived ease of use and the students' behavioral intention was confirmed

An empirical investigation into the influence of image, subjective norm and self-identity on e-learning acceptance in Saudi government universities

Saudi Arabian universities are currently offering many online subjects and courses using different learning management system. While, the researchers claimed that the successful implementation of E-learning system needs more understanding of users' acceptance process and the related variables influencing this process. The current research explores and investigates empirically the extent to which social variables namely Image, Subjective Norm and Self-Identity influence the students' acceptance of E-learning in Saudis' Universities. The proposed variables or elements have examined with the students' acceptance using the Theory of Reasoned Action (TRA) as theoretical base in order to determine whether the proposed variables have an effect on the students' acceptance or not. The students' acceptance was measured by students' intention to use E-learning as independent variable on TRA theory. Questionnaire survey was distributed to five universities in different geographical locations.For hypotheses testing, liner regression results demonstrated that there was a significant relationship between the image, self-identity and Subjective Norm with the students' acceptance. The Attitude was significantly association with the students' acceptance. The paper concluded with some discussion of the findings key implications on both practical and theoretical prospective

Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators

Image segmentation refers to the process of partitioning a digital image into multiple sets of pixels are known as segments. The main goal of image segmentation is to change and simplify the representation of an image into something that is more meaningful and easier to analyze. The manual transactions for segmentation by experts is a difficult phenomena and time consuming process as well as. Most of the images in the process received are lacking of good quality. The main objective of this study is to develop a reliable mechanism to enhance the image quality and extract the abnormal portion through brain MR image accurately. A spatial filter is designed by utilizing the spatial information of the image and further to use collective information to enhance the poor quality of image(s), whereas, k-means clustering and mathematical morphological operations which extract the tumor segment from images. The proposed method is applied on different types of brain MR images for both visual and quantitative evaluations. Experimental results concluded during the practicum showed promising and reliable accuracy to open a thorough research for better future perspective of the technique developed in the article. Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators. Available from: http://www.researchgate.net/publication/265294217_Fully_Automatic_Detections_of_Abnormalities_of_Brain_MR_Images_by_utilizing_Spatial_Information_and_Mathematical_Morphological_Operators [accessed Nov 3, 2015]

Diagnosis system for the detection of abnormal tissues from brain MRI

The brain tumor is widely disseminating disease all over the world and causing the increasing death rates. If the tumor is diagnosed at early stages, the increasing death rate can be decreased to some extent. Manual segmentation of brain MR images by experts is very expensive, non-repeatable and time consuming task. The computer-aided diagnosis system assists experts to take the opinion to diagnose the disease severity. The diagnosis process can be affected if the images are low contrast or poor quality and wrong diagnoses chances become high. The objective of this paper is to establish an automatic, accurate, fast and reliable diagnosis system which could be able to diagnose the brain tumor and also extract the region of the brain tumor from brain MR images. The median filter is used for enhancing the poor quality image, fuzzy c-means clustering technique for segmentation of images and mathematical morphological operations are performed to extract the abnormal portion from images. The proposed technique is applied on different brain MR images for both visual evaluations and quantitative. Experimental results of the proposed method showed, the proposed approach provides a fast, effective and promising method for the brain tumor extraction from MR images with high accuracy

Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators

Image segmentation refers to the process of partitioning a digital image into multiple sets of pixels are known as segments. The main goal of image segmentation is to change and simplify the representation of an image into something that is more meaningful and easier to analyze. The manual transactions for segmentation by experts is a difficult phenomena and time consuming process as well as. Most of the images in the process received are lacking of good quality. The main objective of this study is to develop a reliable mechanism to enhance the image quality and extract the abnormal portion through brain MR image accurately. A spatial filter is designed by utilizing the spatial information of the image and further to use collective information to enhance the poor quality of image(s), whereas, k-means clustering and mathematical morphological operations which extract the tumor segment from images. The proposed method is applied on different types of brain MR images for both visual and quantitative evaluations. Experimental results concluded during the practicum showed promising and reliable accuracy to open a thorough research for better future perspective of the technique developed in the article

Enhancement of Magnetic Resonance Images Using Soft Computing Based Segmentation

Segmentation is the process of extracting points, lines or regions, which are then used as inputs for complementary tasks such as registration, measurement, movement analysis, visualization , etc in MRI. The noise in MR images degrades the image quality and also affect on the segmentation process which can lead to wrong diagnosis. The main aim of this study is to suggest a system to enhance the quality of the human brain MRI. In the proposed system, median filter is used for image enhancement of brain MRI and fuzzy c-means for segmentation purpose. The proposed method is completely automatic that is there is no user involvement in the proposed system. The system is tested on different kinds of brain MR images and proved robust against noise as well as segments the images fast with improvements

Automated segmentation of brain MR images by combining Contourlet Transform and K-means Clustering techniques

Segmentation is usually conceived as a compulsory phase for the analysis and classification to the field of medical imaging. The aim of the paper is to find a means for the segmentation of brain from MR images by technique of combining Contourlet Transform and K-Means Clustering in an automatic way. De-noising is always an exigent problem in magnetic resonance imaging and significant for clinical diagnosis and computerized analysis such as tissue classification and segmentation. In this paper Contourlet transform has been used for noise removal and enhancement for the image superiority. The proposed technique is exclusively based upon the information enclosed within the image. There is no need for human interventions and extra information about the system. This technique has been tested on different types of MR images, and conclusion had been concluded