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Massive Open student’s courses (MOOC) have stimulated the efforts made for improving the learning techniques and enhancing it the spectrum for students learning. Unfortunately, the acceptance of MOOC as a learning instrument re-mained low, which is perceived as an entertainment tool rather than an academic tool, particularly in developing countries. The study evaluated the student’s adap-tation of MOOC as an academic tool. It developed an understanding of the asso-ciated factors which impact the students’ decision towards utilizing MOOC as a learning instrument. It initially investigated the constructs of the native UTAUT, subsequent to which is derived theory from the literature, amplifying the UTAUT theory scope by instigating e-learning factors associated with MOOC, such as at-titude and self-efficacy. Based on the established framework, a survey was con-ducted where 150 MOOCs’ students were recruited. The collected data were sta-tistically analyzed using SPSS. The results showed that acceptance of the MOOCs was substantially affected by its performance expectancy, effort expec-tancy, social influence, self-efficiency, attitude, and facilitating conditions. It also suggested that efforts should be introduced to promote the use of MOOCs among the academic institutes in Saudi Arabia.

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.