The fast growth of the Internet proposes and offers several new applications and advanced services that has greatly and quickly innovated the educational environment. The platform of elearning represents an attractive educational field where the acceptance or utilization progresses and more and more people are taking courses or training using this technology. Mainly, e-learning platform exploits Internet infrastructure which became location for illegal events and actions, especially exposed to several kinds of threats or attacks. Furthermore, most of e-learning platforms are designed without taking into account security concerns. In this paper, we present e-learning environment, especially characteristics, development, growth, benefits and challenges. We also discussed and used TRI (Technology Readiness Index) which is considered a widely accepted metric for studying the behavior process behind the utilization of technological products and services. The results of the conducted TRI study demonstrate the need for a security scheme that incorporates students' behaviors and requirements for improving e-learning usage. For this reason, we develop a new security scheme that combines the use of information service management (ISM) and a hybrid algorithm for guaranteeing the needed security requirements. Finally, we demonstrate that our proposal can guarantee the suitable environment which provides students' satisfaction and acceptance as well as e-leaning success.

E-learning system success factors identification is of major interest in higher education. Understanding the role of students’ aspirations factor affecting the success of the e-learning system is a challenge for most educational institutions. The present study aims to analyze the effects of students’ aspirations factors in ensuring the success of the e-learning system through a developed research model extended from the integrated updated Unified Theory of Acceptance and Use of Technology and the DeLone and McLean Information System Success Model . The study participants who made up the model data sample were collected from 379 students engaged in the e-learning system at universities across the Kingdom of Saudi Arabia. Students’ aspirations are resumed in Motivation , Expectation, and Enjoyment factors. The structural equation model was used to analyze the main causes and effects that would guide students towards the use and success of the e-learning system. The study results showed the strong relationship between the students’ aspirations factors (Motivation, Expectation, and Enjoyment ) and the adoption factors ( Intention to Use and Perceived Usefulness ) that lead to increased students’ confidence that e-learning adds value to their educational experience. In addition, results revealed the determining role of the effect of the Enjoyment factor on the benefits expected from the e-learning system process. Therefore, higher education institutions that aspire to benefit the most from the e-learning system should pay close attention to the aspirations of their students and enhance their enjoyment, and then redefine the “e” in e-learning as enjoy rather than simply electronic.

Purpose This paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically. Design/methodology/approach The LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background. Findings Results show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS. Originality/value This empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

PurposeThe objective of this study is to investigate the key determinants affecting the acceptance and utilization of Blackboard as a Computer-Assisted Language Learning (CALL) platform among Saudi university students pursuing English as a foreign language (EFL) courses.Design/methodology/approachUnderstanding how to engage EFL students in their learning requires identifying the factors that influence their acceptance and use of CALL tools, particularly on Blackboard's LMS platform. This study proposes and validates a research framework that predicts students' behavioral intentions and usage of CALL by utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) by Venkatesh et al. (2012). This research model provides insight into the various drivers that impact CALL acceptance via Blackboard LMS. The study's findings demonstrate UTAUT2's superior ability to address the fear of technology adoption and provide valuable insights into the factors that influence technology intention and usage.FindingsThe study's findings indicate that performance expectancy, social influence, effort expectancy and price value significantly affect the attitudes of EFL students toward using CALL. The habit factor was the most robust predictor of behavioral intention and technology use, indicating that CALL usage can become automatic for students and improve their engagement in EFL learning. The study highlights the importance of providing better technical and organizational support to EFL students who want to use CALL more effectively. The theoretical and practical implications of the study's findings are thoroughly discussed.Originality/valueUnderstanding how to engage EFL students in their learning requires identifying the factors that influence their acceptance and use of CALL tools, particularly on Blackboard's LMS platform. This study proposes and validates a research framework that predicts students' behavioral intentions and usage of CALL by utilizing the UTAUT2 by Venkatesh et al. (2012). This research model provides insight into the various drivers that impact CALL acceptance via Blackboard LMS. The study's findings demonstrate UTAUT2's superior ability to address the fear of technology adoption and provide valuable insights into the factors that influence technology intention and usage.

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