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This study aims to employ the supervised machine learning algorithms to examine factors that negatively impacted academic performance among college students on probation (underperforming students). We used the Knowledge Discovery in Databases (KDD) methodology on a sample of N = 6514 college students spanning 11 years (from 2009 to 2019) provided by a major public university in Oman. We used the Information Gain (InfoGain) algorithm to select the most effective features and ensemble methods to compare the accuracy with more robust algorithms, including Logit Boost, Vote, and Bagging. The algorithms were evaluated based on the performance evaluation metrics such as accuracy, precision, recall, F-measure, and ROC curve, and then validated using 10-folds cross-validation. The study revealed that the main identified factors affecting student academic achievement include study duration in the university and previous performance in secondary school. Based on the experimental results, these features were consistently ranked as the top factors that negatively impacted academic performance. The study also indicated that gender, estimated graduation year, cohort, and academic specialization significantly contributed to whether a student was under probation. Domain experts and other students were involved in verifying some of the results. The theoretical and practical implications of this study are discussed.

This research develops a model to investigate the influence of different determinants on student's Academic Performance (AP) in UAE University (UAEU). The model consists of Big Five personality traits (BigF) and other learning-orientation strategies. The importance of this research to the UAE's context stems from the fact that determinants of AP vary from one country to another and hence, UAE students may possess different personal traits and learning strategies. From 179 legible responses, the proposed model showed its success in detecting AP of UAEU students with the BigF attributes being the main contributors to explaining AP. The research showcases interesting insights and implications pertaining to UAEU-student's learning strategies, traits and habits. The implications point to different theoretical and professional contributions and contentions and hence, suggesting different recommendations.

Simpler is better. There are a lot of "needs" in e-Learning, and there's often a limit to the time, talent, and money that can be thrown at them individually. Contemporary pedagogy in technology and engineering disciplines, within the higher education context, champion instructional designs that emphasize peer instruction and rich formative feedback. However, it can be challenging to maintain student engagement outside the traditional classroom environment and ensure that students receive feedback in time to help them with ongoing assignments. The use of virtual learning platforms, such as Blackboard Learn, and web feed syndication, using technology such as Rich Site Summaries (RSS), can help overcome such challenges. However, during an initial pilot at an institution in Lebanon, only 21% of students reported making use of both these facilities. In this study, the Technology Acceptance Model (TAM) was used to guide the development of a scale to be used to investigate antecedents to the use of web feeds. The proposed scale was reviewed by 4 experts and piloted with 235 students. The collected data were analysed using structural equation modeling (SEM) technique based on AMOS methods. The results revealed adequate face, content, and construct validity. However, perceived ease of use was not a significant predictor of attitude towards use. Overall, the proposed model achieves acceptable fit and explains for 38% of its variance of which is lower than that of the original TAM. This suggests that aspects of the model may lack criterion validity in the Lebanese context. Consequently, it may be necessary to extend the scale by capturing additional moderators and predictors, such as cultural values and subjective norms. We concluded that the existence of RSS feeds in education improves significantly the content presented by the instructors to the e-learning user decreasing at the same time the size and access cost.

Really Simple Syndication (RSS) offers a means for university students to receive timely updates from virtual learning environments. However, despite its utility, only 21% of home students surveyed at a university in Lebanon claim to have ever used the technology. To investigate whether national culture could be an influence on intention to use RSS, the survey was extended to British students in the UK. Using the Technology Adoption Model (TAM) as a research framework, 437 students responded to a questionnaire containing four constructs: behavioural intention to use; attitude towards benefit; perceived usefulness; and perceived ease of use. Principle components analysis and structural equation modelling were used to explore the psychometric qualities and utility of TAM in both contexts. The results show that adoption was significantly higher, but also modest, in the British context at 36%. Configural and metric invariance were fully supported, while scalar and factorial invariance were partially supported. Further analysis shows significant differences between perceived usefulness and perceived ease of use across the two contexts studied. Therefore, it is recommended that faculty demonstrate to students how educational RSS feeds can be used effectively to increase awareness and emphasise usefulness in both contexts.

The main objective of this article is to study the factors that affect university students' acceptance of E-learning systems. To achieve this objective, we have proposed a new model that aims to investigate the impact of innovativeness, quality, trust, and knowledge sharing on E-learning acceptance. Data collection has taken place through an online questionnaire survey, which was carried out at The British University in Dubai (BUiD) and University of Fujairah (UOF) in the UAE. There were 251 students participated in this study. Data were analyzed using SmartPLS and SPSS. The Structural Equation Modelling (SEM) has been used to validate the proposed model. The outcomes revealed that knowledge sharing and quality in the universities have a positive influence on E-learning acceptance among the students. Innovativeness and trust were found not to significantly affect the E-learning system acceptance. By identifying the factors that influence the E-learning acceptance, it will be more useful to provide better services for E-learning. Other implications are also presented in the study.

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.

This research examines the learning preferences of students in UAE University (UAEU). The uniqueness of this research emanates from the fact that no prior research examined this area from the UAE's perspective. Thus, this research embarks on the fact that student learning strategies vary from one country to another due to many factors. This research utilizes six learning strategies extended from the literature and attempts to examine their importance on UAEU students using survey research. The selected learning strategies were students' motivation, time-poorness, mastery effort, assessment focus, competitiveness, and listening. This research provided interesting insights and contrasts pertaining to the learning strategies of UAEU students. Implications are discussed highlighting different theoretical as well as professional contributions and contentions and portrayed a path where pending issues could be addressed by future research.

This research investigates the personality characteristics of Information Technology students (CIT) in UAE University (UAEU) and how such features impact their IT learning. To achieve this objective, this research attempts to explain the impact of the Big-5 factors on learning using survey research. Results from 179 respondents suggested that agreeableness, extraversion, openness, and conscientiousness respectively were the most important strategies adopted by CIT students. However, neuroticism scored the lowest. Such results could be attributed to personal as well as to cultural reasons as highlighted in this research. The research highlights theoretical as well as professional contributions and implications. Some of challenges in this research could be addressed by designing programs aiming at enhancing student's learning ability from the perspective of their personal traits.

This study examines the major factors that may hinder or enable the adoption of e-learning systems by university students in developing (Qatar) as well as developed (USA) countries. To this end, we used extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with Trust as an external variable. By means of an online survey, data were collected from 833 university students from a university in Qatar and another from USA. Structural equation modelling was employed as the main method of analysis in this study. The results show that performance expectancy, hedonic motivation, habit and trust are significant predictors of behavioural intention (BI) in both samples. However, contrary to our expectation, the relationship between price value and BI is insignificant. Our results also show that effort expectancy and social influence lead to an increase in students' adoption of e-learning systems in developing countries but not in developed countries. Moreover, facilitating conditions increase e-learning adoption in developed countries which is not the case in developing countries. Overall, the proposed model achieves an acceptable fit and explains its variance for 68% of the Qatari sample and 63% of the USA sample. These results and their implications to both theory and practice are described.

Purpose: This research aims to examine the factors that may hinder or enable the adoption of e-learning systems by university students. Design/methodology/approach: A conceptual framework was developed through extending the unified theory of acceptance and use of technology (performance expectancy, effort expectancy, hedonic motivation, habit, social influence, price value and facilitating conditions) by incorporating two additional factors, namely, trust and self-efficacy. Data were collected from students at two universities in England using a cross-sectional questionnaire survey between January and March 2015. Findings: The results showed that behavioral intention (BI) was significantly influenced by performance expectancy, social influence, habit, hedonic motivation, self-efficacy, effort expectancy and trust, in their order of influencing the strength and explained 70.6 per cent of the variance in behavioral intention. Contrary to expectations, facilitating conditions and price value did not have an influence on behavioral intention. Originality/value: The aforementioned factors are considered critical in explaining technology adoption but, to the best of the authors' knowledge, there has been no study in which all these factors were modeled together. Therefore, this study will contribute to the literature related to social networking adoption by integrating all these variables and the first to be tested in the UK universities.

Despite the prevalence and significance of e-learning in education, there is a dearth of published instruments for educational researchers and practitioners that measure users' acceptance of e-learning. To meet this need, Teo (2010) developed the E-learning Acceptance Measure (ElAM). The main objective of this paper is to validate the ElAM (Teo, 2010) across two cultures, one is from a European country: England, and the other from Asia: Lebanon. A total sample of 461 university students from two private universities in Lebanon (n = 209) and one university in England (n = 252) participated in this study. Using confirmatory factor analyses, our findings revealed that the original 3-factor solution for ElAM (Teo, 2010) was supported and found to be adequate for the British sample, whereas the results revealed a bad fit for the Lebanese sample. Despite the differences, the ElAM was found to possess an acceptable level of internal consistency and item reliability for the pooled sample. Theoretical and practical implications are discussed at the end of the paper.

This paper examines the social, organisational and individual factors that may affect students' acceptance of e-learning systems in higher education in a cross-cultural context. A questionnaire was developed based on an extended technology acceptance model (TAM). A total sample of 1173 university students from two private universities in Lebanon and one university in England participated in this study. After performing the satisfactory reliability and validity checks, the hypothesised model was estimated using structural equation modeling. The findings of this study revealed that perceived usefulness (PU), perceived ease of use (PEOU), social norms (SNs), perceived quality of work life (QWL), computer self-efficacy (SE) and facilitating conditions (FC) are significant determinants of behavioural intentions (BIs) and usage of e-learning system for the Lebanese and British students. QWL, the newly added variable, was found the most important construct in explaining the causal process in the model for both samples. Differences were found between Lebanese and British students with regard to PEOU, SN, QWL, FC, SE and actual usage; however, no differences were detected in terms of PU and BI. Overall, the proposed model achieves acceptable fit and explains for 69% of the British sample and 57% of the Lebanese sample of its variance which is higher than that of the original TAM. Our findings suggest that individual, social and organisational factors are important to consider in explaining students' BI and usage of e-learning environments.

The success of an e-learning intervention depends to a considerable extent on student acceptance and use of the technology. Therefore, it has become imperative for practitioners and policymakers to understand the factors affecting the user acceptance of e-learning systems in order to enhance the students' learning experience. Based on an extended Technology Acceptance Model (TAM), the main aims of this study are to investigate the factors affecting students' behavioral intention to adopt e-learning technology and to explore the moderating effect of age and gender on the relationships among the determinants affecting e-learning acceptance. This study is based on a total sample of 604 students who used a Web-based learning system at Brunel University in England. Confirmatory Factor Analysis (CFA) was used to perform reliability and validity checks, and structural equation modeling (SEM) was used to test the research model. The results indicate that perceived ease of use, perceived usefulness, social norm, and self-efficacy were critical factors for students' behavioral intention to use e-learning, with the effect of perceived usefulness found to have the highest magnitude among the main determinants. We also found that age moderates the effect of PEOU, PU, and SE on BI, and that gender moderates the effect of PEOU and SN on BI. However, surprisingly, no significant moderating effect of age on the relationship between SN and BI was found; results also revealed no moderating of gender on PU or SE and BI. Overall, the proposed model achieves acceptable fit and explains 62% of its variance, which is higher than that explained by the original TAM. Based on these findings, implications to both theory and practice are discussed.

The authors would like to note that one of the co-author’s names was displayed incorrectly. Not Serhani Mohamd, but Mohamed Adel Serhani co-authored this article as correctly shown above. [ABSTRACT FROM AUTHOR]