Search

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.

Idiopathic granulomatous mastitis (IGM) is an inflammatory-mediated rare disease that can be linked to rare manifestations. Erythema nodosum (EN) and polyarthritis, seen in a multitude of autoinflammatory and autoimmune diseases, have been rarely linked to IGM. Despite the cause of IGM being unclear, Corynebacterium infections are thought to play a role in the pathophysiology of IGM. Unusually, IGM has a relapsing and remitting course, which also applies to its systemic manifestations. As such, we present a case of IGM in a middle-aged lady who was initially thought to have Corynebacterium-containing unilateral abscesses for which drainage was performed. However, several abscesses devoid of bacterial growth started recurring, and the disease course was complicated by EN and polyarthritis. IGM, EN, and polyarthritis eventually resolved and were managed with symptomatic treatment. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to develop an amalgamated conceptual model of technology acceptance that explains how individual, social, cultural and organizational factors affect the students' acceptance and usage behaviour of the Web-based learning systems. More specifically, the proposed model extends the Technology Acceptance Model (TAM) to include four constructs namely, Quality of work life; Social Norm; Facilitating Conditions and self-efficacy. In addition, Individual differences--namely, age, gender, and experience--are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. A comprehensive understanding of this model will provide valuable insights into the factors that influence the acceptance or resistance of web-based learning system by intended users and offers opportunities for future research in understanding the acceptance of technology. Further, understanding these variables is helpful for instructors to design meaningful educational activities to promote student knowledge construction and make learning more effective and appealing.

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.

In the technology acceptance studies, both the theory of reasoned action and the technology acceptance model have been widely adopted to study the factors that influence users' technology usage intentions. While these frameworks have been mostly tested in Western nations, there has been a little effort to apply these frameworks in non-Western nations. With the globalization of education and technology, there is an urgent demand to know whether TRA and TAM apply in another culture. This study compared TRA, TAM and integrated frameworks that best explained or predicted students' technology usage intention. Structural equation model was employed to perform the data analysis collected from 487 university students. The results showed that there were no differences in predictive strength of behavioral intention among the three models. Thus, the predictive strength of the three models was similar. This study contributed to the ongoing discourses in employing theoretical models to understand undergraduate students' behavioral intention in educational contexts in developing countries. Implications, limitations and future studies were discussed.