Search

A major problem an instructor experiences is the systematic monitoring of students' academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students' academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students' performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.

The dynamic movement of electronic government (e-government) needs a clear pathway on the adoption level of stakeholders within the public sector organization. This research has identified gaps in the movement of the adoption. The respondents comprised 237 public sector managers at the ministry level who conduct egovernment services in the Sultanate of Oman. A questionnaire was designed to tap into the manager's perception of the Internet's Perceived Usefulness, Perceived Credibility of technology, Intention to use the technology, and adoption of the Internet itself. Seven hypothesized relationships were tested in the structural model. An advanced quantitative data analysis using multivariate data analysis was employed. The data were analyzed using structural equation modeling (SEM) to test the causal and mediating effects of latent variables. Based on the research, the hypothesized model fit fails to be supported (p<.05). The findings support the TAM theory extremely well, whereby, all the hypothesized paths were asserted. The generated model found three significant direct paths between Perceived Usefulness, Perceived Credibility, and intention as well as between intention and adoption. [ABSTRACT FROM AUTHOR]

Nowadays, to survive in the highly competitive industries, organizations require adapting to the rapid changes in the industrial environment to deliver the products or services with high quality in accordance with the consumer's expectation. In order to achieve maximum efficiency, organizations endeavour to enhance their asset integrity, reliability, availability, productivity, maintenance practice, and concentration on safety and product quality. It is also vital to ensure the highest maintenance efficiency practice in organizations to achieve the best efficiency practice. Therefore, this paper aims to identify the most influential factors that impact workforce productivity's Wrench Time (WT). Understanding the impacts of these factors on the WT and identifying their causes will provide a clear vision of how the management can allocate the necessary resources to eliminate these impacts. As a methodology, random sampling is used to determine the sample of observations from a given population. This sample is then used in the pilot test that is conducted to test the reliability of the data collection method. The Day In Life Of (DILO) approach is used to collect data as an observation method that involves the supervisor's participation with the technicians throughout the whole working day. A case study based on one of the largest LNG plants in the Middle East is conducted to verify the achieved maintenance efficiency. As a result, workforce productivity has increased to 38.1% by changing the monthly workforce plan, leading to a future roadmap to enhance maintenance efficiency by considering more related factors. [ABSTRACT FROM AUTHOR]