This study aims at predicting undergraduate students' performance in the Virtual Learning Environment (VLE) based on four time periods of the examined online course. This is to provide an early and continuous prediction of students' academic achievement. This research depends on data from one of the scientific courses at the Open University (OU) in Britain, which offers its lectures using VLE. The data investigated consists of 1938 students in which the influence of demographic and behavioral variables was explored first. Then, three features were generated to improve the prediction accuracy as well as examining the effect of learners' engagement on their academic performance. Accordingly, a comparison was made between the prediction accuracy of integrating the proposed features with the behavioral and demographic features and the use of the original features only. The findings suggest that some of the demographic variables and all behavioral features had a significant impact on students' performance. However, the accuracy was highly improved after using the new generated features. It was found that the level of the financial and service instability, level of participation in the course, assessment grades, the total number of clicks, the interaction with different course activities, and students' engagement were significant predictors of academic achievement.

The author suggests using computer technology and data mining in intelligent engineering management to increase the management capacity of engineering projects and decrease the consumption of construction expenses. The emphasis is mostly on theoretical and analytical approaches to problems of practical concern for data mining, perhaps in combination with other conventional tools, and the associated applications to engineers and managers of various industrial sectors. This has immediate benefits for both academic and applied data mining researchers as well as research students. The author developed a BIM project management system based on Browser/Server architecture and Client/Server architecture in conjunction with BIM technology and the BIM 4D model. To execute complete project management of business management, real-time control, and decision support, the system must establish and use its business logic and data exchange link. Each module in the 4D construction dynamic management subsystem recognizes dynamic management of the construction process, and the working duration of each module is determined by calculating by using the unified engineering decomposition principle, rationalizing the relevant data through time parameters, recording the acquired management data and storing it in the BIM database, and connecting the BIM database bi-directionally through the web server through the system integration. The results showed that when the time is 20[Formula: see text]min, the throughput of the system is higher than [Formula: see text][Formula: see text]kb/s. The system can effectively improve the project management ability, reduce the construction cost and construction period of the project, and the system responds quickly.

Predicting students' success in virtual learning environments (VLEs) can help educational institutions improve their online services and provide efficient online learning content. However, this cannot be achieved without identifying the possible effective features that have a high influence on students' performance. This research aims at providing an early prediction approach to learners' achievement on VLEs. A new feature selection method called a Developed Sequential Feature Selection (D-SFS) was proposed to identify the most effective features that could highly enhance prediction accuracy. The findings suggest that the D-SFS method outperforms the original Sequential Forward Selection (SFS) approach. The prediction accuracy using the SFS method was 92.466% with seventeen features, whereas the proposed approach successfully predicted 92.518% of students' performance using seven features only. Such outcomes highlight the importance of implementing a feature selection method to enhance prediction accuracy, decrease the number of features, and reduce the model's time and execution complexity.