Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies.…

In the program-driven information age, programming education is concerned by the global education system, which makes the cultivation of children's programming ability become the focus of attention. However, there is no clear definition of programming ability and teaching model. Through the snowball method, 86 studies from 1980 to 2020 were…

The underrepresentation of women in Engineering, Technology, and Computing (ETC) education, with enrollments in leading global institutions falling below 30%, is a persistent challenge; however, emerging data suggests the efficacy of structured interventions. Analyses of contemporary data demonstrate that a confluence of institutional,…

Block-based visual programming tools, such as Scratch, Alice, and MIT App Inventor, provide an intuitive and easy-to-use editing interface through which to promote programming learning for novice students of various ages. However, very little attention has been paid to investigating these tools' overall effects on students' academic achievement…

More studies are concentrating on using virtualization-based labs to facilitate computer or network learning concepts. Some benefits are lower hardware costs and greater flexibility in reconfiguring computer and network environments. However, few studies have investigated effective mechanisms for using virtualization fully for collaboration.…

China will suffer into the future from a shortage of computer professionals. With 75% of her population engaged in agriculture, she has only about 100,000 computer professionals, which is less than can be found within a healthy radius of Stanford University. There are 97 "key universities," but only 10 or 15 stand at the top as the…

Critically analyzes recent research studies that examine background variables, cognitive style, and psychological type as influences on performance in college-level computer science courses. Implications for matching curriculum and instructional strategies with student characteristics are presented, recommendations for further research are made,…

Based on literature and student input, six major concerns have been noted for student programming progress for the academic class and work environment. The areas of concern are module driver programming, program documentation, output design, data design, data validation; and reusable code. Each area has been analyzed and examined in the teaching…