This study investigates computer science (CS) students' perceived needs for support in an array of academic and nonacademic areas prior to entering college and relates…
This study investigates computer science (CS) students' perceived needs for support in an array of academic and nonacademic areas prior to entering college and relates these findings to their subsequent performance in the core CS curriculum. This study specifically explored how students' perceived needs vary by gender and residency and how these perceived needs relate to students' academic performance in CS courses.
Data included survey responses and academic performance measures from 718 CS students. Approximately 14 percent of the participants were female students, and 86 percent were male students. Also, 24 percent of students were international, 46 percent out-of-state, and 30 percent were in-state students. To address research questions, multiple regressions and analysis of covariance were conducted. For all analyses, students' ACT scores were used as covariates.
Results show significant main effects for both gender and residency, but interaction is not significant. Female students, on average, selected more perceived needs compared to male students. Also, international students selected more needs compared to domestic students. Also, the number of perceived needs for different categories is unique across students of different residency and gender. Results also indicate that the perceived need for assistance with STEM content is associated with lower CS academic performance. In contrast, perceived needs for professional skills and support services are not related to CS performance. Finally, students' ACT score is a good predictor of their academic performance.
This study provides important contributions to higher education and CS education literature. This is the first study with CS students focusing on their perceived needs. Also, this study includes an almost complete data set (94.6 percent survey completion rate) from CS students.
A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the…
A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated social media and crowdsourcing data mining system for the synchronization of the police and law enforcement agencies for the prevention of criminal activities during and post a large crisis situation.
The paper realized qualitative research in the form of a review of the literature. This review focuses on the necessity of using social media and crowdsourcing data mining techniques in combination with advanced Web technologies for the purpose of providing solutions to problems related to criminal activities caused during and after a crisis. The paper presents the ATHENA crisis management system, which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention.
Conclusions are drawn on the significance of social media and crowdsourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.
The paper shows how the integrated use of social media and data mining algorithms can contribute in the resolution of problems that are developed during and after a large crisis.