Big Data, Internalization, and Community College Retention
Wauseca Briscoe
Big data analytics are among many economical methods that improve decisionmaking for student retention programs in higher education. Among economic proportions, metrics that enable improved programming for fields of Science, Technology, Engineering, and Mathematics (STEM) expand domestics and international study through traditional, virtual, and work-study programs that broaden student preparedness with resources that support student services beyond the digital divide. Research findings highlight numerous factors contributing to student matriculation, suggesting that representations for lower socioeconomic communities impact the global balance of diverse opportunities (Shahar, 2021). Moreover, classifications for student competencies are levels of environmental safety for social representation and resource services. For example, virtual courses require web-based instruction for adequate independent learning. However, navigating course content is a component of academic retention, and procedures that establish college address inadequate Wi-Fi connections, lower-grade laptops, mobile devices as the primary digital device, and insufficient study environments for assignment completion. Additionally, the objective for student retention programs must be student matriculation to determine methods of career success for a global agency as a; 1) research for socioeconomic issues and student retention, 2) the effectiveness of existing methods traditional and non-traditional classrooms, and 3) determine the impact on learning resources for underprivileged communities to support planning for community college education.
