Examining Instructional Spending in Technical Community Colleges for Student Goal Attainment
Faculty Advisor Name
Yoon, Nara
Department
School of Strategic Leadership Studies
Description
Empirical research studies provide a multifaceted understanding of the low graduation rates among community college students. For example, some findings present that the inherent socioeconomic characteristics of community college students are linked with lower graduation rates, such as race, ethnicity, parental education background, gender, college preparedness, financial independence, and household economic status. Other findings suggest that different community college environments and specific local community contexts also affect students’ goal attainment, such as geographic locations and the size of community colleges. Furthermore, research studies posit that neither individual students nor community colleges can solely tackle this complex challenge on their own.
Extant empirical research studies often treat the subcategories of community colleges, such as technical, tribal, and historically black community colleges, as control variables. Thus, empirical research studies focusing on the interaction of specific types of community college and student goal attainment are limited. This paper aims to examine instructional spending, such as academic support for FTE undergraduates, student services per FTE undergraduates, and administrative expenditures per FTE undergraduates in technical community colleges for student goal attainment. Understanding the factors influencing community college student goal attainment is important because AI technology is now leading the impactful transformation in the post-modern industrial production processes. Investing in the training of future workforces equipped with advanced technical skills and capabilities will be critical not only for the sustainable development of local and national businesses but also for maintaining competitive workforces on the global stage. Moreover, along with the increasing demands of localized industry production to booming local businesses, addressing the vexing challenges of unemployment and community thriving, technical community colleges have been strategically positioned in a prioritized role.
Given this, this research paper will employ longitudinal multilevel modeling to examine the central research question of how spending on instruction influences student goal attainment in technical community colleges. Data will be collected from the Integrated Post-Secondary Data System – National Center for Education Statistics.
https://nces.ed.gov/collegenavigator/?s=all&ct=1&ic=2&id=180203
Independent variables:
Community College_ID (Community college identifier -Factor)
Technical community College_ID (Community college identity -binary) 0=nontechnical, 1=technical. -Level -2 predictor
Spending on * Instruction (L1 predictor) (variable centered *time) continuous – Level-1 predictor
Dependent variable
Community college student graduation rate (Outcome) – Continuous (2015-2019)
Model:
Intercept-only model
Random-coefficient unconditional growth modeling – adding level-1 predictor (spending)
Random-coefficient conditional growth modeling -adding level-2 predictor (technical community colleges)
Examining Instructional Spending in Technical Community Colleges for Student Goal Attainment
Empirical research studies provide a multifaceted understanding of the low graduation rates among community college students. For example, some findings present that the inherent socioeconomic characteristics of community college students are linked with lower graduation rates, such as race, ethnicity, parental education background, gender, college preparedness, financial independence, and household economic status. Other findings suggest that different community college environments and specific local community contexts also affect students’ goal attainment, such as geographic locations and the size of community colleges. Furthermore, research studies posit that neither individual students nor community colleges can solely tackle this complex challenge on their own.
Extant empirical research studies often treat the subcategories of community colleges, such as technical, tribal, and historically black community colleges, as control variables. Thus, empirical research studies focusing on the interaction of specific types of community college and student goal attainment are limited. This paper aims to examine instructional spending, such as academic support for FTE undergraduates, student services per FTE undergraduates, and administrative expenditures per FTE undergraduates in technical community colleges for student goal attainment. Understanding the factors influencing community college student goal attainment is important because AI technology is now leading the impactful transformation in the post-modern industrial production processes. Investing in the training of future workforces equipped with advanced technical skills and capabilities will be critical not only for the sustainable development of local and national businesses but also for maintaining competitive workforces on the global stage. Moreover, along with the increasing demands of localized industry production to booming local businesses, addressing the vexing challenges of unemployment and community thriving, technical community colleges have been strategically positioned in a prioritized role.
Given this, this research paper will employ longitudinal multilevel modeling to examine the central research question of how spending on instruction influences student goal attainment in technical community colleges. Data will be collected from the Integrated Post-Secondary Data System – National Center for Education Statistics.
https://nces.ed.gov/collegenavigator/?s=all&ct=1&ic=2&id=180203
Independent variables:
Community College_ID (Community college identifier -Factor)
Technical community College_ID (Community college identity -binary) 0=nontechnical, 1=technical. -Level -2 predictor
Spending on * Instruction (L1 predictor) (variable centered *time) continuous – Level-1 predictor
Dependent variable
Community college student graduation rate (Outcome) – Continuous (2015-2019)
Model:
Intercept-only model
Random-coefficient unconditional growth modeling – adding level-1 predictor (spending)
Random-coefficient conditional growth modeling -adding level-2 predictor (technical community colleges)
