The Role of Decision-Driven Data Collection On Northwest Ohio Local Education Agencies\u27 Intervention For First-Time-In-College Students\u27 Post-Secondary Outcomes: A Quasi-Experimental Evaluation of the PK-16 Pathways of Promise (P3) Project

Research shows that the variance in lifetime earnings of Americans can often be forecast by their level of education. Americans with a bachelor’s degree are more likely to live an economically sound life, as their lifetime earnings total US$1 million more than high school graduates (Blagg & Blom, 2018). However, earning a degree in higher education can be challenging for students attending college for the first time. Studies indicate that a substantial number of first-time-in-college (FTIC) students are underprepared to meet the demands of a college education (Carnevale, Smith, & Strohl, 2013; Conley, 2016). This issue is significant, as projections reflect a shortage of 16 to 23 million college-educated adults by 2025 (Carnevale & Rose, 2011). The purpose of the study was to assess the effects of the PK-16 Pathways of Promise (P³) Project—a high school intervention program—on the post-secondary outcomes of full-time, FTIC students. In total, 1,574 full-time, FTIC students from 20 local education agencies (LEAs) in institutes of higher education (IHEs) in Northwest Ohio were compared for significant differences on several variables, including grade point average (GPA), proportion of credits lost in early-level courses, cumulative number of credit-bearing hours earned by the end of the academic year, and persistence and retention rates. The quasi-experimental research design included an intervention group and a comparison group. Students in both groups attended one of the three IHEs in the study. However, the intervention group resided within a 20- to 25-mile radius of the IHEs in the study, whereas students in the comparison group resided in different regional areas within Ohio. Based on their home districts’ geographical locations, students in the comparison group were assumed to be more likely to attend one of the IHEs as a residential student. Controlling for sex, ethnicity, high school GPA, and school typology, the analysis used multilevel modeling (MLM). MLM is an extension of regression analysis. However, while multiple regression assumes the data are independent, MLM assumes the data are not independent of one another, thereby addressing the inherent nature of clustering in educational data. Overall, there were statistically significant differences between the intervention group and comparison group when assessing the cumulative number of credit-bearing hours earned by the end of the academic year, and persistence and retention rates, after controlling for sex, ethnicity, high school GPA, and school typology. Students in the comparison group were significantly more likely to accumulate more credit-bearing hours by the end of their first academic year than were students in the intervention group. However, students in the intervention group were significantly more likely to persist from first-semester enrollment to second-semester enrollment and significantly more likely to be retained by their chosen IHE than were students in the comparison group. Although there were statistically significant differences between the two groups in the study, the differences in post-secondary achievement between the two groups—represented by the coefficient of the intervention variable and effect sizes—were minimal. A deeper examination of the results suggests that geographical location, course rigor, and a sense of belonging might offer possible explanations for the group differences