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While the COVID-19 pandemic necessitated the short-term use of online courses, colleges’ experiences with COVID-era online course delivery may also affect the way that they offer and approach online courses going forward. We draw on interviews with 35 distance education leaders from the California Community Colleges system to provide insights into how the use of online education may change in the system going forward. Leaders predicted that post-pandemic, colleges would increase their online course offerings, and that many instructional innovations to online courses from the pandemic—such as the use of synchronous courses—would persist. They hoped that a more prominent position for online education within the system would be matched by more resources to provide supports for online learning.
Despite recent evidence on the benefits of same-race instructor matching in K-12 and higher education, research has yet to document the incidence of same-race matching in the postsecondary sector. That is, how likely are racially minoritized college students to ever experience an instructor of the same race/ethnicity? Using administrative data from Texas on the universe of community college students, we document the rate of same-race matching overall and across racial groups, the courses in which students are more or less likely to match, the types of instructors students most commonly match to, and descriptive differences in course outcomes across matched and unmatched courses. Understanding each of these measures is critical to conceptualize the mechanisms and outcomes of same-race matching and to drive policy action concerning the diversity of the professoriate.
We investigate whether and how Achieve Atlanta’s college scholarship and associated services impact college enrollment, persistence, and graduation among Atlanta Public School graduates experiencing low household income. Qualifying for the scholarship of up to $5,000/year does not meaningfully change college enrollment among those near the high school GPA eligibility thresholds. However, scholarship receipt does have large and statistically significant effects on early college persistence (i.e., 14%) that continue through BA degree completion within four years (22%). We discuss how the criteria of place-based programs that support economically disadvantaged students may influence results for different types of students.
The role of racial diversity at college campuses has been debated for over a half a century with limited quasi-experimental evidence from classrooms. To fill this void, I estimate the extent that classmate racial compositions affect Hispanic and African-American students at a large and over-subscribed California community college where they are minorities. I find that when minority students are exposed to a greater share of same race classmates, they are more likely to complete the class with a pass and are more likely to enroll in a same subject course the subsequent term. The findings are robust to first-time students with the lowest registration priority vs. all students and different combinations of fixed effects (e.g., student, class, and instructor race).
We study the importance of job-related and non-job-related factors in students’ college major choices. Using a staggered intervention that allows us to provide students information about many different aspects of majors and to compare the magnitudes of the effects of each piece of information, we show that major choices depend on a wide set of factors. While students do not change their choices when given information about earnings, they do update their choices when told about other aspects of majors. The non-job-related factors, such as a major’s course difficulty and gender composition, are important to students but not well-known to them. We also find that male and female students value different major characteristics in different ways. Lower-ability females flee from majors that they learn are more difficult than they had believed, while other students do not. On the other hand, male students are averse to being taught by female faculty, while female students are not. Overall, our results show that a variety of factors are important for students’ major choices and that different factors matter for male and female students.
Increasing numbers of students require internet access to pursue their undergraduate degrees, yet broadband access remains inequitable across student populations. Furthermore, surveys that currently show differences in access by student demographics or location typically do so at high levels of aggregation, thereby obscuring important variation between subpopulations within larger groups. Through the dual lenses of quantitative intersectionality and critical race spatial analysis, we use Bayesian multilevel regression and census microdata to model variation in broadband access among undergraduate populations at deeper interactions of identity. We find substantive heterogeneity in student broadband access by gender, race, and place, including between typically aggregated subpopulations. Our findings speak to inequities in students’ geographies of opportunity and suggest a range of policy prescriptions at both the institutional and federal level.
AB705 is a landmark higher education policy that has changed approaches to developmental/remedial education in the California Community College system. We study one district that implemented reforms by placing most students in transfer-level math/English courses and encouraging enrollment in support courses based on multiple measures of academic preparation (e.g., GPA). We use regression discontinuity designs to examine the impact of these new placement procedures, finding benefits to English support course recommendations for low GPA students, but no evidence of benefits or penalties for math. We use inverse probability weighted regression adjustment to explore the relationship between support course enrollment and subsequent outcomes. While enrollment in concurrent support courses appeared beneficial, enrollment in developmental courses was associated with poorer outcomes.
College success requires students to engage with their institution both academically and administratively. Missteps with required processes can threaten students’ ability to persist. We experimentally assessed the effectiveness of an artificially intelligent text-based chatbot to provide proactive outreach and support to college students to navigate administrative processes and use campus resources. In both the two-year and four-year college context, outreach was most effective when focused on administrative processes which were acute, time-sensitive, and for which outreach could be targeted to those for whom it was relevant. We draw lessons regarding the effective use of nudge-type efforts to support college success.
Given the simultaneous rise in time-to-graduation and college GPA, it may be that students reduce their course load to improve their performance. Yet, evidence to date only shows increased course loads increase GPA. We provide a mathematical model showing many unobservable factors -- beyond student ability -- can generate a positive relationship between course load and GPA unless researchers control student schedules. West Point regularly implements the ideal experiment by randomly modifying student schedules with additional training courses. Using 19 years of administrative data, we provide the first causal evidence that taking more courses reduces GPA and increases course failure rates, sometimes substantially.
Data science applications are increasingly entwined in students’ educational experiences. One prominent application of data science in education is to predict students’ risk of failing a course in or dropping out from college. There is growing interest among higher education researchers and administrators in whether learning management system (LMS) data, which capture very detailed information on students’ engagement in and performance on course activities, can improve model performance. We systematically evaluate whether incorporating LMS data into course performance prediction models improves model performance. We conduct this analysis within an entire state community college system. Among students with prior academic history in college, administrative data-only models substantially outperform LMS data-only models and are quite accurate at predicting whether students will struggle in a course. Among first-time students, LMS data-only models outperform administrative data-only models. We achieve the highest performance for first-time students with models that include data from both sources. We also show that models achieve similar performance with a small and judiciously selected set of predictors; models trained on system-wide data achieve similar performance as models trained on individual courses.