Despite rising enrollment, CS Education struggles with training adequate educators, leading to increased teaching loads. Open-source teaching materials alleviate this by streamlining course preparation. Yet, there is a scarcity of free, open curricula that offer a contemporary coding experience while covering CS fundamentals. To address this gap, we introduce the CS1 Python Bakery curriculum with a "Batteries Included" approach, aiming to furnish instructors with comprehensive teaching resources. This curriculum refines an earlier open-source CS1 with detailed lesson plans, slides, rubrics, reference answers, student answers, and more. We present the learning content in a cross-platform, autograded textbook format and embrace modern Python features such as Dataclasses and static types. We deployed the curriculum in multiple university CS1 courses and collected data on the tradeoffs of our approach. This paper offers a thorough self-assessment based on student learning outcomes, code snapshot analyses, and reflection via the TEC Rubric for curriculum evaluation. Although we improved teacher accessibility, the change in student learning outcomes was unexpectedly minimal. Recognizing room for advancement, we conclude with recommendations for our next iteration to emphasize Equity, Community, and Identity.

Large-scale administrative data is a common input in early warning systems for college dropout in higher education. Still, the terminology and methodology vary significantly across existing studies, and the implications of different modeling decisions are not fully understood. This study provides a systematic evaluation of contributing factors and predictive performance of machine learning models over time and across different student groups. Drawing on twelve years of administrative data at a large public university in the US, we find that dropout prediction at the end of the second year has a 20% higher AUC than at the time of enrollment in a Random Forest model. Also, most predictive factors at the time of enrollment, including demographics and high school performance, are quickly superseded in predictive importance by college performance and in later stages by enrollment behavior. Regarding variability across student groups, college GPA has more predictive value for students from traditionally disadvantaged backgrounds than their peers. These results can help researchers and administrators understand the comparative value of different data sources when building early warning systems and optimizing decisions under specific policy goals.

Engineering transfer students experience diverse pathways and unique challenges on their way to earning a degree. Some of these challenges include phenomena like transfer shock and fewer opportunities to build community and receive support. In this work in progress, we explore differences in learning strategies between engineering transfer students and non transfer students with particular focus on transfer students who are part of an NSF-funded S-STEM program. The S-STEM program supports low income engineering transfer students from diverse backgrounds through co-curriculum cohort activities and peer and faculty mentoring with the goal of reducing the negative impact of transfer shock and improving their academic success and persistence. We analyze self-reported quantitative survey data from students in three upper division mechanical engineering courses, comparing the learning strategies of transfer students, S-STEM scholars, and non transfer students. Generally, non transfer students report better learning strategies than transfer students, and S-STEM scholars report better strategies in some areas of peer learning and effort regulation than other transfer students.

Although online courses can provide students with a high-quality and flexible learning experience, one of the caveats is that they require high levels of self-regulation. This added hurdle may have negative consequences for first-generation college students. In order to better understand and support students’ self-regulated learning, we examined a fully online Chemistry course with high enrollment (N = 312) and a high percentage of first-generation college students (65.70%). Using students’ lecture video clickstream data, we created two indicators of self-regulated learning: lecture video completion and time management. Performing a k-means clustering on these indicators uncovered four distinct self-regulated learning patterns: (1) Early Planning, (2) Planning, (3) Procrastination, and (4) Low Engagement. Early Planning behaviors were especially important for course success—they consistently predicted higher final course grades, even after controlling for important demographic variables. Interestingly, first-generation college students classified as Early Planners achieved at similar levels as their non-first-generation peers, but first-generation students in the Low Engagement group had the lowest average grades among students. Overall, our results show that self-regulation may be an important skill for determining first-generation students’ STEM achievement, and targeting these skills may serve as a useful way to support their specific learning needs.