Inclusion in neuroscience through high impact courses

Recognizing that STEM disciplines, including neuroscience, have a long way to go to attract and retain diverse talent, educators can take action by being more intentional about their departmental curricula, course design, and pedagogical strategies. A deep body of research suggests that one way we can promote inclusion is through the use of high impact practices (HIPs). These active learning teaching practices promote deep learning and student engagement and have been shown to have a positive differential impact on historically underserved student populations. Here we describe the characteristics of two different types of HIP courses, makerspace classes, and course-based undergraduate research experiences (CUREs). In addition, we provide ideas for how these courses can be structured to help all students engage and learn. With experience overseeing a large campus-wide program introducing these course types to the curriculum, we also provide insights about faculty experiences and assessment. We propose that including these types of courses in a curriculum can engage a more diverse group of students to choose neuroscience as a major and as a career

Estimating the responsiveness of college applications to the likelihood of acceptance and financial assistance: Evidence from Texas.

This paper investigates the impact of Texas's Top Ten Percent Rule - which grants automatic entry to any public college in Texas for Texas high school graduates who graduate in the top decile - and subsequent targeted recruitment programs initiated by Texas's flagship universities. Using data on SAT test takers in Texas from 1996-2004, we find that the Top Ten Percent rule affects the set of colleges that students consider, and the targeted recruitment programs are able to attract the attention of students from poor high schools that were not traditional sources of students for the flagships in Texas

Estimating the responsiveness of college applications to the likelihood of acceptance and financial assistance: Evidence from Texas

This paper investigates the impact of Texas's Top Ten Percent Rule--which grants automatic to any public college in Texas for Texas high school graduates who graduate in the top decile--and subsequent targeted recruitment programs initiated by Texas's flagship universities. Using data on SAT test-takers in Texas from 1996 to 2004, we find that the Top Ten Percent Rule affects the set of colleges that students consider, and the targeted recruitment programs are able to attract the attention of students from poor high schools that were not traditional sources of students for the flagships in Texas.College choice Top Ten Percent Rule Targeted recruitment

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Student Evaluations of Teaching (SETs) are widely used in colleges and universities. Typically SET results are summarized for instructors in a static PDF report. The report often includes summary statistics for quantitative ratings and an unsorted list of open-ended student comments. The lack of organization and summarization of the raw comments hinders those interpreting the reports from fully utilizing informative feedback, making accurate inferences, and designing appropriate instructional improvements. In this work, we introduce a novel system, SETSum, that leverages sentiment analysis, aspect extraction, summarization, and visualization techniques to provide organized illustrations of SET findings to instructors and other reviewers. Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSum helps them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both). This demonstrates that our work holds the potential to reform the SET reporting conventions in the future. Our code is available at https://github.com/evahuyn/SETSumComment: NAACL 2022 Demo (20 pages

Patterns of self-regulation and the Big Five

The authors examined relations between self-regulatory properties of personality (ego-control and ego-resilience) and the Big Five. Ego-control and ego-resilience were independent predictors of each Big Five dimension. Additionally, cluster analysis suggested four replicable types. Participants in the first (largest) cluster reported the highest levels of resilience and moderately low levels of control. The second cluster reported above-average resilience and high control. The third cluster reported below-average resilience and extremely low control. The final cluster reported very low resilience and high control. The four clusters differed systematically in their Big Five profiles. These findings suggest that self-regulatory processes are co-ordinated with other basic personality dimensions, and attest to the utility of conducting both variable-centred and person-centred analyses