Background: Prior research about lecturing practices in computer science (CS) has not focused on the accessibility of those practices.

Access to formal computer science (CS) courses before college is limited and unequal. Students may instead learn about CS careers through informal channels such as media. My dissertation work includes three studies that examine how CS is portrayed on social media and one study that examines how students perceive this media. These studies contribute to a better understanding of the messages students encounter about CS careers outside of formal educational settings.

Background: Ongoing debates about the structure of cognitive load theory, which traditionally distinguishes among intrinsic, extraneous, and germane cognitive load, have important implications for how cognitive load is measured in educational research. Cognitive load theorists debate the distinction between germane and intrinsic load, as well as the reliability of self-reported cognitive load measures and their ability to capture germane load. Prior critiques of cognitive load surveys may equally apply to the widely used measurement tools in computer science education.

We report our development of a survey measuring students' sense of belonging in computing courses. Existing definitions and surveys are helpful in identifying and measuring perceptions of belonging (e.g., feeling accepted, included, and valued) but give little insight into why students feel they do (not) belong. To address this limitation, we use a conceptual framework that breaks sense of belonging into four components: motivations, opportunities, competencies, and perceptions. Survey item development was based on interviews with undergraduate computing students that were analyzed using this conceptual framework. We revised survey items based on 1) cognitive interviews and 2) psychometric properties of pilot survey data with participants from multiple required courses typically taken by first- and second-year undergraduate computing students. The resulting survey consists of 12 items with three items per component of the conceptual framework. A four-factor confirmatory factor analysis model indicated good empirical fit with the proposed structure of the framework (N=514). Cronbach's alpha indicated good internal consistency within each component (>0.70) as well as across all 12 items (>0.90). The inter-component correlations were relatively high for all pairs of components (>0.60), aligning with previous interview findings that indicate the components may influence each other. Altogether, we have sufficient evidence of validity for the survey's use in undergraduate computing courses and discuss avenues for future work.

College has a lot of unwritten rules, and some of our students will matriculate knowing those rules while others don't. Research has shown that revising homework assignments to make these unwritten rules explicit can help students' academic confidence and sense of belonging. This tutorial is intended for faculty who may be interested in revising their assignments to make those unwritten rules clear to students. We will focus on Transparent Assignment Design, a framework used to make explicit the purpose, task, and criteria of assignments. Come to this tutorial for a crash course on making your assignments transparent followed by hands-on help! Please bring an assignment you are interested in revising (or we will provide samples) and your choice of editing tool (e.g., laptop, pen/paper).

Background: Live coding, or the process of instructors writing code in real time in front of students, is an alternative teaching method to showing students static code examples. Variations of live coding tightly coupled with more active learning approaches are common, which can make it difficult to understand the contribution of live coding alone.

Sense of belonging correlates with retention rates for university-level students especially for those in their earlier years [5, 9, 10] and for those in science, technology, engineering, and math (STEM) and computing majors [2, 8]. In computing, women and students of color (i.e., Black, Latinx, and Indigenous) tend to report lower sense of belonging compared to men [6, 7]. Unfortunately, we lack consensus on how sense of belonging should be defined, and why students come to feel like they do or do not belong. Past definitions (e.g., [3, 4] help identify educational contexts and students’ feelings, they do not necessarily provide coherent insights into why students develop these feelings.

Background: Instructional transparency makes a course’s learning goals, evaluation criteria, and path to success clear to students, with the goal of improving equity in higher education. Increased transparency may improve equity by bolstering students’ self-efficacy and sense of belonging in computing, both of which are correlated with persistence in the field. Purpose: We aim to understand whether there are group differences in how students perceive and benefit from instructional transparency. We are additionally interested in understanding whether perceiving instructional transparency is positively correlated with students’ self-efficacy and sense of belonging and, therefore, can contribute to the persistence of students from historically underrepresented groups in computing. Methods: To investigate these relationships, we used linear regressions to analyze survey responses from 11,046 undergraduate students from 203 institutions. Findings: We found that there are group differences in students’ perception of transparency in their CS courses: students who identify as women, first-generation college students, and/or disabled reported perceiving less instructional transparency than their peers. We also found that perceiving more transparency has a positive correlation with students’ self-efficacy and sense of belonging in computing while controlling for important confounding variables, such as prior CS experience. We further demonstrated that this relationship is different for certain groups of students: first, for Black students and first-generation college students, perceiving transparency has a larger positive impact on their self-efficacy, and second, for Hispanic students, perceiving transparency has a smaller positive impact on their sense of belonging. Contributions: Our work constitutes one of the first empirical, multi-institutional investigations of the perceptions and benefits of transparency in CS classrooms that focuses on group differences. Our work also includes a theoretical articulation of the mechanisms through which transparent teaching practices may influence students’ self-efficacy and sense of belonging in computing. Taken together, our empirical findings and theoretical argument provide important evidence for the benefits of instructional transparency in CS courses, particularly as it relates to improving equity in computing.

Introductory programming courses can be taught in a variety of ways, including live coding, where instructors write code in real-time in front of students, or static code examples, where pre-prepared code is explained to students. While previous studies have compared live coding and static coding and their impacts on student assessment and cognitive load in large lecture environments, we present our experiences in a single lab session, highlighting student engagement differences. After presenting the same material to groups of students through a live-coding presentation and a static code presentation, we reflect on the observable differences in student engagement through an established framework of cognitive engagement. Additionally, we compare pre-surveys, post-tests, and cognitive load surveys from both groups. While our findings did not result in significant differences in student assessments, our experience highlighted differences between live and static code presentations. Live coding presentations can often take up to twice as long as static code presentations. Students may tend to ask more questions in live coding presentations, suggesting live coding provides instructors and students with more opportunities for further discussion. Live coding may also provide the instructor with additional opportunities to discuss other concepts that may not have been included in a pre-prepared presentation.