Over the past decades, computer science education (CSEd) research has studied the multitude of factors that may impact student success in introductory programming courses (CS0/CS1). The lack of foundational structure behind how these factors interrelate has made it difficult to gain a thorough understanding of this area of CSEd literature. Gaining a deeper understanding and applying structure to these factors would allow CSEd to adopt better teaching practices, study habits, learning environments, course materials, etc. and to better understand the student experience to better foster success among a broader population of students. Our systematic literature review used search criteria for factors that predicted student success in CS0/CS1, which yielded 311 research articles. We then mapped this body of work under the Biggs' 3P (Presage, Process, Product) educational model, which provides a comprehensive framework for how students engage with learning opportunities. We discovered that although many studies focused on the Presage and Product phases of the model, fewer studies mapped to the Process phase, which describes the students' active learning processes. Our study shows there is a potential gap in the literature and future studies should focus more specifically on how students choose to engage with learning opportunities and what factors may be hindering that engagement throughout a learning period.

Research in introductory computer science (CS1) has been an ongoing and popular area in the CS Education community. As a foundational course in any CS curriculum it is the entry point for any aspiring computer science major or anyone considering the major. Over many decades, research has focused on a variety of different factors and contexts that have impacted student success in CS1. These factors are numerous and focus on different aspects of the student, learning setting, and even pedagogical approaches to teaching. Furthermore, the community has begun seeking further use of theory in computer science education research, whether that is adopting a theory from another field (such as education or psychology) or developing a CS-specific theory. This doctoral project focuses on creating a foundation for the future development of a theory describing student learning and the student experience in CS1. The scope of this thesis is not to develop a full theory, but to survey the current CS1 literature and highlight important insights that can be central to a theory or require further research. The key supporting project of this work is a systematic literature review across articles that focus on a variety of factors that have been associated with student learning and success in CS1.

Prior studies in multilingual computing education have shown that many non-native English speakers (NNES) in India struggle with introductory programming courses as they learn both a programming language (e.g., Java) and a natural language (e.g., English) concurrently. Although multiple studies have been conducted with NNES in India whose first language is Hindi or Tamil, we do not yet know the influence a students' native language may have among Spanish speaking students in the United States. This replication study investigates the effects of an instructional design integrating the students' native language along with English on high school students' learning and engagement in a two week CS0 course using the block-based programming language, Scratch. We designed an experiment to teach introductory computing topics (e.g., algorithms, variables, loops, conditionals) to two groups of students from a rural area spanning multiple institutions in the US. The experimental group was taught using English and Spanish (students' native language) and the control section was taught using only English. A pre-test and post-test was conducted to test students' programming knowledge before and after the course. We also recorded all the questions students asked during the course to measure student engagement. We found that teaching Scratch programming using Spanish and English is no different than teaching Scratch programming using only English to high school students whose native language is Spanish. We also found that the students in the experimental group asked more questions when compared to the control group.

Recent research in computing has shown that student performance on prerequisite course content varies widely, even when students continue to progress further through the computing curriculum. Our work investigates instructors' perspectives on the purpose of prerequisite courses and whether that purpose is being fulfilled. In order to identify the range of instructor views, we interviewed twenty-one computer science instructors, at two institutions, that teach a variety of courses in their respective departments. We conducted a phenomenographic analysis on the interview transcripts, which revealed a wide variety of views on prerequisite courses. The responses shed light on various issues with prerequisite course knowledge, as well as issues around responsibility and conflicting pressures on instructors. These issues arise at the department level, as well as with individual course offerings.

Computer science students struggle in early computing courses as evinced by high failure rates and poor retention. As such, studies have attempted to characterize the root of student struggles from many perspectives, including cognitive, meta-cognitive, and social emotional. Typically, studies have limited their inquiry to a specific perspective or a single course. This paper reports the results of a broad student experience survey conducted across several computer science courses. Through a periodic survey, students rated various cognitive, socio-emotional, external, personal, and structural barriers in terms of how much each impacted their learning throughout the term. An exploratory factor analysis of these questions revealed four factors—personal obligations, lack of sense of belonging, in-class confusion, and lack of confidence—that capture a range of possible struggles students may face. We analyzed the prevalence of these factors across courses, performance quartiles, and demographic groups broken down by gender, race/ethnicity, and matriculation status. Students in lower performance quartiles report higher stress levels on multiple factors, with statistically significant differences found between all quartiles and courses, for most factors. Moreover, students from traditionally underrepresented groups report struggling more across all four factors, suggesting that they may be facing more challenges than classmates from represented populations. Overall, these findings indicate that student struggles are associated with stresses from many areas of their lives, suggesting that future interventions should target multiple areas of stress.

Spring 2020 brought enormous change to student learning, as universities scrambled to put into place support structures to aid students' learning in a remote context. Computer science education was both well-positioned for this change and faced unique challenges, e.g. that students often need significant (in-person) support with programming. In this study we examine how aspects of students' remote learning experience in spring 2020 compared to the same aspects in previous in-person, pre-COVID offerings of 6 lower- to mid-division computer science courses at UC San Diego (UCSD), a large US research university. We were in a unique position to make this comparison because we had been collecting data on several aspects of students' course experiences throughout the 2019-2020 academic year. We found, surprisingly, that most elements of students' experiences that we examined were unchanged, or even improved, in spring 2020. Students in spring reported similar or lower stress levels and found their courses similarly or less challenging relative to previous quarters. However, some aspects did degrade. Students had less connection with their peers (particularly in introductory classes), more interference from family obligations, and higher drop/fail rates in some classes. Surprisingly, these results hold across all assessed demographics. Our results indicate that the actions UCSD and its CS instructors took to mitigate the stresses of remote learning in spring 2020 were largely successful and provide implications for improving education beyond the pandemic.

Previous studies show that CS students may not learn as much from their courses as we might expect. This could have ramifications on how students succeed in their future careers and may explain why researchers report a gap between industry expectations and the abilities of recent CS graduates. However, previous studies have also shown that students improve their prerequisite knowledge in subsequent courses. This study investigates the introductory data structures proficiency of students in different courses at various stages in our CS program, employing the validated Basic Data Structures Inventory (BDSI). Additionally, we investigate whether subpopulations, including transfer students and underrepresented groups, may be more prone to not attaining as much knowledge from our courses as we might expect. We find that students' knowledge of basic data structures is, on average, better in later courses. However, we also find subpopulations of students that perform worse than others or seem to not improve their knowledge in later courses. Specifically, we find students that transferred to our institution from a different school perform significantly worse on the BDSI than other students and do not improve their BDSI performance in later courses. We also find students from demographic backgrounds that are underrepresented in computing scored slightly, though not statistically significantly, worse than others. Our findings warrant future investigations on how our programs can better serve the students in the affected subpopulations.

Over a decade ago, the CS1 course for students without prior programming experience at a large research-intensive university was redesigned to incorporate three best practices in teaching programming: Media Computation, Pair Programming, and Peer Instruction. The purpose of this revision was to improve the quality of the course, appeal to a larger student body, and improve retention in the major. An initial analysis of the course indicated an increase in pass rates and 1-yr retention of students in the major. Now that time has passed and those students impacted by the revision have had time to graduate, this longitudinal study revisits and expands on these prior findings through examining student outcomes over a twelve year period (2001 through 2013). The student outcomes examined include failure rates in CS1, retention rates in the major, rates of switching into the major, time to degree, and performance in subsequent major courses. We compare these findings against similar metrics collected for another CS1 course at the same institution that caters to students with prior programming experience and did not make changes during this same time period. Overall, the inclusion of media computation, pair programming, and peer instruction corresponds to a significant improvement in passing rates for CS1 as well as retention of majors from CS1 through graduation. In turn, there is no indication that this larger group of students experienced any harm in terms of lower grades in upper-division courses or their time to degree.