As computing skills become increasingly vital across disciplines, programs designed as ''bridges'' between prior degrees and a master's in computing have emerged as important pathways for individuals with non-computing backgrounds to prepare for graduate study in computing-related fields. The bridge typically consists of a set of courses providing a foundational understanding of topics often covered in an undergraduate computing program.

The student journey in degree programs can be viewed as a pipeline: applicants must be admitted, admitted students must matriculate, and matriculants must persist and perform. Using administrative records from over 50,000 applicants to an online graduate computing program, we model these three stages as distinct outcomes: admission, matriculation among admitted students, and longer-run success among matriculants. For each stage, we estimate multivariable logistic models using structured application attributes (e.g., age, citizenship, application history, undergraduate GPA) and highdimensional text features from self-reported academic and employment histories (TF-IDF). We then compare success models based on application data alone versus application data augmented with firstterm performance and enrollment patterns. Across stages, models reveal different correlates of advancement through the pipeline: application features strongly differentiate admission and yield, while long-run success is only weakly explained at application time but becomes highly explainable when we include first-term academic momentum. Together, the results clarify what can be inferred at each decision point and highlight the outsized role of early-course performance for retention-oriented interventions.

After several years of relatively stable application growth, one online at-scale CS graduate program experienced a major uptick in applications in 2024. Like other at-scale degree programs, this program generally accepts all qualified students, and the number of matriculating students is projected based on applications rather than established based on capacity. As a result, understanding drivers of application growth is important to anticipate unexpected sudden increases in demand. In this analysis, we chart trends in application growth over this program's history and report the results of a survey sent to the application class of 2024 to better understand their motivations for applying.

Research opportunities offer students at the master's level a chance to apply their knowledge, create projects for their portfolio, and to gain an understanding of the research process in preparation for potential doctoral studies. The traditional structure of these opportunities, however, is not trivial to translate to online programs with asynchronous delivery. As online programs grow, it is important to examine ways that the traditional benefits of these opportunities can be extended to a broader range of students. In this poster, we discuss our experience building the infrastructure and running early efforts at reaching this goal. We discuss several dedicated courses and seminars developed to offer larger scale opportunities for students to pursue research. We also discuss efforts in active development to provide better infrastructure support to reduce the friction and complexity of pursuing research at scale.

The research focuses in academia are typically determined from the top down, with professors focusing on projects that align with their existing lab or can be readily supported by grants. This approach is pragmatic, but this focus may not align with the bottom-up interests of the available student body. As part of a broader effort to expand the available research opportunities in a graduate program in computer science, this work focuses on collecting data on what is motivating student interest in research and what specific fields students desire to study with the intention of using the results for decision-making about how to allocate resources to best match student interests. This work reports on the responses of 143 graduate students in computer science at a major research institution in the United States in a course-based program where access to research opportunities is not guaranteed. It examines both the fields of particular interest and the self-reported motivations leading these students to attempt to seek out research opportunities.

International research collaboration (IRC) leads to stronger, more visible research, and it allows researchers to account for global perspectives in their work. This is particularly important in Learning @ Scale research, as "scale" often implies learning that spans countries or continents. This paper examines IRC at Learning @ Scale by analyzing the authorship of all Learning @ Scale papers over the conference's 10-year history. We find that IRC at the conference is low, and possibly trending downwards. Additionally, authors from South American, African, and Asian countries tend to favor IRC, and those countries that publish most frequently at Learning @ Scale tend to be less internationally collaborative. This paper serves as a call to action for researchers and the conference to increase IRC, and we provide recommendations for accomplishing this goal.

This paper examines research trends from the first 10 years of the Learning @ Scale conference across three dimensions: research context, subject matter, and the countries of the authors' affiliations. Each of the 562 papers published over the conference's history was coded across these dimensions. The results reveal a significant drop in MOOC research, a rise in research from outside the United States, and a consistent focus on computer science and STEM curricula.

Ten years ago in 2014 saw the launch of the first in what would become a trend to launch online, at-scale degree programs (mostly at the graduate level) by leveraging MOOC pedagogies and platforms. Using publicly-available data, this paper describes ten trends that characterize the first decade of one such program in computer science in order to assess the audience and performance of students who elect to enroll in such programs. Past research has found that students in these programs tend to enroll in large part because they do not have other options for rigorous, respected credentials that fit into their professional and personal lives; this study unpacks many of these trends. Specifically, this study finds that the fraction of women enrolling in the program has steadily increased over time, that the gender discrepancy can be partially explained by underlying differences in women in CS across different nationalities, that applicants to the program tend to be evenly split between technical and non-technical backgrounds, and that the geographic distribution of students over time has shifted toward more international audiences even while the state in which the program originates comprises the largest fraction per capita of enrollees. The paper concludes by discussing how these trends might generalize to feedback to other similar programs and other at-scale initiatives.

Research on how students prepare for graduate computer science programs typically focuses on single, subject-specific interventions or relates to preparation for life as a graduate student. Preparatory work completed prior to enrolling in such a program can be particularly important for underrepresented minorities and those without technical backgrounds. We use survey data from incoming students in a large online graduate computer science program to answer three research questions: What are the backgrounds of students entering the program? How do students prepare for the program? And how does student preparation differ based on demographics and prior experience? We find that: male students are more likely than female students to enter the program with computer science qualifications; older students, female students, and those with non-technical degrees are more likely to pursue preparation; and students with no online learning experience are less likely to pursue preparation. These findings highlight the importance of student backgrounds when creating preparatory courses and indicate the value of preparatory courses in increasing diversity in large online graduate programs.

We examine trends in the Learning at Scale conference from 2014 through 2021. We use an original coding scheme to classify all 142 full papers from five angles: setting, approach, pedagogical strategy, population of interest, and dependent variable. We observe a decline of research on MOOCs, an increase in number of settings studied over time, and a consistent focus on assessment strategies. We then examine other conferences to which Learning at Scale authors contribute research. This paper contributes an original coding scheme to classify future Learning at Scale papers; an analysis of the research focuses at Learning at Scale over time; and an evaluation of the mutual influence between Learning at Scale and other venues. These latter two contributions contextualize learning at scale research within Learning at Scale and the broader research community to show how the field has evolved and to help predict its future directions.

Proponents of at-scale education initiatives often tout the benefits of these programs to the students who enroll in them, but anecdotally, faculty who participate in creating at-scale courses have often commented on how their participation positively impacted their subsequent in-person teaching. In this study, we investigate this potential phenomenon more thoroughly to assess how generalizable this perspective is among participants in these initiatives. We conduct three studies: we interview 78 faculty teaching in at-scale degree programs, examine offering histories for 70 courses offered in one such program, and survey 153 faculty who have developed massive open online courses. Based on these three studies, we propose a list of ways in which in-person instruction benefits from the existence of at-scale teaching initiatives. We further discuss remaining perceived drawbacks of at-scale education as derived from these interviews and surveys.

Using a Computer Science affordable degree at scale as a case study, the context of this paper explores the ways in which student life initiatives can humanize the at scale and online distance learning student experience through a holistic and student care-centric approach. By analyzing the top ten student life initiatives featured in an online at scale computer science program, we highlight ways to ensure belonging, facilitate engagement, and create community at scale while tackling inherent problems of online distance learning such as isolation and social disconnectedness.

The General Data Protection Regulation (GDPR), passed in 2018, outlined new data privacy standards applying to residents of the European Union (EU). The impact of this law stretches beyond the EU to anyone - such as researchers across the world - collecting or processing data from EU residents. Researchers have had to augment their methodologies to ensure GDPR compliance. This initiative intersects with at-scale educational programs, which often enroll EU students and are also often the subject of institutional research. While creating a study to research students in an online Master of Science in Computer Science (MSCS) program, some of whom are in the EU, we encountered difficulties with ensuring GDPR compliance. This paper discusses the implications of the GDPR related to both general research and our specific study. We discuss the challenges of interpreting the GDPR and integrating it into our methodology as well as potential solutions, our ultimate resolution, and practical recommendations, and we consider what the future of data privacy legislation means for researchers.

Demand for CS education has risen, leading to numerous new programs, such as the rise of affordable online degrees. Research shows these programs meet an otherwise untapped audience of working professionals seeking graduate level CS education. In this study, we examine the motivations for enrollment among students in one such online MSCS program. Based responses to an open ended question, we develop a typology of motivations, including goals (e.g. career transition), opportunities (e.g. enrolling without taking time off work), and assurances that their goals will be met (e..g the program's accreditation). We then issue a closed survey question to a new group of students to further explore these motivations. In this paper, we discuss both aggregate and demographic trends in motivations, including the different motivations of men and women and what they imply about the program's impact on the gender divide in computing. We also examine older students' tendency towards intrinsic motivation to pursue an MSCS degree.

Peer advising in education, which involves students providing fellow students with course advice, can be important in online student communities and can provide insights into potential course improvements. We examine reviews from a course review web site for online graduate programs. We develop a coding scheme to analyze the free text portion of the reviews and integrate those findings with students' quantitative ratings of each course's overall score, difficulty, and workload. While reviews focus on subjective evaluation of courses, students also provide feedback for instructors, personal context, advice for other students, and objective course descriptions. Additionally, the average review varies by course overall score, difficulty, and workload. Our research examines the importance of student communities in online education and peer advising at scale.