Interest in teaching-track faculty positions has been steadily increasing as enrollments in computer science degree programs continue to trend upward. While departments have welcomed these new teaching-track faculty members, senior faculty, department chairs, and university committees often struggle with how to best evaluate these faculty members during the promotion process. In our experience, some universities try to use a "watered-down" version of the tenure-track promotion standards with the intent of uniformity. Other universities have created whole new processes, which may be better at capturing the differences in teaching-track positions, but also can create a "second-class citizen" status for the teaching-track faculty members.

The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course.

This paper presents the first curricular landscape analysis of transfer pathways for computer science (CS) transfer students in the public higher education system in California, the largest and most complex higher education system in the United States. Drawing on data from 115 community colleges and 31 public universities in California, this study examines and compares computer science Bachelor's degree requirements, curriculum complexities, and both ideal and existing course articulation coverage between schools. We find considerable variation in the CS degree requirements across the system, particularly in the number of math courses required and the overall flexibility of the course requirements. Articulation agreements between community colleges and four-year schools have the potential to (and sometimes do) reduce the complexity of the degree for transfer students significantly, but articulation agreements are not consistently in place across the system. This research both suggests concrete action items and surfaces important areas of further exploration to create a more seamless process for transfer students to complete their CS Bachelor's degrees.

This paper reports on the longitudinal impacts of an inclusive, structured research experience program for early career undergraduates in computer science that engages a large number of students from minoritized groups. We compared academic performance and retention in the major for program participants at two large public research universities in the United States vs. a matched control group of demographically and academically similar students. We found that the retention rate of program participants was higher than the control at both universities, though not statistically significantly so. We found no significant difference in post-program GPA, and the program did not erase equity gaps in GPA by race and first generation status that existed before the program. These results help us understand the benefits and limitations of large-scale early research programs for increasing equity in computer science.

Engaging undergraduates in research has been shown to improve retention, increase students' sense of computer science identity, and increase their chances of continuing to graduate school. Yet research experiences at most universities are ad hoc, and many undergraduates-particularly those from groups underrepresented in computing-do not have the opportunity to participate. The Early Research Scholars Program (ERSP) is a structured, academic-year group-based undergraduate research program designed to help universities vastly increase participation in research for early computing undergraduates. ERSP launched at UC San Diego in 2014 where it now annually engages over 50 second-year undergraduates, 59% of whom are women, and 22% of whom are from underrepresented racial and ethnic groups. The program's portable design has enabled its expansion to 7 other colleges and universities. This workshop will train participants in launching ERSP (or any part of it) at their university to increase and diversify the undergraduates participating in research. Workshop leaders are the ERSP directors at four universities. They will address how to launch and run the program in different contexts. They will provide an interactive, hands-on experience of running the program covering the following topics: developing and teaching a research methods class, student application and selection to ensure a diverse and supportive cohort, and creating a dual-mentoring structure to engage and retain early undergraduates without overburdening faculty. Workshop participants will be invited to join the ERSP virtual community to get support launching their own version of ERSP.

This poster presents a study of the academic outcomes of students who participate in the Early Research Scholars Program (ERSP) at the University of California, San Diego. We investigated whether participation in ERSP improves the retention and academic outcomes of students in computing majors compared to a matched control group and whether there are demographic differences in these outcomes. Our control group comprised students who matched each of the 190 ERSP participants on admit year, department, GPA, race/ethnicity, gender, and transfer status. We found that participants were retained in computing at higher rates than the control group (95.6% vs. 90.2%), though the difference was only statistically significant for women students (96.1% vs. 83.0%). The overall ATE of ERSP on GPA was 0.083 grade points. Within the program, there was no significant improvement in post-program GPA distributions for minority ERSP participants compared to their non-minority peers.

While the computer science community has explored the importance of Undergraduate Research Experiences (UREs) and, separately, collaboration in computing (e.g. pair programming), little research has studied collaboration in the context of a URE. We performed a qualitative thematic analysis of how students collaborate within a group-structured, academic-year, inclusive computing URE catered towards second-year students at two large public research universities in the United States. We analyzed free-response and Likert-scale survey data collected early and late in the program from a total of 106 students who comprised three program cohorts. We studied their overall group function, what aspects of group work led to positive or negative group experiences, how their group affected their feelings of being supported, and how their group affected their sense of belonging in computing. We found that group experiences were overwhelmingly positive. Further, we found that students’ experiences in groups centered around three themes: group fit and belonging, emotional and academic support, and logistics. Within each theme, their experiences were rich and nuanced, and we observed variations by gender, and to a lesser degree by race. Our work suggests that group-structured UREs are both feasible and beneficial for students, and we give concrete suggestions for how to make these experiences successful.

Conference presentations usually focus on successful innovations: new ideas that yield significant improvements to current practice. Yet we often learn more from failure than from success. In this panel, we present five case studies of "good ideas" for improving CS education (most related to the COVID-19 pandemic) that didn't go as planned. Each contributor will describe their "good idea", the situation that resulted, and wider lessons for the CS community.

The Early Research Scholars Program (ERSP) was launched in 2014 at UC San Diego as a way to provide the benefits of research experiences to a large and diverse group of students early in their undergraduate computing career. ERSP is a structured program in which second-year undergraduate computing majors participate in a group-based, dual-mentored research apprenticeship over a full academic year. In its first four years ERSP engaged 139 students with a high proportion of women (68%) and racially minoritized students (19%), and participation in ERSP correlated with increased class grades. In 2018 we partnered with three additional universities to launch their own version of ERSP. Implementations at our partner sites have seen similar diversity and initial success, and have taught us how to implement the program in different contexts (e.g. quarters vs. semesters, different credit structures). This paper describes the structure of ERSP and how it can be adapted to different contexts to construct a scalable and inclusive research experience for early-career undergraduates in computing and related fields.

Course design and oversight is difficult, and we've seen a move toward including undergraduates on course staff over the past several years. In this panel, we argue that giving additional autonomy to the undergraduates involved in our courses is both empowering to them and beneficial to the course itself (as their perspective having actually taken the course can help us understand inequities and/or confusing pieces of the curriculum).

Students’ sense of belonging has been found to be connected to student retention in higher education. In computing education, prior studies suggest that a hostile culture and a feeling of non-belonging can lead women, Black, Latinx, Native American, and Pacific Islander students to drop out of the computing field at a disproportionately high rate. Yet, we know relatively little about how computing students’ sense of belonging presents and evolves (if at all) through their college courses, particularly in courses beyond the introductory level, and little is known about how sense of belonging impacts student outcomes in computing. In an extension of a previous study, we examined students’ sense of belonging in six early undergraduate computer science courses across three consecutive quarters at a large research-intensive institution in North America. We found that women and first generation students have a lower incoming sense of belonging across all courses. When exploring sense of belonging’s tie to student outcomes we found that lower sense of belonging was correlated with negative course outcomes in terms of pass rates and course performance. We also found that it is less tied to student performance as students get further into the CS curriculum. Surprisingly, there was no indication that sense of belonging is predictive of retention in terms of persistence to the next CS course outside of the first course in our two-course CS1 sequence.

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.

Although Massive Open Online Courses have the potential to reach a much broader audience and offer a lower cost education than traditional in-person classes, they have struggled with low completion rates and low diversity amongst those enrolled and completing the courses. In 2015, we built a series of online courses in computing with the specific goal of attracting and retaining students from groups underrepresented in computing. In our design, we incorporated a number of features aimed at improving the inclusive nature of the courses including: a project-centered course design; an online version of Peer Instruction ConceptTests; videos where students, faculty, and professionals report their struggles when they first learned computing concepts; videos by professional software engineers explaining how computing concepts from the course are used in industry; and videos aimed at providing additional support on the project to students who might be struggling. In this work, we report on the design of the courses and examine how successful our courses were at attracting and retaining women students. We find that compared to other computing courses offered by our institution on the same platform, our courses have: a higher percentage of women enrollment, higher rates of course completion for both men and women, and a slightly smaller gap between completion rates for men and women.

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.

Despite the documented importance of mentoring in undergraduate research, few studies examine how students---especially early undergraduates in computing---perceive their relationships with their mentors. We present a qualitative thematic analysis of the mentoring practices used in an inclusive, structured computer science research program targeting second-year undergraduates across two large public research universities in the United States. Uniquely in this program, students had two mentoring sources: a technical mentor for each research group and a graduate student mentor common to all groups. We analyzed reflections on mentoring from 64 undergraduate researchers at two points in the program. We compared the roles of the two mentors, characterized students' perceptions of both successful and unsuccessful mentors, and examined how mentoring relationships evolved. Generally, students valued mentors who provided project guidance or technical support and who were perceived to be friendly. We found that the roles of the two mentors were complementary in sometimes surprising ways. Overall, our analysis confirms prior work on undergraduate research mentoring, and provides new insights into the unique benefits of a dual-mentoring approach and how to best support early undergraduate computing researchers.

Given the long-standing concern about students failing introductory programming courses, there is a need for interventions that may aid those students. In this work, we examine the potential benefit of three interventions based on prior computing education research (CER) or STEM education research literature: mindset interventions, the use of "Thinkathons" as an alternative to programming labs, and metacognitive interventions to encourage more productive study habits. We conducted an in-class study that controlled for both time-on-task and selection bias to investigate the potential benefits of integrating these interventions into the existing footprint of an introductory computing course. Despite the previously reported promise of the interventions we implemented, our findings were that in this context these techniques had only a mild positive effect for some students. We discuss possible reasons why these techniques are less successful than instructors might hope and argue for the need for more research on this topic.

Undergraduate research experiences have been shown to have many positive effects on undergraduates including increased confidence, sense of belonging and retention. However, many previous studies of undergraduate research experiences have focused on advanced undergraduate (juniors and seniors) in one-on-one research experiences with a faculty mentor. Less is known about the effects of early undergraduate research, particularly via opportunities that scale beyond one-on-one faculty-student relationships to encompass large numbers of early undergraduates. The research question addressed in this work is whether a more scalable group-based research model aimed at early undergraduates from groups underrepresented in computing would show the same kinds of benefits for participants as more personalized one-on-one programs aimed at more advanced students. We evaluated a group-based early research program in the computer science department of a large public university. Through survey data and direct measurements of performance and retention several years after students had completed the program, we found that students who participated in this program have higher overall GPAs, more confidence, and more interest in research compared to several different control groups. Our design also allowed us to examine the considerable impact that selection bias can have on the evaluation of research programs. This work both validates the scalable structure of this research program and provides a richer perspective on the benefits of early undergraduate research in CS.

Although recent work in computing has discovered multiple techniques to identify low-performing students in a course, it is unclear what factors contribute to those students' difficulties. If we were able to better understand the characteristics of such students, we may be better able to help those students. This work examines the characteristics of low- and high-performing students through interviews with students from an introductory computing class. We identify a number of relevant areas of student behavior including how they approach their exam studies, how they approach completing programming assignments, whether they sought help after identifying misunderstandings, how and from whom they sought help, and how they reflected on assignments after submitting them. Particular behaviors within each area are coded and differences between groups of students are identified.

A number of recent studies in computer science education have explored the value of various data sources for early prediction of students' overall course performance. These data sources include responses to clicker questions, prerequisite knowledge, instrumented student IDEs, quizzes, and assignments. However, these data sources are often examined in isolation or in a single course. Which data sources are most valuable, and does course context matter? To answer these questions, this study collected student grades on prerequisite courses, Peer Instruction clicker responses, online quizzes, and assignments, from five courses (over 1000 students) across the CS curriculum at two institutions. A trend emerges suggesting that for upper-division courses, prerequisite grades are most predictive; for introductory programming courses, where no prerequisite grades were available, clicker responses were the most predictive. In concert, prerequisites and clicker responses generally provide highly accurate predictions early in the term, with assignments and online quizzes sometimes providing incremental improvements. Implications of these results for both researchers and practitioners are discussed.

This panel focuses on the challenges of collecting and analyzing data relating to retention of students in undergraduate computer science education programs. The panelists will share learnings and recommendations from the final report of the ACM Retention Committee and share their individual perspectives on data collection challenges, promising interventions, and recommendations for actively addressing retention for all students.

Using instructional videos - either as supplemental content or in a flipped classroom - has become increasingly popular among students and instructors in higher education CS courses. However, producing or finding appropriate videos can be expensive and time-consuming. This experience report describes a novel, relatively low-cost approach to creating customized video resources that requires very little instructor time. This approach leverages widely available screen capture and podcasting technologies to produce a suite of 2-5 minute "podcast highlights" videos from full-length live lecture footage. Podcast highlights distill lecture concepts into short segments, and supplementary annotations help illustrate these concepts further. Podcast highlights can be created quickly by undergraduates with just a small amount of instructor supervision. We created podcast highlights videos for four undergraduate CS courses. In a pilot study with two of these classes, many students used the highlights videos regularly and praised their usefulness for both previewing and reviewing class concepts. This report presents the podcast highlights production process, student reaction to and use of podcast highlights videos, and best practices for creation and deployment of these customized educational videos.

As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated and lightweight means to identify students who are at risk of performing poorly in a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. This study describes a modeling methodology that predicts student final exam scores in the third week of the term by using the clicker data that is automatically collected for instructors when they employ the Peer Instruction pedagogy. The modeling technique uses a support vector machine binary classifier, trained on one term of a course, to predict outcomes in the subsequent term. We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. Our modeling approach includes a set of strengths not seen wholesale in prior work, while maintaining competitive levels of accuracy with that work. These strengths include using a lightweight source of student data, affording early detection of struggling students, and predicting outcomes across terms in a natural setting (different final exams, minor changes to course content), across multiple courses in a curriculum, and across multiple institutions.

Engaging undergraduates in research has been shown to improve retention, increase students' sense of science identity, and increase the chances that they will continue to graduate school. Yet many undergraduates don't participate in research until very late in their undergraduate program, while most undergraduates don't participate in research at all. On the other hand, many faculty are eager and willing to do research with undergraduates, but are unsure how to mentor and supervise them, particularly early undergraduates who may have very little specific technical knowledge and skills. This workshop will provide participants with concrete skills and techniques for engaging early undergraduates (first and second-year students) in real research projects, and, if desired, for developing or growing a department-wide early undergraduate research program. Participants will engage in hands-on activities where they will learn how to develop appropriately scoped research projects, manage and mentor early undergraduates successfully, and teach core research skills like reading research papers and writing research proposals. The workshop will also cover how to mitigate specific challenges faced by students from groups underrepresented in computer science. The materials presented in this workshop are based on the successful NSF-funded Early Research Scholars Program at UC San Diego (ersp.ucsd.edu), which is in its fourth year, and engages 40 second-year students per year--the majority of whom are women and/or students from underrepresented racial or ethnic groups--in academic-year research apprenticeships.

Many college computer science majors have little or no pre-college computing experience. Previous work has shown that inexperienced students under-perform their experienced peers when placed in the same introductory courses, and are more likely to drop out of the CS program. However, not much is known about what, if any, differences may persist beyond the introductory sequence for students who remain in the program. We conducted a study across all levels of a CS program at a large public university in the United States to determine whether grade differences exist between students with and without pre-college experience, and if so, for what types of experiences. We find significant grade differences in courses at all levels of the program. We further find that students who took AP Computer Science receive significantly higher average grades---by up to a half grade---in nearly all courses we studied. Pre-college experience appears to have a weaker relationship with retention and with low-stakes assessment grades. We discuss the limitations of these findings and implications for high school and college level CS courses and programs.

With surging enrollments in computer science, large classes are becoming standard, even at the upper division. Unfortunately, this new reality can leave students feeling anonymous and unsupported. This work examines the impact of several lightweight interventions on students' sense of connection with instructors and the class. These strategies were employed in a range of large courses at a public research-focused university. The implemented techniques include: opportunities for one-on-one tutoring, seating assignments with consistent teaching staff members seated in class, and assigned small discussion sections, among others. All strategies are lightweight and require only the usual staffing resources afforded to a class. In this report, we evaluate student sense of community and reflect on the benefits and challenges of these techniques.

Underrepresented students in computing (women and non-White/non-Asian men) are known to feel a weaker sense of belonging than majority students (Asian/White men). This difference is important because a low sense of belonging can lead to disengagement and attrition in education settings. In the current study, we assessed whether and how early formal research experience might narrow this gap in sense of belonging. The sample for this study derives from a longitudinal study on undergraduate students affiliated with computing departments across the United States. We used propensity scores to generate an appropriate sub-sample of students to compare against formal research participants (n = 110 formal research students; n = 110 students with no formal research experience). We found formal research experience during students/ first year was associated with a strong sense of mentor support during their second year. Perceived mentor support predicted a strong sense of belonging for underrepresented students, but not majority students. Importantly, the typical gap in sense of belonging among underrepresented and majority students disappeared among students with high mentor support. Our work suggests that formal research, when introduced early, might promote greater diversity in computing in the long term. We present a model for early undergraduate research, and resources for readers who wish to adopt the model.

For many years, computing faculty have devoted substantial time and energy to the retention of diverse populations. But how are we doing really? The ACM Retention Committee has identified at least 5 populations of interest in tracking student retention: * Students who start college expecting to major in computing. * Students who enter college with some interest in computing, but also with other interests. * Students who enter college with interests outside computing, but who take computing early as part of a broad education. * Students who enter college with little or no interest in computing, but need a computing course to satisfy a general education requirement or a prerequisite in another discipline. * Students who transfer into a four-year university from a two-year college, partway into a computer science program. In practice, each group has different characteristics, and retention rates may vary dramatically. On some campuses, gathering data for the first group may be manageable--particularly if students declare majors as they enter college. Data collection and tracking for others is difficult, since these populations may not be known in early years. This BoF will identify approaches for tracking students and for exploring retention rates. Further, this BoF will encourage sharing and brainstorming for further mechanisms to help data collection. As we better identify retention rates among various populations, the ACM Retention Committee hopes we can better understand obstacles and opportunities related to retention. Session Agenda: Context/Introduction, Data most relevant locally, What data are currently tracked, Thoughts about a common data gathering instrument

This paper presents results of a large-scale survey of students' experiences in CS classes at two institutions: a small liberal arts college and a large research-focused university. Our work provides a fine-grained view of students' feelings and behaviors in CS classes, from introductory through to upper division courses. We find significant differences between the reported behaviors and feelings of female students compared to male students: female students are less comfortable asking questions in class and interacting with their instructor, and come out of a class with lower confidence in their ability to tutor for the class, despite the fact that they perform just as well as male students. Furthermore, we find some of these differences are consistent or increase across course levels, and could potentially affect students' post-college trajectories. Focusing attention on the student experience in more advanced classes may impact gender differences seen in the transition to the CS workforce.

As class-sizes grow in computer science, the personal attention received by students tends to diminish. This work aims to replicate small-class community effects within a large class by creating "micro-classes"---small groups within the large class. These micro-classes consist of 20--30 students led by graduate teaching assistants and undergraduate tutors who are specifically trained in small-classroom instructional techniques. This paper studies the outcomes of the micro-classes framework in an upper-division data structures course and compares them to outcomes from the same class taught in a large lecture, active-learning format. Students report increased satisfaction and a higher perception of community in the micro-classes section, though there was no discernible difference in student academic performance.

Research experiences for undergraduates (REUs) have many positive outcomes on students' perception of and retention in Computer Science (CS). Yet nearly all REUs are aimed at late-college students, well into a CS program. We present the Early Research Scholars Program (ERSP), a 4 quarter program designed to engage early-college (first or second year) CS students in high-quality research experiences in active research groups at a large research university. ERSP's structured course-supported group-apprentice model and its unique dual advising structure make it possible to vastly increase number of early-career CS students who participate in high-quality research experiences with little additional burden on individual faculty mentors. ERSP's focus on community building and support makes it particularly appropriate for students from groups who are traditionally underrepresented in CS. This paper reports the structure of the program and observations and learning thus-far with ERSP, with the goal of enabling others to implement this program at other large research-focused universities.

Students who attend academic conferences can broaden their horizons, increase their commitment to the discipline, find role models and mentors, and gain concrete opportunities for work and study. However, attending one's first conference can be an overwhelming experience. We often focus on the procurement of funding to send students to conferences, and do not spend as much time considering what will happen once they get there. In this BoF, we will share strategies for guiding and supporting undergraduates to take full advantage of attending a CS conference.

New CS1 curricula and pedagogies have resulted in many positive outcomes over the last several years including lower fail rates and increased long-term retention. Given these positive outcomes, the question becomes how much do the traditional factors of prior experience and confidence still play a role in students' performance in and attitudes about these courses' Furthermore, given that increasingly recommended collaborative pedagogies (e.g. pair programming) force students to interact with their peers for a large percentage of their work in the class, how much does the confidence of their peers affect their own attitudes and performance? This paper presents a study investigating these questions. We find that prior experience and confidence still predict success, but only for some students. We also find that student confidence levels have little to no impact on the attitudes and performance of their peers.

Objects in freely-drawn sketches often have no spatial or temporal separation, making object recognition difficult. We present a two-step stroke-grouping algorithm that first classifies individual strokes according to the type of object to which they belong, then groups strokes with like classifications into clusters representing individual objects. The first step facilitates clustering by naturally separating the strokes, and both steps fluidly integrate spatial and temporal information. Our approach to grouping is unique in its formulation as an efficient classification task rather than, for example, an expensive search task. Our single-stroke classifier performs at least as well as existing single-stroke classifiers on text vs. nontext classification, and we present the first three-way single-stroke classification results. Our stroke grouping results are the first reported of their kind; our grouping algorithm correctly groups between 86% and 91% of the ink in diagrams from two domains, with between 69% and 79% of shapes being perfectly clustered.

Historically, Harvey Mudd College (HMC) has had very little success attracting women to the study of computer science: women have chosen CS less than any other field of study. In 2006 HMC began three practices in order to increase the number of women studying and majoring in CS; these practices have now been in place for 3 years. With this paper we describe these practices and present a thorough evaluation of the quantitative and qualitative differences that have accompanied them. In sum, these efforts have rebalanced our department by significantly increasing women's participation in our computer science program.

This paper presents a thorough evaluation of CS for Scientists, a CS 1 course designed to provide future scientists with an overview of the discipline. The course takes a breadth-first approach that leverages its students' interest and experience in science, mathematics, and engineering. In contrast to many other styles of CS 1, this course does not presume that its students will study more computer science, but it does seek to prepare them should they choose to. We summarize the past year's worth of assessments of student learning, retention, and affect -- with particular attention paid to women's voices. Where possible, we contrast these student measures with those from a traditional, imperative-first CS1 that this new course replaced. The data thus far suggest that CS for Scientists significantly improves students' understanding of CS, its applications, and practice.

This paper describes an introductory CS course designed to provide future scientists with a one-semester overview of the discipline. The course takes a breadth-first approach that leverages its students' interest and experience in science, mathematics, and engineering. In contrast to many other styles of CS 1, this course does not presume that its students will study more computer science, but it does seek to prepare them should they choose to do so. In addition to describing the curriculum and resources, we summarize our preliminary assessments of this course and a comparison with the more traditional, imperative-first introduction it replaced. The data thus far suggest that this CS for Scientists course improves our students' understanding of CS, its applications, and practice.

This paper presents a modified diary study that investigated how people performed personally motivated searches in their email, in their files, and on the Web. Although earlier studies of directed search focused on keyword search, most of the search behavior we observed did not involve keyword search. Instead of jumping directly to their information target using keywords, our participants navigated to their target with small, local steps using their contextual knowledge as a guide, even when they knew exactly what they were looking for in advance. This stepping behavior was especially common for participants with unstructured information organization. The observed advantages of searching by taking small steps include that it allowed users to specify less of their information need and provided a context in which to understand their results. We discuss the implications of such advantages for the design of personal information management tools.

We present SketchREAD, a multi-domain sketch recognition engine capable of recognizing freely hand-drawn diagrammatic sketches. Current computer sketch recognition systems are difficult to construct, and either are fragile or accomplish robustness by severely limiting the designer's drawing freedom. Our system can be applied to a variety of domains by providing structural descriptions of the shapes in that domain; no training data or programming is necessary. Robustness to the ambiguity and uncertainty inherent in complex, freely-drawn sketches is achieved through the use of context. The system uses context to guide the search for possible interpretations and uses a novel form of dynamically constructed Bayesian networks to evaluate these interpretations. This process allows the system to recover from low-level recognition errors (e.g., a line misclassified as an arc) that would otherwise result in domain level recognition errors. We evaluated Sketch-READ on real sketches in two domains--family trees and circuit diagrams--and found that in both domains the use of context to reclassify low-level shapes significantly reduced recognition error over a baseline system that did not reinterpret low-level classifications. We also discuss the system's potential role in sketch based user interfaces.