Background and Context. An ongoing thread in computing education research is how to increase women's participation in computing. One potential way is to improve their sense of belonging, as prior research has found that individuals' sense of belonging is related to their persistence in computing. Research from other STEM fields has shown that students benefit from sharing a gender identity with their professor. However, there is limited work on how a teaching assistant's (TA) identity relates to students' outcomes, especially in computing contexts.

Background and Context. Academic help-seeking from both internal (course-affiliated) and external resources is a key part of computing students' learning. The recent emergence of Generative AI (GenAI) tools has substantially transformed students' help-seeking, but our understanding of this impact remains limited. Objectives. We seek to understand the collective changes in computing students' (1) preferences of using external help resources and (2) usage of internal help resources, and the relationship between these two kinds of metrics at the individual level. We also seek to examine whether the relationship is subject to context influences. Method. We analyzed students' self-reported preferences for external resources and recorded usage of two internal resources in 26 offerings of four computing courses (N=3,921 total enrollments) at our institution over a timespan of eight 15-week terms. Findings. We find increases in students' preferences for external resources and decreases in their usage of internal resources in some but not all courses. We identify a substantial negative relationship between the two kinds of metrics at the individual level, but its strength depends on help resource and instructional context.

Background and Context. Prior experience (PE) has been shown to be related to computing students' performance, persistence, and help-seeking behavior. However, most works studied prior programming experience in introductory programming (CS1) courses, while other forms of PE in other contexts are underexplored.

Background and Context. Existing works in computing students' help-seeking and resource selection identified an expanding set of important dimensions that students consider when choosing a help resource. However, most works either assume a predefined list of help resources or focus on one specific help resource, while the landscape of help resources evolve at a faster speed.

Background and Context. Academic help-seeking is vital to post-secondary computing students’ effective learning. However, most empirical works in this domain study students’ help resource selection and utilization by aggregating the entire student body as a whole. Moreover, existing theoretical frameworks often implicitly assume that whether/how much a student seeks help from a specific resource only depends on context (the type of help needed and the properties of the resources), not the individual student.

Although hybrid courses have become increasingly common in higher education, it remains uncertain whether a student's experience of a course is consistent between in-person and online modalities. To investigate this, we analyzed student modality and discussion data from the Spring 2023 offering of an elective data science course where students are allowed to attend each lecture in person or synchronously online.

We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the working query so that it becomes semantically equivalent to the target. It is particularly useful in an educational setting, where novices can receive help from Qr-Hint without requiring extensive personal tutoring. Since there are many different ways to write a correct query, we do not want to base our hints completely on how Q* is written; instead, starting with the user's own working query, Qr-Hint purposefully guides the user through a sequence of steps that provably lead to a correct query, which will be equivalent to Q* but may still "look" quite different from it. Ideally, we would like Qr-Hint's hints to lead to the "smallest" possible corrections to Q. However, optimality is not always achievable in this case due to some foundational hurdles such as the undecidability of SQL query equivalence and the complexity of logic minimization. Nonetheless, by carefully decomposing and formulating the problems and developing principled solutions, we are able to provide provably correct and locally optimal hints through Qr-Hint. We show the effectiveness of Qr-Hint through quality and performance experiments as well as a user study in an educational setting.

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 that didn't go as planned, related to offering additional help to students. Each contributor will describe their "good idea," the situation that resulted, and wider lessons for the CS community.

Undergraduate teaching assistants (UTAs) office hours are an approachable way for students to get help, but little is known about why and for what do the students choose to attend office hours. We sought to understand what kind of help the students believe they need by analyzing the problem-solving step students self-reported when joining the office hours queue app. We used the UPIC framework to aggregate course specific problem-solving steps to enable comparing between seven data sets from a CS1 and a data science course across four semesters. We then compared the class-level and student-level phase distributions to understand the differences between the two courses and the two levels in the courses. We found most students have a "primary phase" where a majority of their interactions fall, and there are significant individual differences in their phase distributions. Moreover, we did not find either students' demographics or the context of their first visits to significantly impact their individual differences in the phase distributions, suggesting students may have fixed beliefs on how to approach office hours. Finally, a strong majority of interactions happen within 3 days of the deadline, such that the UPIC distribution for those days looks like the class-level phase distribution.

Concerns about academic misconduct are nearly ubiquitous among educators, and are especially prevalent in computer science. However most conversations relating to misconduct focus on how students cheat, how to detect when they do, and how to discipline offenders. This emphasis on "detect and punish" can have severe negative consequences, including toxic classroom cultures, adversarial student-staff relationships, and massive mental and emotional workloads for instructors. In this panel, we examine possible root causes for misconduct in CS courses and advocate for shifting the narrative to focus on designing and delivering courses that discourage misconduct by being inclusive and supportive to all students. We also offer concrete suggestions for approaches to reduce mis-conduct through non-punitive means.

No abstract available.

This panel is the evolution of a Technology that Educators of Computing Hail (TECH) Birds of a Feather session held at SIGCSE for seven years, grew into popular panels for many years, and served as a springboard for a regular column in ACM Inroads. It will provide a chance for seasoned middle school, high school, and university educators to showcase the technologies they can't live without, what problems they solve, and how to use them. This year, we asked our panelists to highlight any technology in particular that helped them survive (and possibly even thrive!) during their remote teaching.

Despite the enduring popularity of SQL (Structured Query Language), it is challenging to learn and debug, even for people with considerable programming experience. There is also a lack of SQL tools with advanced debugger features like breakpoints, stepped execution, and variable watching. We present I-Rex, an interactive SQL debugger that enables users to trace the evaluation of a query by its constituent blocks, visualizing how each block computes results from its inputs, and exploring the dependencies among these blocks. I-Rex can be integrated into an autograder, which typically works by comparing the results of submitted queries against reference queries over test database instances. Instead of showing full test instances, which often overwhelm students, I-Rex automatically generates small, illustrative instances for debugging. In this demo, we show how I-Rex helps a student trace complex SQL query execution, learn the semantics of various query constructs, and understand why a query produces (or does not produce) certain results. We also show how a teacher can customize I-Rex for a set of SQL exercises over a database. Overall, we demonstrate how I-Rex supports SQL learning and debugging, thereby increasing students' self-reliance and reducing the burden on the teaching staff.

Discussion forums are invaluable resources when scaling up undergraduate CS courses to larger class sizes. However, passive incorporation of discussion forums is not a silver bullet, as these platforms tend to devolve into places of shallow engagement. To aid our understanding of the factors that influence the nature of these interactions, we collected data from three CS1/CS2 forums. We obtained survey responses from the course instructors and performed a content analysis of the question-response pairs across all the courses. The results suggest that students' help-seeking patterns are influenced by the course curriculum, mode of delivery, and the existence of other help-seeking avenues. The findings also shed light on common strategies used by instructors to incentivize productive student-teaching staff and student-student interactions (e.g., instructing students to describe their debugging questions in detail, asking teaching staff to respond with hints/questions instead of direct answers). This poster presents a series of takeaways that can inform CS educators' choices around discussion forums.

We present the CS1 Reviewer App - an online tool for an introductory Python course that allows students to solve customized problem sets on many concepts in the course. Currently, the app's questions focus on code tracing by presenting a block of Python code and asking students to predict the output of the code. The tool tracks a student's response history to maintain a "mastery level" that represents a student's knowledge of a concept. We also provide an option of answering auto-generated quizzes based on the student's mastery across concepts. As a result, the tool provides students a choice between creating their own learning experience or leveraging our question selection algorithm. The app is supported on traditional webpages and mobile devices, providing a convenient way for students to study a variety of concepts. Students in the CS1 course at Duke University used this tool during the Spring and Fall 2020 semesters. In this paper, we explore trends in usage, feedback and suggestions from students, and avenues of future work based on student experiences.

Most personal student interactions with instructional staff come through office hours. Particularly in large courses, office hours are predominantly run by teaching assistants (TAs). TAs are best advantaged by support and training from more senior instructional staff, especially faculty. This Birds-of-a-Feather session will provide a forum for discussing challenges and innovations in managing office hours towards improving the student learning experience and environment by discussing ideas around (1) personalized support and mentoring for TAs, (2) technological support such as online queues and internal wikis, (3) considerations for remote teaching and learning, and (4) methods for evaluating office hours. It is hoped that these conversations may inspire transformative changes to office hours structure that lead to future educational innovations in research and in practice. While this session will emphasize TA-supported office hours, the discussion may also inspire new ideas for managing instructor office hours as well. During our session we will create breakout rooms for each topic and use a shared document where each room can take notes. After the break out session, each group will report back and create a summary of their discussion notes, which will be publicly archived at https://kevinl.info/office-hours

The CS-Ed Podcast's theme for season 2 is "Where should we go from here?" Three of the episodes focused on equity. This panel consists of all the podcast guests that spoke on this topic and the podcast host will serve as the moderator. In their episode, each panelist focused on a different aspect of equity, including cultural competence, supporting students of color, and how to make computer science education an integral and sustained part of learning through systemic change. This panel will be a conversation on equity by having the guests react to each other's episodes and to questions from the audience.

Metacognitive monitoring is an individual's ability to assess their level of mastery. This skill is integral to learning because students decide what to study based on what they believe they do not understand. Therefore, how well can a CS1 student gauge their mastery? We had students demonstrate their metacognitive monitoring skills by predicting their scores for all three exams of a 15-week CS1 course. We collected data from two course offerings. Moreover, we had students predict both before and after each exam to understand the effect of seeing it and surveyed students on how they studied. We found that our study's students were reasonably accurate, but low performers were worse than high performers. However, high performers did not improve between their before and after predictions. We did not have sufficient evidence that students improve their predictions over time. Prediction accuracy did not have a gender effect. Finally, we found no difference in study tactics by gender and little difference between high and low performers. Overall, we found only some similarities to related work.

Most student personal interactions with the course staff come through office hours. Particularly in large courses, the office hours are predominantly run by teaching assistants (TAs). TAs are best advantaged by support and training from more senior instructional staff, especially faculty. This BOF will provide a forum for discussing mentoring techniques and other individualized support of the TAs (particularly in computer science courses) to improve student learning and experience, as well as longer-term gains to the discipline. Second, we will be discussing what technological support exists, such as queue software or internal wikis, to improve student learning and experience in office hours. Finally, we will discuss ideas for more significant changes in office hour structures that could lead to future collaborations or research experiments.

Formative feedback is important for learning. Code-tracing is a vital skill in computer science learning. We set out to deliver formative feedback to students on code-tracing, constructed-response assessments by building a student error model using insights gained from inspecting the assessment's frequent wrong answers. Moreover, we compared two different kinds of hints: reteaching and knowledge integration. We found wrong answer co-occurrence provides useful information for our model. However, we were unable to find evidence in our intervention experiment that our hints improved student outcomes on post-test questions. Therefore, we also report here our results on a retrospective, exploratory analysis to understand potential reasons why our results are null.

Constructed-response, code-tracing questions ("What would Python print?") are good formative assessments. Unlike selected-response questions simply marked correct or incorrect, a constructed wrong answer can provide information on a student's particular difficulty. However, constructed-response questions are resource-intensive to grade manually, and machine grading yields only correct/incorrect information. We analyzed incorrect constructed responses from code-tracing questions in an introductory computer science course to investigate whether a small subsample of such responses could provide enough information to make inspecting the subsample worth the effort, and if so, how best to choose this subsample. In addition, we sought to understand what insights into student difficulties could be gained from such an analysis.

In contrast to multiple-choice or selected response questions, constructed response questions can result in a wide variety of incorrect responses. However, constructed responses are richer in information. We propose a technique for using each student's constructed responses in order to identify a subset of their stable conceptual misunderstandings. Our approach is designed for courses with so many students that it is infeasible to interpret every distinct wrong answer manually. Instead, we label only the most frequent wrong answers with the misunderstandings that they indicate, then predict the misunderstandings associated with other wrong answers using statistical co-occurrence patterns. This tiered approach leverages a small amount of human labeling effort to seed an automated procedure that identifies misunderstandings in students. Our approach involves much less effort than inspecting all answers, substantially outperforms a baseline that does not take advantage of co-occurrence statistics, proves robust to different course sizes, and generalizes effectively across student cohorts.

For an instructor who is teaching a massive open online course (MOOC), what is the best way to understand their class? What is the best way to view how the students are interacting with the content while the course is running? To help prepare for the next iteration, how should the course's data be best analyzed after the fact? How do these instructional monitoring needs differ between online courses with tens of thousands of students and courses with only tens? This paper reports the results of a survey of 92 MOOC instructors who answered questions about which information they find useful in their course, with the end goal of creating an information display for MOOC instructors.