Students learn multiple programming languages during their undergraduate studies in Computer Science. In some cases, students learn at least two languages during their first two courses, such as Java and Python. While the transition between languages early in the curriculum is well-studied and usually scripted, little is known about students' language preferences and outcomes when given a choice in later courses. We provided students in a third-in-a-sequence major-required algorithms course the choice of language on each programming assignment (PA). Following a CS1 course in Python and a CS2 course in Java, students were asked to complete their PAs in either Java or Python, given equivalent scaffolding code. We conducted pre-course surveys of language preferences and analyzed the language use and resulting overall performance of 268 students across two semesters. On average, students had more self-reported familiarity and had taken more courses in Java, but felt Python had a better reputation. Additionally, while students tended to program in the language they were more familiar with, over 25% of students completed at least one PA in the other language. In two individual PAs (one per semester), students who used Python scored significantly higher than those using Java. However, there was no statistically significant difference in overall scores across PAs, problem sets, and quizzes based on their chosen PA language. Students also faced similar struggle---i.e., average number of submissions---on PAs regardless of language. Therefore, educators of upper-level courses should not worry about the impact of programming language choices on student outcomes.

Students typically experience multiple programming languages early in their Computer Science studies.Some programs have trended towards starting with languages like Python [2-5] to facilitate learning while enabling instructors to include real-world and engaging examples in the CS1 classroom, such as asking students to write classifiers for cancer data or create games [4].However, students may then be required to quickly transition to C++ [4], Java [5], or other languages as early as their second CS course.While several studies [3,4] have shown that students make this transition fairly well, little evidence exists on student preferences and course outcomes in later courses when given a choice of language rather than the curriculum or instructor's prescribed language.To address this gap, we examined programming language preference and performance of 268 students across two semesters in a third-in-a-sequence major-required algorithms course at the University of Virginia 1 .In this course, students were given a choice between two familiar languages on all programming assignments.More specifically, following a CS1 course in Python and a CS2 course in Java, students in Data Structures and Algorithms 2 (DSA2) were allowed to complete assignments in either Python or Java and were provided equivalent scaffolding code in both languages.We found that while students who chose to write in Python scored significantly higher on two programming assignments (one of five per semester), there was no statistically significant difference in overall outcomes or struggle-defined in Alzahrani et al [1] as the average number of submissions for programming assignmentsbetween students who complete their programming assignments solely in Python, solely in Java, or a combination thereof.Additionally, there was no statistically significant difference in overall scores on programming assignments, written problem sets, or quizzes from the course based on the language students chose when implementing their solutions.From these results, we conclude that providing students with a choice of programming language, including allowing students to program in a language they are more familiar with, does not appear to dramatically improve student outcomes.Additionally, the

The Artificial Intelligence Smart Classroom Initiative (ASCI) presents a re-imagined set of online course tools, designed primarily to support growing computer science classes. The system has four primary tools: an office hours queue, an automatic student grouping algorithm, a course-specific local large-language model (LLM), and administration tools for detecting students and TAs that need support. These tools interoperate to improve the quality of one another (e.g., LLM conversations support students directly in the office hours queue) and are enhanced by synchronizing data from multiple external sources such as Piazza, Gradescope, and Canvas. The system has been deployed in multiple courses over the past three semesters: initially as a FIFO queue, then supporting manual grouping and smart grouping of office hour attendees, and recently including LLM support. Preliminary results indicate that students who were grouped using the tool were more likely to return to the queue more than twice as often (on average) than those who were not. However, while grouping in office hours has the potential to decrease student wait times, teaching assistants and students tend to favor one-on-one meetings over group meetings. This might be improved in the future with updates to the software, TA training, and incorporation of other supporting tools (e.g., LLM technology). The other, newer, tools will be more thoroughly evaluated in future semesters.

Auto-grading technologies have become increasingly prevalent in computing education, driven by the need to handle growing class sizes and provide timely and effective feedback. We conducted a survey of 44 computer science instructors at various institutions in order to gather instructor experience and use of auto-graders, the features instructors value most, and the challenges and limitations faced when using these tools. We specifically asked about factors such as grading strategies and policies, opinions on existing tools, and other automated grading methods they employ. Our results indicated that instructors prefer tools that offer significant customizability and integration capabilities, with functionality and program output-based grading as the most commonly used approaches. They emphasized the need for integrated auto-grading solutions that include robust core features and prioritize extensibility to better align with pedagogical goals and to support instructors in managing the increasing demands of computer science education. Based on these findings, we conclude that existing solutions should be improved to address instructor-reported preferences and diverse educational needs.

This research full paper explores the connection between office hours use and online forum engagement in introductory computer science courses. Office hours (OH) and online question-and-answer (Q&A) forums provide a platform for students to interact with their classmates and instructors. We investigate the relationship between student engagement in an online discussion forum (Piazza) and utilization of office hours across 5 semesters of an introductory CS course. We explored the correlation between Piazza utilization and OH attendance, discerned disparities between in-person and online OH involvement, and analyzed the distinct approaches of men and women in engaging with course resources. We found that active Piazza users visit OH more than inactive Piazza users. More specifically, students who interact above average on Piazza in each metric observed - asks, answers, posts, and views - attend OH more than those who are below average in each metric. Additionally, students who attend OH at least once tend to post, ask, answer, and view posts on Piazza more frequently than those who have never attended OH. This indicates that above average help-seeking students on Piazza and in OH tend to engage with available resources more than those who did not seek help as often. This quantifies how often - and through which methods - students seek help. Based on prior research and our findings, we find it likely that students often begin by seeking answers on Piazza. If they find the response unsatisfactory, they then resort to OH for clarification. We also examined the modality of office hours, comparing in-person and online interactions; there is no significant statistical difference in the number of OH visits between those who attend virtually vs those who attend in person. However, online OH visits tended to take longer than in-person visits. In terms of the relationship between engagement and gender, our findings show that women visit OH more than men, both in person and online, and take longer in their OH visits. These findings emphasize the importance of course engagement resources in assisting with learning while also highlighting factors that affect engagement, such as gender, mode of engagement, and usage of other resources, giving instructors a better understanding of which populations tend to engage with specific course resources.

We investigate the effects of a flexible late policy on student performance submission behavior across two semesters of a lower-level required course (LL) and an upper-level elective (UL). The first semester late policies in both courses were strict: LL incorporated a 10% penalty per day for two days; UL rarely allowed late submissions. In each course, the late policy was relaxed in the second semester to provide two no-penalty late days for each assignment.

As undergraduate enrollment in computer science rises, instructors continue to investigate methods to improve the student experience at scale. One aspect commonly used in courses at scale is queue-driven office hours, in which students join an online queue and meet with teaching assistants on a first-come, first-serve basis (FIFO).

Project-based (PB) learning has become increasingly popular in computer science education, particularly as studies have found that the teaching style better prepares students for future careers and improves learning outcomes through increased student engagement. Online forum usage is one measurable component of engagement. In order to study the impact of PB learning on online forum engagement, Piazza usage data from seven online computer science courses at a higher education institution were collected and examined. We analyzed the differences in online forum usage between PB and assignment-based (AB) learning, in addition to differences between men and women in each course type. Specifically, this study builds upon and replicates a previous study on Piazza that measured student engagement, anonymity usage, and peer parity. We found that students in PB courses were less actively engaged in online forums than students in AB courses; they were less likely to ask and answer questions on Piazza but were more likely to view posts and be logged on more days. Across both course types, students posted anonymously a similar amount as a proportion of the total number of questions and answers and experienced a proportionally similar amount of peer parity. Our findings mirror prior results on gender engagement on Piazza. Across both PB and AB courses, women were more engaged, asked and viewed more questions, posted anonymously more frequently, and were less likely to experience peer parity than men.

Online assessments allow instructors to facilitate exams and quizzes in both virtual and large classes. Having a clear online timer during these assessments is vital to help students manage their time. However, these same timers can be a cause of anxiety, affecting student performance. Our goals were to determine (i) which types of visualizations are currently in use, (ii) which styles of online timer were preferred by students, and (iii) if providing students a choice of timer impacted their performance.

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon. This will be accomplished via a literature review and survey to identify the tools that have been developed by the community; the solutions that are currently available and in use by instructors; what features are needed moving forward for classroom and research use; what support for extensions is needed to support further CS Education research; and what institutional challenges instructors and researchers are currently facing or have faced in the past in developing, deploying or otherwise using community software solutions. Finally, the working group will identify factors that limit adoption of solutions and ways to integrate and improve the accessibility, discoverability, and dissemination of existing community projects, as well as manage and overcome institutional challenges.

As computer science educators, many of us share the common challenge of assessing students' programming skills. While a number of commercial services have popped up to fill this need, numerous members of the CS-Education community develop and maintain their own in-house tools. Commercial services may increase the costs to institutions and students, while instructor-developed tools are usually tailored to their own use cases; as a result, new instructors often, in turn, build their own highly-specialized solutions. This BOF is intended to spark a conversation about how we as a community can address this challenge by working together. In particular, we will discuss the availability of current open source tools and how they can be generalized, with a focus on editors, compilers, questionnaires, and scoring systems for assessments, including web-based and local-software solutions. Looking forward, we will discuss possible avenues to unite platforms and code bases, coordinate, and collaborate in the future-including student involvement as part of their coursework (e.g., capstone projects) to further develop platforms. Finally, by providing a dedicated session, we hope to advertise and disseminate the open source availability of these projects for those who seek a low- or no-cost solution for their students.

Visualizing time limits during online assessments is a cause of anxiety, affecting student performance. An initial survey of 34 students across two Computer Science courses found that time-tracking devices produced anxiety for 67.7% of students. While students differed on timer color preference, a majority preferred a count-down display showing time remaining with the ability to hide the timer. In a small pilot study across five exams, we employed multiple time-tracking displays. Preliminary data suggests that students presented with a count-down grayscale timer performed better on average than those presented with a green-yellow-red (GYR) version. Other displays, such as text-only digital count-down timer or elapsed time progress bars, did not elicit as large a difference in performance. These findings indicate the need for further study.

The coronavirus (COVID-19) pandemic has forced an unprecedented global shift within higher education in the ways that we communicate with and educate students. This necessary paradigm shift has compelled educators to take a critical look at their teaching styles and use of technology. Computing education traditionally focuses on experiential, in-person activities. The pandemic has mandated that educators reconsider their use of student time and has catalysed overnight innovations in the educational setting. Even in the unlikely event that we return entirely to pre-COVID-19 norms, many new practices have emerged that offer valuable lessons to be carried forward into our post-COVID-19 teaching. This working group will explore what the post-COVID-19 academic landscape might look like, and how we can use lessons learned during this educational shift to improve our subsequent practice. The exploration will strive to identify practices within computing that appear to have been improved through exposure to online tools and technologies, and that should therefore continue to be used in the online space. In the broadest sense, our motivation is to explore what the post-COVID-19 educational landscape will look like for computing education.

Collaborative learning has been effective and widely adopted in Computer Science education. Existing studies have controlled for group sizes by assigning members to determine the optimal collaboration environment, with some focusing on a peer-programming environment and others observing a wider range of sizes and tasks.

Online discussion forums are being increasingly used in classrooms as a way to encourage collaborative learning and community. Piazza is one such forum that was built specifically for academic institutions, and has been widely adopted. Students have the opportunity to ask questions and seek answers from peers and instructors alike online, allowing them to find the information they need even if they do not know fellow students in the class or if they cannot make an instructor's office hours. However, recent analysis of the popular online discussion site Stack Overflow, suggests that women are more likely than men to withdraw from such a community if they do not identify other members of the same gender. Women are often a minority in computer science courses and may express difficulty interacting with or seeking help from their peers who are predominantly men. Considering the importance of providing equal access to students regardless of gender and the value of resources like Piazza in one's education, it is imperative to assess the representation and impact of gender on Piazza. We analyzed data from over 2,500 Piazza users across three computer science courses at the University of Virginia and found that women on Piazza post more questions than men, spend more time on the discussion site, and achieve higher reputation scores on average. However, they are more likely than men to both ask and answer questions anonymously and less likely to receive responses from members of the same gender.

As Wikipedia has grown in popularity, it is important to investigate its diverse user community and collaborative editorial base. Although all user data, from traffic to user edits, are available for download under a free and open license, it is difficult to work with this data due to its scale.

We provide a method for assessing self-reported rates of cheating among students. The method is both i) privacy-preserving in the sense that one cannot use answers as evidence that any particular student cheated and ii) non-anonymous in the sense that one can record each student's answer for use in future correlative studies. Because accuracy relies on students' willful participation, we describe how to convince students that they take no risk by taking the survey. This method showed that 42% of 847 students willfully cheated in an Algorithms course. Surveying 181 CS Theory students showed no difference in cheating rates on written vs. coding assignments.

The best-studied techniques for reducing academic dishonesty rates rely on increasing the likelihood of consequences. These techniques offer instructors effective tools for identifying dishonest behavior as a means to "encourage" honesty. We wonder if we can promote integrity through proactive measures, such as designing courses' structures and assignments to reduce temptations for cheating, or by sculpting culture and forming relationships to foster a robust "community of trust." Our discussion will consider students' perspectives on academic integrity, how those might differ from instructors' perspectives, and how to build firm yet compassionate systems for promoting honesty in coursework.

The paper discusses a course in software development, as advocated by the CC2001 report. The course revolves around a single project divided into six assignments. In addition, the course includes lab assignments covering the tool of the week. The order of coverage of topics and the order of labs is determined using just-in-time learning. Grading criteria and an assessment of the course are discussed.