Motivation: Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) suggests cognitive design principles for coordinating views, but says little about what determines whether learners actually engage with the representations available to them.

Generative Artificial Intelligence (GenAI) tools are increasingly used by computing students, yet their effects on learning outcomes remain mixed. Prior work found that while GenAI use may improve performance on assignments, it can negatively relate to overall course performance. We aim to replicate and extend this work across four computing courses. Using self-reported GenAI usage from assignments and study preferences alongside course performance data, we examine how these relationships vary by course, and compared to the previous study. Our results show that students who used GenAI tools to solve the assignment performed equally or better than those who did not report using it, however, they received lower final grades in the course. We observe no major difference between students who used GenAI to study for the midterm test compared to those who did not. These findings suggest that the impact of GenAI use is present in various contexts, highlighting the need for instructional guidance on how students should use GenAI as a learning aid, and insights for other instructors that wish to integrate GenAI tools into computing curricula.

As computing education becomes increasingly globalized, many students learn computer science through a second language. Prior work has documented cognitive and performance challenges faced by non-native English-speaking (NNES) students, yet less is known about how language background shapes their motivational experiences and intentions to persist. Drawing on Expectancy-Value Theory, we analyzed matched pre- and post-term survey data from 374 students (198 NNES, 176 NES) in a CS1 course at a large North American university. We measured programming self-efficacy, implicit theories of intelligence, need for cognition, sense of belonging, motivation and learning strategies, and intentions to major in computing. NNES students began the course believing intelligence is fixed and had lower self-efficacy on language-dependent tasks, gaps that persisted throughout the term. However, despite lower confidence, NNES students were more willing to choose challenging assignments and consistently used more strategic learning approaches. While NNES and native English-speaking (NES) students reported similar overall belonging, NNES students experienced greater belonging uncertainty, specifically when encountering difficulties, and were more likely to want to fade into the background within the CS community. These findings reveal language background as a persistent motivational cost in introductory computing and underscore the need for instructional designs that explicitly support belonging, self-efficacy, and adaptive strategy use for linguistically diverse learners.

Motivation: Database courses focus on SQL querying (DQL) while treating schema definition (DDL) and data manipulation (DML) as side topics, even though real-world database work begins with understanding schema design and data updates. This misalignment leaves students underprepared for authentic data management practice. Method: We integrated scaffolded DDL and DML exercises as a core concept in a third-year data course across three offerings (2023-2025). Students completed structured weekly tasks in an LMS that provides immediate feedback and unlimited attempts, encouraging low-stakes, iterative practice. We analyzed student interaction data (number of attempts and performance) to examine learning patterns across DDL/DML and DQL. We analyzed 9,071 total exercise submissions from 669 students, examining both the number of LMS exercise attempts and assignment performance across DDL/DML and DQL. Results: Students required fewer attempts on DDL/DML tasks than on traditional DQL tasks, indicating strong receptiveness when these topics were properly scaffolded. Early performance on schema-definition tasks was moderately correlated with later SQL performance, suggesting that schema competence supports subsequent query learning. Implications: We encourage database educators to teach schema design and data manipulation as core topics to strengthen students' conceptual foundations, as our results suggest these skills are learnable with scaffolding and may support subsequent query learning.

Motivation: Solid-state drives (SSDs) are now ubiquitous in computing systems, yet their internal data layout and operations remain largely invisible and difficult for students to conceptualize. Existing operating systems (OS) teaching materials provide minimal learning support for these abstract processes, which in turn limits student understanding. Method: We designed SSDVis, a web-based interactive SSD visualizer that enables students to simulate file operations and observe SSD internals through coordinated visualizations and step-by-step execution. We evaluated it in a third-year OS course by surveying students (N=154) on their usage and perceptions of SSDVis. Results: 95.4% of survey participants reported using the visualizer extensively, and the overwhelming majority reported improved understanding of SSD data layout, file operations, and garbage collection when using SSDVis. Students valued the interactive features for predicting and verifying behavior, including the step-by-step mode. While usage for understanding wear leveling was lower, this likely reflects the topic's inherent complexity to observe indirectly in elaborate scenarios, rather than usability issues. Implications: Our findings show that a pedagogically designed SSD visualizer has the potential to effectively support the learning of modern OS topics with complex hidden mechanisms.

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have led to changes in educational practices by creating opportunities for personalized learning and immediate support. Computer science student perceptions and behaviors towards GenAI tools have been studied, but the effects of such tools on student learning have yet to be determined conclusively. We investigate the impact of GenAI tools on computing students' performance in a database course and aim to understand why students use GenAI tools in assignments. Our mixed-methods study (N=226) asked students to self-report whether they used a GenAI tool to complete a part of an assignment and why. Our results reveal that students utilizing GenAI tools performed better on the assignment part in which LLMs were permitted but did worse in other parts of the assignment and in the course overall. Also, those who did not use GenAI tools viewed more discussion board posts and participated more than those who used ChatGPT. This suggests that using GenAI tools may not lead to better skill development or mental models, at least not if the use of such tools is unsupervised, and that engagement with official course help supports may be affected. Further, our thematic analysis of reasons for using or not using GenAI tools, helps understand why students are drawn to these tools. Shedding light into such aspects empowers instructors to be proactive in how to encourage, supervise, and handle the use or integration of GenAI into courses, fostering good learning habits.

Faculty and industry practitioners recognize written communication to be important in computer science, but it can be challenging to convince students of the same. As student perceptions are molded early in a program of study, we focus on early-year CS students to understand their perceptions towards the importance of writing in CS, with the goal of framing discipline-specific writing pedagogy. We qualitatively analyze responses from first and second-year CS students in a survey about the role of writing in their field. The responses reveal that a majority view writing as an indispensable skill. Specifically, students recognize it as a fundamental skill, applicable across diverse contexts, and uniquely relevant in CS compared to other fields. We identified 4 perceptions that they hold which are helpful to their development as writers: that writing is a useful fundamental skill, which is useful for achieving various goals in a variety of contexts, and that writing in CS is different than in other fields. However, 20% of responses include reasons why writing is not important in CS, and we identify 4 perceptions harmful to students' development as writers: that writing skills can be avoided, are defined narrowly, do not need to be developed beyond a baseline, and come at the cost of computing skills. We believe that there is an opportunity to align discipline-specific writing instruction with these useful and harmful perceptions.

In computing courses, instructor involvement and social comfort are vital for resilience and belonging. We examine engagement with instructor emails aimed at strengthening the connection with students. We sent weekly emails from instructors to first- and upper-year computing students. These emails included reminders for the assignments due each week. Half of the students received reminders embedded in an informal message that contained approachable wording and relevant current course events, while the rest received a list of precise deadlines. This text had no emotional engagement from the instructor. We collected and analyzed email access and link click rates, along with student survey responses about email preferences and engagement. We found that first-year students had lower email access and link click rates than upper-year students. While we did not find differences in first-year engagement based on the type of email, upper-year students appeared to be more engaged when receiving the intentionally informal version of the email. Understanding the message preferences of computing students can enhance instructor messaging and improve engagement. Strategies should be explored to boost first-year student engagement, while the higher engagement among upper-year students underscores the importance of instructor support in advanced courses.

Motivation and Objectives. Productive Failure is a problem-based learning technique where students attempt to solve a problem before receiving instruction in the topic. By design, students may not find a satisfying solution. Prior studies of Productive Failure in STEM contexts have been conducted in secondary or introductory college settings. Focusing primarily on exploring appropriate analysis and modeling techniques, these studies showed that a Productive Failure approach can lead to greater conceptual knowledge acquisition and transfer capabilities compared to »traditional«Direct Instruction techniques. In this study, we build on these studies along two dimensions: First, we report on the design and evaluation of a Productive Failure intervention in a more advanced undergraduate class: third-year Operating Systems. Second, our intervention targeted a more advanced skill: applying synchronization primitives, rather than selecting appropriate modeling and analysis techniques.

Instructors often prefer to use email for course communication. The use of emails has been widely discussed in the fields of marketing and behavioural design, but the prevalence of email in education makes it important for instructors to collect metrics on emails to see how students engage with them. One component of emails are the subject lines, which constitute as one of the first things a receiver sees before deciding to open an email. This poster discusses a case study at deploying an email intervention in an online CS1 course. We investigate how the length of subject lines impact the rate at which students open emails of a particular type that prompts them to start their homework early. We aim to share key results to inform instructors how to design their emails to better reach students. Further, we highlight the potential benefits for instructors when collecting and analyzing email engagement data.

Writing skills are often considered unimportant by computer science students and were under-emphasized in our curriculum. We describe our experience embedding CS-specific writing instruction at scale in most of our large, core, first- and second-year Computer Science courses, each with 300-800+ students. Our approach is to collaborate with a writing specialist and a community of course instructors, centralize the management of writing teaching assistants, and introduce a variety of relevant genres and contexts to help students develop and apply writing skills. We outline the institutional support and organization crucial to a project of this scale. In addition, we report on a survey collecting student perception of the writing instruction/assessment. We reflect on quantitative and qualitative evidence of success, as well as the challenges that we faced. We believe that many of these challenges will be common across institutions, particularly those with large courses.

With the shift to online delivery, instructors looked to provide comparable help supports for students, especially for first-year learners who need timely assistance the most. Our work aims to understand student help seeking behavior and perception of getting help in an online CS2 course.

This study analyzes common issues in the writing of our upper-year, undergraduate computer science students in timed (e.g. tests) and untimed (e.g. longer assignment reports) scenarios. Our goal is to identify writing issues that should be addressed earlier in the CS curriculum. In collaboration with a writing specialist, we develop and fine-tune a rubric with Grammar, Conciseness, Clarity, Organization, Structure, and Formality as the main categories.

Most active learning methods aim to engage students in collaborative problem-solving. While active learning and collaboration benefits are indisputable, more investigation is needed to understand student engagement in group activities. This qualitative study investigates the student perspective on group work in a CS2 inverted classroom, to better understand the learner mindset and identify potential barriers or conduits for collaborative engagement. We conducted 30-45 minute interviews with 30 participants from six sections of CS2, with five sections being scheduled in an Active Learning Classroom (ALC) and one in a traditional lecture hall, all taught in the same inverted model and using the same in-class activities. A multitude of facets of student behavior or engagement in group work and interactions with peers were identified via emergent coding. We classified emerging themes into higher-order categories which subsume semantically-related themes, forming a hierarchy with the top-level categories being Perceived Utility and Social Environment. This classification is intended to provide insight to educators seeking to better engage students in active learning via collaborative in-class activities.

We surveyed 533 CS2 students taught in an inverted classroom model either in an active learning classroom (ALC) or a traditional lecture hall, with both groups shifting to an online setting towards the end of term. Students perceived the ALC as more conducive to group work than a traditional classroom, while the online environment was perceived similarly by both groups of students, with those transitioning from an ALC having a less positive perception of collaborative engagement in an online context.

We report our experience of preparing and conducting unplanned online exams in the unique half-physical, half-virtual semester of Winter 2020. The report covers four courses in a large university's computer science program, ranging from first-year to third-year. With the data generated by students taking both in-person and online exams in multiple courses, we perform analyses to evaluate the validity of the online exams (especially the unproctored ones) as an assessment of student understanding. With the fine-grained student activity data provided by the online exam platform, we are also able to investigate the patterns in student exam-taking behaviours and their correlations with student performance on the exam. In addition, we share, in detail, the tips and lessons that were learned throughout the process of designing, implementing, and hosting the online exams.

Active learning environments have only recently started to be analyzed in the CS discipline, in terms of their effect on student performance. Recent studies in CS1 found contradictory results, in part due to different control on the learning pedagogy used, and issued a call for further investigation. This study evaluates the effects of the learning space on student performance in CS2, as measured by their grades. We use a quasi-experimental setup with 529 participants across five lecture sections over one academic term. All sections employ the same active learning method (inverted classroom), identical lecture materials, and the same number of TAs for in-class support, but differ in terms of classroom type (active learning classroom vs traditional lecture hall), instructor, and lecture time of day. Similarly to a recent study in CS1, we find no significant impact of the learning space in CS2. We also inspect factors not analyzed in previous studies, such as student prior preparation (as measured by prerequisite CS1 grades), course drop rates, and exam failure rates, and find that the CS2 sections are statistically similar. This work also examines student survey responses, to assess student perception differences on properties of the learning space which may impact their learning experience, such as the use of technology, ability to hear the instructor, ability to get help during lectures, and conduciveness of desk types to group work.

Traditional relational database engines have been losing ground to specialized data processing engines in virtually every market segment, from data warehousing, OLTP, and stream processing, to scientific applications. Although relational database engines are evolving to leverage new technologies and more efficient processing paradigms, the generality of a large monolithic engine often makes this a significant effort. Our aim is to delimit and decouple database engine components to design a more lightweight and flexible data processing engine that can support any application domain efficiently and without the effort of a complete redesign. We introduce Slingshot, a new data processing engine, where modularity and implementation flexibility are the top priority. Its core database engine is minimal and mainly handles inter-operation of the database components. Each component, abstracted by an interface, can be externally implemented and plugged into the framework as a module that handles the component's functionality. As a result, this allows designers the liberty to choose suitable features for their target applications, to drop excess functionality, and to optimize code independent of the rest of the engine. We compare Slingshot to a traditional RDBMS and to custom solutions on queries that are representative of three application types (spatial, OLAP, and OLTP). We show that Slingshot outperforms the RDBMS in most cases, while performing comparably in others. Furthermore, Slingshot performs better or comparable to custom solutions on most tests. Finally, Slingshot's flexibility allows us to efficiently leverage computer architectures such as GPUs for speeding up complex computational tasks.

Spatial join is a crucial operation in many spatial analysis applications in scientific and geographical information systems. Due to the compute-intensive nature of spatial predicate evaluation, spatial join queries can be slow even with a moderate sized dataset. Efficient parallelization of spatial join is therefore essential to achieve acceptable performance for many spatial applications. Technological trends, including the rising core count and increasingly large main memory, hold great promise in this regard. Previous parallel spatial join approaches tried to partition the dataset so that the number of spatial objects in each partition was as equal as possible. They also focused only on the filter step. However, when the more compute-intensive refinement step is included, significant processing skew may arise due to the uneven size of the objects. This processing skew significantly limits the achievable parallel performance of the spatial join queries, as the longest-running spatial partition determines the overall query execution time.

Spatial data analysis applications are emerging from a wide range of domains such as building information management, environmental assessments and medical imaging. Time-consuming computational geometry algorithms make these applications slow, even for medium-sized datasets. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends demands effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud.

Spatial databases are increasingly important for a wide variety of real-world applications, such as land surveying, urban planning, cartography and location-based services. However, spatial database workload properties are not well-understood. For example, it is unknown to what degree one spatial application resembles another in terms of resource demand, or how the demand will change as more concurrent queries (i.e., more users) are added. We show that spatial workloads have a different CPU execution profile than well-studied decision support workloads, as represented by TPC-H.

The volume of spatial data generated and consumed is rising exponentially and new applications are emerging as the costs of storage, processing power and network bandwidth continue to decline. Database support for spatial operations is fast becoming a necessity rather than a niche feature provided by a few products. However, the spatial functionality offered by current commercial and open-source relational databases differs significantly in terms of available features, true geodetic support, spatial functions and indexing. Benchmarks play a crucial role in evaluating the functionality and performance of a particular database, both for application users and developers, and for the database developers themselves. In contrast to transaction processing, however, there is no standard, widely used benchmark for spatial database operations. In this paper, we present a spatial database benchmark called Jackpine. Our benchmark is portable (it can support any database with a JDBC driver implementation) and includes both micro benchmarks and macro workload scenarios. The micro benchmark component tests basic spatial operations in isolation; it consists of queries based on the Dimensionally Extended 9-intersection model of topological relations and queries based on spatial analysis functions. Each macro workload includes a series of queries that are based on a common spatial data application. These macro scenarios include map search and browsing, geocoding, reverse geocoding, flood risk analysis, land information management and toxic spill analysis. We use Jackpine to evaluate the spatial features in 2 open source databases and 1 commercial offering.

In this paper, we study parallelization of multiplayer games using software Transactional Memory (STM) support. We show that the STM provides not only ease of programming, but also better performance than that achievable with state-of-the-art lock-based programming, for this realistic high impact application.

This work addresses the problem of parallelizing multiplayer games using software Transactional Memory (STM) support. Using a realistic high impact application, we show that STM provides not only ease of programming, but also better performance than that achievable with state-of-the-art lock-based programming.