Reflexive Thematic Analysis (RTA) is a critical method for generating deep interpretive insights. Yet its core tenets, including researcher reflexivity, tangible analytical evolution, and productive disagreement, are often poorly supported by software tools that prioritize speed and consensus over interpretive depth. To address this gap, we introduce Reflexis, a collaborative workspace that centers these practices. It supports reflexivity by integrating in-situ reflection prompts, makes code evolution transparent and tangible, and scaffolds collaborative interpretation by turning differences into productive, positionality-aware dialogue. Results from our paired-analyst study (N = 12) indicate that Reflexis encouraged participants toward more granular reflection and reframed disagreements as productive conversations. The evaluation also surfaced key design tensions, including a desire for higher-level, networked memos and more user control over the timing of proactive alerts. Reflexis contributes a design framework for tools that prioritize rigor and transparency to support deep, collaborative interpretation in an age of automation.

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.

Researchers use replication to confirm, strengthen, and advance Computing Education Research (CER). However, prior research shows that replication is infrequently used in CER, even though the community encourages its use. Previous research suggests that the CER community uses different terms to describe replication, which we aim to confirm in this Working Group (WG). We will conduct a Systematic Literature Review (SLR) across influential international CER venues to understand how researchers present and conduct replication studies, possibly identifying venues receptive to papers applying this research design. In addition, we will interview Computing Education researchers, conference leaders, and journal editors to understand their experiences and perceptions with replication. We expect to collect suggestions and recommendations on how CER can encourage more replication in future studies. Our work will highlight and confirm the terms the CER community uses to present replication studies, enabling researchers and educators to better identify these studies.

Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.

Motivation: Large Language Models (LLMs) have been widely applied to student-facing educational tools, this work explores their use in supporting instructors by presenting a practical adaptation of the Framework for Adaptive Content using Educational Technology (FACET) system to generate personalized instructional materials for an Introduction to Computer Programming (CS1) course.

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.

Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This friction suppresses the iterative, exploratory nature of scholarly work. To investigate these challenges, we conducted an exploratory design study with 20 experienced researchers. This study identified key friction points: 1) the high cognitive load of managing iterative query refinement across multiple databases, 2) the overwhelming scale and pace of publication of modern literature, and 3) the tension between automation and scholarly agency.

The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun supporting multiple modes of communication, known as multimodality. In this work, we explored how undergraduate programming novices choose and work with multimodal GenAI tools, and their criteria for choices. We selected a commercially available multimodal GenAI platform for interaction, as it supports multiple input and output modalities, including text, audio, image upload, and real-time screen-sharing. Through 16 think-aloud sessions that combined participant observation with follow-up semi-structured interviews, we investigated student modality choices for GenAI tools when completing programming problems and the underlying criteria for modality selections. With multimodal communication emerging as the future of AI in education, this work aims to spark continued exploration on understanding student interaction with multimodal GenAI in the context of CS education.

In engineering education, Generative Artificial Intelligence (GenAI) might be misused to complete assessments with limited understanding. On courses that allow the use of GenAI, students might also forget to acknowledge its assistance. There is a need to identify such assistance. We present an automated detector with over-uniqueness and sample matching. GenAI-assisted submissions are identified based on their uniqueness and their similarity to a GenAI sample. Unique to our GenAI detector, it requires no training data and/or dedicated rules for each programming/scripting language. Further, the method can be integrated into any existing similarity detectors to identify plagiarism. The detector covers five similarity measurements, two similarity modes, and eight programming/scripting languages. Our evaluation of four data sets with thousands of submissions shows that our detector is effective (71% MAP). However, many factors can affect its effectiveness, including submission length and student attempts to align the code. Combining both mechanisms does not result in higher effectiveness, yet it takes longer to process.

Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that personalized LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content.

Feedback is a cornerstone of effective learning, offering students insight into their progress, while also providing instructors with information to refine their teaching. This paper presents a practical tool leveraging large language models (LLMs) to cluster and summarize teaching assistant (TA) feedback. Designed for educators, the tool streamlines the identification of common student issues, provides course-level insights, and generates actionable summaries intended to be modified by educators and shared with students. To validate the tool, we conducted an instructor survey assessing its perceived usefulness and accuracy, compared TA and LLM-generated feedback on a shared assignment, and presented a case study where the tool informed improvements to the assignment handout. Our findings suggest that this practitioner-focused tool can enhance feedback workflows, promote consistency in instruction, and support scalable improvements in teaching and learning.

Computational Thinking (CT), particularly abstraction, is essential in engineering education, enabling students to break down complex systems into manageable parts. Abstraction helps learners focus on key elements of a problem, ignoring extraneous details. The PGK framework, suggested by Per-renet, Groote, and Kaasenbrood, defines abstraction across four cognitive levels: problem, algorithm, program, and execution. At a higher education institution that focuses on engineering education, we assessed students' abstraction skills using sorting algorithms, chosen for their foundational role and suitability for testing such skills. Our study focused on two areas: (1) the performance of computer science (CS) and non-CS students on algorithmic abstraction tasks, and (2) how factors like demographics, training, programming proficiency, and self-assessed abstraction mastery correlate with task performance. Results showed that all students, especially non-CS majors (including Engineering), need stronger skills at the algorithm, program (coding algorithms), and execution (code functionality) levels. Many non-CS students overestimated their abilities, highlighting a gap in mastery. Students with programming experience performed better, underscoring the importance of hands-on training. These findings suggest interventions for non-CS students are needed to gain experience in programming and to bridge the gap between perceived and actual skills. Future research should focus on discipline-specific curricula and long-term studies to ensure that all students develop the essential CT skills for the digital era.

This pilot study investigated students' perceptions of visual progress bar and on-demand hints in a CS1 course. Students valued both feedback systems, with more confident students preferring on-demand hints for direct problem-solving support. Progress bars were consistently perceived as beneficial across varying confidence levels.

Self-explanation promotes active learning by having students articulate their conceptual understanding in their own words. This study investigates whether the timing of self-explanations (before vs. after solving an exercise) relates to performance in a flipped, second-year computer organization course. Although students who self-explained before the exercises achieved higher course marks, the difference was not significant. Still, these findings suggest that early self-explanation may better prepare students for problem-solving when learning new concepts.

Self-explanation deepens understanding by giving learners an opportunity to reflect on what they are learning in a structured way. However, many students struggle to engage in it effectively. We investigate whether large language models (LLMs) can scaffold self-explanations in a flipped computer organization course. In an A/B test, one group used a fixed prompt to compare their explanations with an expert's, while another engaged in an interactive dialogue with an LLM to identify gaps. Although the overall quality of the explanation did not differ significantly between conditions, some students (non-native English speakers and women) reported greater comfort and perceived value when using the LLM.

Adaptive Experimentation is one of the most promising approaches to support complex decision-making in learning experience design and delivery. This paper reports on our experience with a real-world, multi-experimental evaluation of an adaptive experimentation platform within the XPRIZE Digital Learning Challenge framework, and summarizes data-driven lessons learned and best practices for Adaptive Experimentation in education. We outline key scenarios of the applicability of platform-supported experiments and reflect on lessons learned from this two-year project, focusing on implications relevant to platform developers, researchers, practitioners, and policy stakeholders to integrate Adaptive Experiments in real-world courses.

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.

Creating a supportive community in introductory programming courses is vital to student success, yet forums meant to facilitate this can cause stress due to social comparison. According to social identity theory, students are more likely to engage and feel a sense of belonging when they perceive connections with their peers. This study investigates whether peer-modeled posts that simulate students exhibiting desirable engagement behavior can reduce feelings of isolation and foster social connection among students. We introduced curated posts modeling expected student behavior -- covering content, providing emotional support, and offering study tips -- into Q&A forums for two introductory computing courses. These posts were inserted using different student accounts. Surveys and forum data were analyzed to measure the impact on students' feelings of isolation. Students responded positively to the seeded posts, reporting a significant reduction in feelings of isolation. Notably, women reported feeling less isolated after seeing the posts more than men, and many students reported feeling relieved that other students had the same worries and concerns as them. Seeding peer-modeled posts can significantly reduce student isolation and foster a greater sense of belonging in competitive academic contexts. However, future work may explore alternative delivery mechanisms, such as instructor posts framed as ''questions from last year,'' to determine if they can achieve similar effects.

Leveraging a Large Language Model (LLM) for personalized learning in computing education is promising, yet cloud-based LLMs pose risks around data security and privacy. To address these concerns, we developed and deployed a locally stored Small Language Model (SLM) utilizing Retrieval-Augmented Generation (RAG) methods to support computing students' learning. Previous work has demonstrated that SLMs can match or surpass popular LLMs (gpt-3.5-turbo and gpt-4-32k) in handling conversational data from a CS1 course. We deployed SLMs with RAG (SLM + RAG) in a large course with more than 250 active students, fielding nearly 2,000 student questions, while evaluating data privacy, scalability, and feasibility of local deployments. This paper provides a comprehensive guide for deploying SLM + RAG systems, detailing model selection, vector database choice, embedding methods, and pipeline frameworks. We share practical insights from our deployment, including scalability concerns, accuracy versus context length trade-offs, guardrails and hallucination reduction, as well as data privacy maintenance. We address the "Impossible Triangle" in RAG systems, which states that achieving high accuracy, short context length, and low time consumption simultaneously is not feasible. Furthermore, our novel RAG framework, Intelligence Concentration (IC), categorizes information into multiple layers of abstraction within Milvus collections mitigating trade-offs and enabling educational assistants to deliver more relevant and personalized responses to students quickly.

Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper’s hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document’s navigational structure. Drawing insights from a formative study on academic reading practices, we introduce Treereader, a novel language model-augmented paper reader. Treereader decomposes papers into an interactive tree structure where each section is initially represented by an LLM-generated concise summary, with underlying details accessible on demand. This design allows users to quickly grasp core ideas, selectively explore sections of interest, and verify summaries against the source text. A user study was conducted to evaluate Treereader’s impact on reading efficiency and comprehension. Treereader provides a more focused and efficient way to navigate and understand complex academic literature by bridging hierarchical summarization with interactive exploration.

This research evaluates voice-based self-explanations as a pedagogical tool in preparation for lectures, assesses user preferences between voice and text, and derives design insights. We report two studies: Study 1, a quasi-experimental field study, with 247 participants divided into voice-based (N = 83), text-based (N = 81), and choice (N = 83) conditions. Study 2 uses semi-structured interviews (N = 16) to explore perceptions of the interaction paradigms in-depth. Results from the first study revealed a general preference for text, though voice users produced longer responses and more topic-related keywords. Over time, the preference for voice increased among students, from 10% to 46%, when given a choice. Study 2 suggested that factors like social presence contribute to hesitance toward voice-based explanations, with a cognitive load, self-confidence, and performance anxiety also influencing medium preferences. Our findings highlight design recommendations and demonstrate the potential of voice-based self-explanations in educational settings, indicating that mixed interfaces might better meet diverse needs.

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.

Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers’ flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly modify variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text fields for rapid in-place comparisons using mouse-over interactions on a popup toolbar. Our user study with 12 writers shows that ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances user perceptions of the revision process (d = 2.41, p < 0.001) compared to a popular baseline workflow, and provides insights into how writers explore variations using LLMs.

Due to the recent improvements and wide availability of Large Language Models (LLMs), they have posed a serious threat to academic integrity in education. Modern LLM-generated text detectors attempt to combat the problem by offering educators with services to assess whether some text is LLM-generated. In this work, we have collected 124 submissions from computer science students before the creation of ChatGPT. We then generated 40 ChatGPT submissions. We used this data to evaluate eight publicly-available LLM-generated text detectors through the measures of accuracy, false positives, and resilience. Our results find that Copy Leaks is the most accurate LLM-generated text detector, G PTKit is the best LLM-generated text detector to reduce false positives, and GLTR is the most resilient LLM-generated text detector. We note that all LLM-generated text detectors are less accurate with code, other languages (aside from English), and after the use of paraphrasing tools.

Motivation: Q&A forums are a critical resource for supporting students in large educational environments, yet students often perceive these forums as stressful and report discomfort in participating visibly, especially in classes that are large and have students with varying levels of prior programming experience (PE). Method: We divided students in a CS1 Q&A forum into smaller, homogenous groups based on their PE. We use a mixed-methods approach to compare data from this experience to data from a setting where all students shared a single, large Q&A forum (a “mixed” setting). We quantitatively analyze measures of student engagement and use an open-ended qualitative approach to examine responses about student experience on the forums. This approach helps us identify the motivation behind student decisions to participate in visible or non-visible ways and to evaluate their alignment with theoretical frameworks. Results: In the mixed setting, students frequently use anonymity, with students without PE using anonymity more than students with PE and women using anonymity more than men. In contrast, in the homogenous groups, novices used anonymity less than novices in the mixed setting, while the students in higher-experience groups tended to use it more. We also observe a reduced anonymity usage among women in the homogenous experience groups, suggesting that PE plays a critical role in the observed gender disparities in forum participation. The qualitative analysis provides additional evidence that social status issues and confidence may explain these behavioral patterns. Conclusion: This study highlights the potential benefits and consequences of grouping students by experience. Homogenous PE groups foster increased student comfort and engagement within the Q&A forum for students with less experience, but students with more experience are exposed to more perceived status threats. We discuss how these results align with the theories we used to design the homogenous group setting. This exploration contributes to a deeper understanding of the underlying dynamics shaping student behavior in online learning communities. Educators and platform designers can use these lessons to more effectively create inclusive environments that accommodate diverse student needs and preferences.

Leveraging Large Language Models (LLMs) for personalized learning and support is becoming a promising tool in computing education. AI Assistants can help students with programming, problem-solving, converse with them to clarify course content, explain error messages to help with debugging, and much more. However, using cloud-based LLMs poses risks around data security, privacy, but also control of the overarching system.

The field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices.

Self-reflection on learning experiences constitutes a fundamental cognitive process, essential for consolidating knowledge and enhancing learning efficacy. However, traditional methods to facilitate reflection often face challenges in personalization, immediacy of feedback, engagement, and scalability. Integration of Large Language Models (LLMs) into the reflection process could mitigate these limitations. In this paper, we conducted two randomized field experiments in undergraduate computer science courses to investigate the potential of LLMs to help students engage in post-lesson reflection. In the first experiment (N=145), students completed a take-home assignment with the support of an LLM assistant; half of these students were then provided access to an LLM designed to facilitate self-reflection. The results indicated that the students assigned to LLM-guided reflection reported somewhat increased self-confidence compared to peers in a no-reflection control and a non-significant trend towards higher scores on a later assessment. Thematic analysis of students' interactions with the LLM showed that the LLM often affirmed the student's understanding, expanded on the student's reflection, and prompted additional reflection; these behaviors suggest ways LLM-interaction might facilitate reflection. In the second experiment (N=112), we evaluated the impact of LLM-guided self-reflection against other scalable reflection methods, such as questionnaire-based activities and review of key lecture slides, after assignment. Our findings suggest that the students in the questionnaire and LLM-based reflection groups performed equally well and better than those who were only exposed to lecture slides, according to their scores on a proctored exam two weeks later on the same subject matter. These results underscore the utility of LLM-guided reflection and questionnaire-based activities in improving learning outcomes. Our work highlights that focusing solely on the accuracy of LLMs can overlook their potential to enhance metacognitive skills through practices such as self-reflection. We discuss the implications of our research for the learning-at-scale community, highlighting the potential of LLMs to enhance learning experiences through personalized, engaging, and scalable reflection practices.

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.

Asking for help (help-seeking) is a recognized and effective problem-solving strategy. This study investigates students' interaction with on-demand hints (automated hints requested by students) and assesses their impact on learning progress. We conducted an A/B experiment in a third-year computer science database course, offering hints for selected SQL problems with different hint designs. We collected data on students' code submissions, grades, and hint requests, and we administered a survey to gather feedback and gauge student perception of the hints. Many students accessed hints immediately without attempting the problem first, often requesting multiple hints in quick succession. While students perceived the hints to be valuable, we did not detect an impact on student problem-solving. These insights could inform future studies on the possible impact of students' attitudes toward hints, and how different types of hints might impact uptake and perception of hints.

In contrast with studies that have identified why students commit academic offences, many educators are familiar with the excuse that an accused student did not know the behavior counted as dishonest. Given the variations in policy and the ways collaboration and code sharing occur in professional and hobbyist spaces, this might be plausible. Mismatches between students' conceptions of academic honesty and course policy can have major consequences, from being kicked out of programs to being too nervous to study with peers. In this work, we investigate what students understand about academic integrity in computer science courses and if there are differences based on university, country, demographic, or online versus in-person courses. We present a study that surveys undergraduate computer science students (N = 1,011) at three universities (Australia, Canada, and the United States of America). The results show that all three institutions take academic integrity seriously, and their students are aware of its importance, but confusion on what is covered under the policies is common. Interestingly, the results also show that course instructors play a huge role as to what students perceive to be a violation of the academic integrity policy at their institution. By understanding student's perspectives on academic integrity, educators can better develop policies and practices that reduce inadvertent and mistaken violations of academic integrity policies.

While there are multiple standards bodies that define characteristics of high-quality, there are limited guidelines on conducting equity-enabling research, particularly in the context of high quality and in computing education. As part of an ACM ITiCSE Working Group in 2023, we engaged in a concept analysis and structured literature review to identify high-impact practices for conducting both high-quality and equity-enabling education research. As a result of this work, we produced a set of guidelines across each major phase of research that integrates characteristics of high-quality education research with those that are necessary for producing research that is designed to honor and meet the needs of various subgroups of learners. Special emphasis is given to the role that the researcher plays in shaping the research based upon how the researcher's lived experiences, perspectives, and training influences their work. During this special session, we will review each set of guidelines and engage attendees in reflection and discussion of them and how they can use the guidelines to enhance their education research.

This study explores links between attributes of computing students, such as prior programming experience (PE) and gender, with expectations for success and the perception of challenges. Using Expectancy-Value Theory (EVT), we investigate their major intentions and the impact of these factors post-CS1. Data was gathered using surveys at the beginning and end of an introductory programming course, focusing on demographics, expectations of success, and perceptions of challenges. Application status for the computing major was also recorded. Our results revealed that men and students with PE generally perceived greater potential for success and reported facing fewer challenges. In contrast, women and students without PE more often indicated concerns about intellectual ability and perceived challenges less positively. Notably, while gender appears in the preceding results, an intersectional analysis indicates that PE is the central factor. PE is also linked to persistence in the field of computing. Our results further highlight the importance of providing students with opportunities to develop experience, as it can help shape their expectations, perceived challenges, and retention in computing.

Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited. Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. However, the challenge lies not only in establishing the efficacy of LLMs but also in discerning the nuances of interaction between learners and these models, which impact learners' engagement and results. We conducted a formative study in an undergraduate computer science classroom (N=145) and a controlled experiment on Prolific (N=356) to explore the impact of four pedagogically informed guidance strategies on the learners' performance, confidence and trust in LLMs. Direct LLM answers marginally improved performance, while refining student solutions fostered trust. Structured guidance reduced random queries as well as instances of students copy-pasting assignment questions to the LLM. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.

Recent work has confirmed that computing students experience the Imposter Phenomenon (IP) at higher rates than reported in other disciplines. However, no work has examined what aspects of the university computing experience might lead to a higher rate of IP experiences. We aim to illustrate the IP experiences students have, identify common sources of these experiences, and document the effects of these experiences and how students respond to them. We asked undergraduate students to share recent experiences that illustrate their experiences with the IP. We conducted an inductive thematic analysis on these open-ended responses, resulting in a set of inter-connected themes. A significant fraction of students related stories about making comparisons with peers or observing peer behaviour that made them question their abilities. Students also spoke about holding unrealistic expectations learned from their peers or imposed by the environment. These experiences may be particularly acute for minority-affiliated students who may come to feel they do not belong. Ultimately, these IP experiences can lead to a loss of motivation or a cycle of failure that leads students to leave computing. The central role social comparisons play in IP experiences suggests that it is particularly important to foster communities where opportunities for comparison are reduced and where realistic expectations are explicitly set.

Generating self-explanations has been identified as a successful strategy in helping learners engage with course content and organize what they learn in a structured format. While typing an explanation may allow more structure and formality, explaining by voice can be more natural and help free cognitive resources to focus on learning goals and understanding concepts. As we investigated the effects and students' perceptions of using voice or text to self-explain new course concepts, we failed to find a tool that would meet our needs. We present our work in designing and developing VoiceEx, a submission courseware that allows text and voice input to collect data in both mediums. VoiceEx was created to support a self-explanations intervention for computer science students; however, given its features and the advantages of being able to collect spoken responses, it can be used in a variety of environments. Future refinement of this tool includes artificial intelligence features to better guide students' submissions.

In this poster, we present a pilot study investigating the impact of the medium used for self-explanation on students' performance outcomes in a databases course. We did not see notable differences in student performance based on the medium they used for self-explanation. We also note that while most students prefer using text to submit self-explanations, their preferences may differ when they use voice.

To investigate and identify promising practices in equitable K-12 computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being conducted and the lack of equity-focused research.

Q&A forums are widely used in large classes to provide scalable support. In addition to offering students a space to ask questions, these forums aim to create a community and promote engagement. Prior literature suggests that the way students participate in Q&A forums varies and that most students do not actively post questions or engage in discussions. Students may display different participation behaviours depending on their comfort levels in the class. This paper investigates students' use of a Q&A forum in a CS1 course. We also analyze student opinions about the forum to explain the observed behaviour, focusing on students' lack of visible participation (lurking, anonymity, private posting). We analyzed forum data collected in a CS1 course across two consecutive years and invited students to complete a survey about perspectives on their forum usage. Despite a small cohort of highly engaged students, we confirmed that most students do not actively read or post on the forum. We discuss students' reasons for the low level of engagement and barriers to participating visibly. Common reasons include fearing a lack of knowledge and repercussions from being visible to the student community.

Previous work has reported on the advantageous effects of prior experience in CS1, but it remains unclear whether these effects fade over a sequence of introductory programming courses. Furthermore, while student perceptions suggest that prior experience remains important, studies have reported that a student's expectation of their performance is a more accurate predictor of outcome. We aim to confirm if prior experience (formal or informal) provides short-term and long-term advantages in computing courses or if the advantage fades. Furthermore, we explore whether the expectation of performance is a more accurate predictor of student success than informal and formal prior experience. To explore these questions, we deployed surveys in a CS1 course to gauge students' level of prior experience in programming, prediction of final exam grades, and self-efficacy to succeed in university. Grades from CS1 and CS2 were also collected. We observed a persistent (1-letter grade) gap between the performance of students with no prior experience and those with any experience, but we did not observe a noteworthy gap when comparing student performance based on formal or informal experience. We also observed differences in self-efficacy and retention rates between different levels of prior experience. Lastly, we confirm that success in CS1 can be better reflected and predicted by some controllable factors, such as students' perceptions of ability.

Drawing inspiration from machine learning and experimentation in product development at leading technology companies, we explore how adaptive experimentation might help in continuous course improvement. In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students. We discuss an example side-by-side comparison of traditional and adaptive experimentation of self-explanation prompts in online homework problems in a CS1 course. This provides the first step in exploring the future of how this approach can help bridge research and practice in continuous course improvement.

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.

Digital experiments can be used in CSedu to test hypotheses about interventions and conditions' efficacy (or inefficacy). This workshop will discuss and deconstruct the design process and analysis for various experiments conducted in CS1. E.g., experiments testing which explanations students find helpful, which emails get them to start homework early, or which webpages effectively encourage and motivate students. This workshop teaches participants how to conduct, interpret, and analyze adaptive field experiments. These adaptive experiments employ machine learning algorithms to analyze experiments during deployment and dynamically shift the allocation of arms/conditions to give future students better conditions more rapidly. Adaptive field experiments can accelerate scientific discovery by enabling more complex experimental designs and increasing statistical power by phasing conditions in and out more efficiently. The workshop is supported by a 5-year NSF grant to build software tools and a digital community, gathering instructors, domain scientists and methodologists to teach them how to run adaptive experiments. The methodological focus includes understanding: (1) which algorithms are best for adaptive experiments that meet domain scientists' needs in specific experimental designs and data sets; (2) which hypothesis tests and Bayesian analyses to choose. Software companies use these innovative methodologies extensively to continuously improve product design. This workshop demonstrates how the same methods can be used in CSedu to improve research rigor and accelerate educational research implementation, ultimately improving student outcomes.

Motivation Despite the widespread belief that computing practitioners frequently experience the Imposter Phenomenon (IP), little formal work has measured the prevalence of IP in the computing community despite its negative effect on achievement.

With the widespread usage of mobile devices, users can now choose to provide input through voice or text. As researchers frequently ask students open-ended questions, we want to explore a natural mode to obtain better feedback in surveys. This study details a preliminary study demonstrating the importance of allowing students to choose between voice or text input to respond to surveys. A survey with several open-ended questions was deployed in a CS1 course. Correlations between the gender of the respondent and their method of responding were evaluated. We found that voice responses tended to be longer and preferred more by females relative to male students.

Accurate indoor positioning has been the subject of investigation for many years. Modern smartphones have a wide suite of internal sensors that allow us to measure different signals. However, traditional positioning methods, such as GPS, typically fail when measurements are taken indoors. Many different solutions have been proposed that rely on various data inputs, including Wi-Fi and camera input. Some proposed methods have used auxiliary data inputs such as BLE Beacons. However, any auxiliary input would require additional infrastructure to purchase and maintain, increasing expenses. This paper explores a variety of indoor positioning techniques that do not require any additional infrastructure beyond what is typically found in a commercial environment. This research explores, implements, and measures, through standardized tests, Wi-Fi RSSI, RTT, and marker-based trilateration, as well as, fingerprinting with two separate machine learning models, and also tests an implementation of PoseNet. It compares and contrasts the various methods, categorizing them according to a proposed set of criteria for evaluating a commercially deployable indoor positioning solution. The paper closes with a brief summary of the techniques that were studied and proposes investigation into various related topics and improvements, as well as future directions.

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.

When student programs are compared for similarity, certain segments of code are always sure to be similar. Some of these segments are boilerplate code -- public static void main String [] args and the like -- and some will be code that was provided to students as part of the assessment specification. The purpose of this working group is to explore what other code is expected to be reasonably common in student assessments, and should therefore be excluded from similarity checking. The answers will clearly vary with programming language, and perhaps with level of assessment item. Working group members will collect assessment submissions from their own or their colleagues' students, and it is hoped that these submissions will together encompass a wide variety of assessment tasks in a wide variety of programming languages. The working group aims to deliver clear guidelines as to what code can reasonably be excluded from automatic code similarity detection in various circumstances. It also aims to deliver a summary of what sort of code lecturers tend to provide for students when setting an assigned task, and why they provide that code.