Building computer science instruction for everyone requires understanding and supporting learner motivations beyond software development. Some learners value understanding the applications of code more than writing code from scratch (e.g., end-user programmers or conversational programmers). However, introductory programming instruction often emphasizes fundamental code-writing skills without clear applications. Our goal is to provide an overview of authentic computing applications to students from a variety of disciplines, including those who may not be interested in code-writing skills or may not have prior experience in programming. To this end, we designed and tested three interactive tutorials in the lab sessions of an introductory computing course for non-CS majors. Instead of teaching programming from scratch, these purpose-first tutorials highlight the purpose and basics of authentic programming libraries and tools, such as SQL queries, web-scraping libraries in Python, and HTTP requests. They are short (<1 hour), require minimal prior knowledge, and can be easily included in existing courses. The learning objectives of our activities are to help students understand why and when these tools are useful and to encourage them to explore these tools in problems that they might encounter in their disciplines. We observed that the activities were beneficial in improving confidence and providing a conceptual understanding of several tools. We present lessons learned to inform the design of similar activities for teaching computing as general knowledge to diverse audiences.

Pedagogical approaches focusing on stereotypical code solutions, known as programming plans, can increase problem-solving ability and motivate diverse learners. However, plan-focused pedagogies are rarely used beyond introductory programming. Our formative study (N=10 educators) showed that identifying plans is a tedious process. To advance plan-focused pedagogies in application-focused domains, we created an LLM-powered pipeline that automates the effortful parts of educators' plan identification process by providing use-case-driven program examples and candidate plans. In design workshops (N=7 educators), we identified design goals to maximize instructors' efficiency in plan identification by optimizing interaction with this LLM-generated content. Our resulting tool, PLAID, enables instructors to access a corpus of relevant programs to inspire plan identification, compare code snippets to assist plan refinement, and facilitates them in structuring code snippets into plans. We evaluated PLAID in a within-subjects user study (N=12 educators) and found that PLAID led to lower cognitive demand and increased productivity compared to the state-of-the-art. Educators found PLAID beneficial for generating instructional material. Thus, our findings suggest that human-in-the-loop approaches hold promise for supporting plan-focused pedagogies at scale.

Open-ended code-writing exercises are commonly used in large-scale introductory programming courses, as they can be autograded against test cases. However, code writing requires many skills at once, from planning out a solution to applying the intricacies of syntax. As autograding only evaluates code correctness, feedback addressing each of these skills separately cannot be provided. In this work, we explore methods to detect which high-level patterns (i.e. programming plans) have been used in a submission, so learners can receive feedback on planning skills even when their code is not completely correct. Our preliminary results show that LLMs with few-shot prompting can detect the use of programming plans in 95% of correct and 86% of partially correct submissions. Incorporating LLMs into grading of open-ended programming exercises can enable more fine-grained feedback to students, even in cases where their code does not compile due to other errors.

Despite the proliferation of studies on broadening participation in computing, individuals with disabilities remain relatively less examined. In this study, we use large-scale data to examine the patterns of persistence and major-switching among students with disabilities in undergraduate computing education. Our findings suggest that disabilities discourage students to enter or remain in computing at the intersection of their race and gender. Our study provides insights on the challenges and opportunities students with disabilities face in undergraduate computing education, with implications for increasing their participation and retention.

Background and Context: A major difference between expert and novice programmers is the ability to recognize and apply common and meaningful patterns in code. Previous works have attempted to identify these patterns as programming plans, such as counting or filtering the items of a collection. However, these efforts primarily relied on expert opinions and yielded many varied sets of plans. No methods have been applied to evaluate these various programming plans as far as their alignment with novices’ cognitive development.

Background and Context. Instruction in most introductory computing courses is typically focused on how to program. However, non-majors who take computing courses have a diverse set of desired endpoints. One group of non-majors are the conversational programmers, who do not want to program in their career but enroll in computing courses to improve their ability to communicate about technical topics and their competitiveness in the job market. Research suggests that these learners need an alternate instructional approach, but so far, conversational programmers in higher educational contexts have only been studied in a limited number of small-scale studies. Objectives. To inform curriculum design for conversational programmers at the university level, we (a) examine the prevalence of conversational programmers among non-majors and their characteristics, (b) understand conversational programmers’ desired learning goals and classroom activities, and (c) investigate factors associated with these learners’ motivation to learn computing. Methods. We designed a survey based on Expectancy-Value Theory and prior work about conversational programmers. We collected responses from randomly sampled non-major students at a large public university, and we analyzed the survey data with descriptive and inferential statistics. Findings. We found that conversational programmers are the largest proportion of non-majors in our sample, both overall and across historically underrepresented groups in CS. We replicated prior findings of low self-efficacy for programming of conversational programmers. We found that conversational programmers’ motivation for taking more computing courses is paradoxically driven more by their interest in computing than its utility, despite their general lack of enjoyment in computing. We validate a previously proposed set of conversational programmers’ learning goals and show that they value employment-oriented learning goals over those focused on conversations. Implications. Our results suggest that addressing the needs of conversational programmers can contribute to broadening participation in computing. Our study motivates a learner-centered curriculum design that could address conversational programmers’ learning needs by enhancing their self-efficacy and interests prior to focusing on conversational goals.

Office hour utilization in computer science courses can spike near deadlines, producing long wait times, frustrated students, and over-worked staff. To address this problem, a large CS1 course implemented a split deadlines policy. Students were randomly divided into two groups with staggered release and due dates. Each group had the same amount of time to complete assignments, but the number of students with each due date was reduced by half. Our study evaluates the effectiveness of this policy. We measure office hour utilization and staff efficiency near deadlines, examine the policy's impact on student performance, and investigate student perception of the policy's fairness and effectiveness. Overall we found that the split deadline policy increased office hour efficiency, resulted in no significant difference in performance between groups, and was considered fair and effective by most students. Our experience report includes reflections and student feedback indicating how to implement and further improve similar policies.

Previous research has shown that students encounter difficulties when learning database systems and their corresponding languages. Researchers have categorized these challenges into syntax and semantic errors and have identified common error types and overall learning obstacles among students. However, most existing studies have primarily focused on quantitatively assessing students‘ overall performance in an aggregated manner’ which may overlook valuable insights into individual-level knowledge transfer. In this study, we scrutinized over 250,000 submissions to query language programming assignments, their corresponding error messages, and the performance data of 702 students who took a database course in the Fall 2022 semester at the University of Illinois Urbana-Champaign to gain a comprehensive overview of each student's performance. We followed each student's progress in semantic and syntax errors across three query languages to determine their overall learning experience and whether knowledge transfer had occurred. Consequently, we discovered that many students may still encounter difficulties when transferring their knowledge from one language to another, despite having already learned and practiced the same abstract data operation concepts in one language. On the other hand, the majority of students were able to reduce syntax errors through practice in one language, but the rate of improvement varied among individuals. This study seeks to investigate two key aspects: the potential transfer of abstract data operation concepts among different database languages, and the possibility of a decrease in syntax errors through consistent practice within a single query language.

Structured Query Language (SQL) is an essential skill to acquire for those who interact with databases, such as researchers, developers, and people involved in businesses. However, the challenges that these users face while learning SQL requires further research. In particular, the types of errors that students encounter on various assignment types or under exam conditions are an area that we are interested in to determine an optimal arrangement of coursework materials for improved learning. In this paper, we analyze 156,513 student SQL submissions to homework assignments, collaborative assignments, and exams of the Database Systems course available to 730 upper-level undergraduate and graduate students offered in the Fall 2022 semester at the University of Illinois Urbana-Champaign. We look at the ratio of syntax and semantic errors, and correct submissions for each of these assignment problem types as well as the most frequent syntax error codes. We visualize our data findings and draw recommendations for future coursework arrangements from the comparisons between the assignment types for a more effective acquisition of SQL as a skill. We found that although students most commonly encountered syntax error codes 1064 and 1054 regardless of the assignment type, they made more syntax errors (and fewer semantic errors) on exam problems compared with homework and collaborative assignment problems. We recommend instructors place a higher emphasis on non-timed SQL programming problems, targeted syntax drills during instruction, and syntax support during exams.

Training the growing number of Computer Science Education (CSEd) PhD students is a pressing concern for our community. To meet the needs of our CSEd PhD students at University of Illinois Urbana-Champaign, we have developed a new course designed to strengthen students’ foundation in relevant fields. Through a collaborative process, we developed a reading list that covers the educational theory and perspectives that most inform our own work, as well as concepts that prepare our graduates to engage with the broader CSEd community.

Background and context. “Explain in Plain English” (EiPE) questions ask students to explain the high-level purpose of code, requiring them to understand the macrostructure of the program’s intent. A lot is known about techniques that experts use to comprehend code, but less is known about how we should teach novices to develop this capability.

Domain-specific programming plans can support application-focused learning. To move towards an effective and explicit process for domain-specific plan identification, we propose metrics to evaluate plan quality and techniques to discover plans.

There is a growing number of Ph.D. graduates whose research focuses on computing education, and they are significantly fueling the growth of the computing education research community. As more computing education Ph.D. graduates weigh their options on the job market, they are increasingly entering postdoctoral positions. For example, seven Computing Innovation Fellows positions over the last two years have been awarded to computing education researchers. Research shows that postdoctoral researchers have positive, cascading effects on research labs. However, despite the critical role they play in the research ecosystem, and the growing importance of postdoctoral positions in career development, there are no support structures for postdoctoral researchers in the computing education research community. This Birds-of-a-Feather session is an organized opportunity for postdoctoral researchers and those interested in postdoctoral positions to connect with one another, share postdoc experiences, and generate best practices. Attendees will have opportunities to discuss research goals and activities, career trajectories and opportunities, future conference and publication plans, pathways for future collaborations, and advice for the postdoc experience.

As the number of conversational programmers grows, computing educators are increasingly tasked with a paradox: to teach programming to people who want to communicate effectively about the internals of software, but not write code themselves. Designing instruction for conversational programmers is particularly challenging because their learning goals are not well understood, and few strategies exist for teaching to their needs. To address these gaps, we analyze the research on programming learning goals of conversational programmers from survey and interview studies of this population. We identify a major theme from these learners' goals: they often involve making connections between code's real-world purpose and various internal elements of software. To better understand the knowledge and skills conversational programmers require, we apply the Structure Behavior Function framework to compare their learning goals to those of aspiring professional developers. Finally, we argue that instructional strategies for conversational programmers require a focus on high-level program behavior that is not typically supported in introductory programming courses.

Conversational programmers want to learn about code primarily to communicate with technical co-workers, not to develop software. However, existing instructional materials don’t meet the needs of conversational programmers because they prioritize syntax and semantics over concepts and applications. This mismatch results in feelings of failure and low self-efficacy. To motivate conversational programmers, we propose purpose-first programming, a new approach that focuses on learning a handful of domain-specific code patterns and assembling them to create authentic and useful programs. We report on the development of a purpose-first programming prototype that teaches five patterns in the domain of web scraping. We show that learning with purpose-first programming is motivating for conversational programmers because it engenders a feeling of success and aligns with these learners’ goals. Purpose-first programming learning enabled novice conversational programmers to complete scaffolded code writing, debugging, and explaining activities after only 30 minutes of instruction.

For many decades, educational communities, including computing education, have debated the value of telling students what they need to know (i.e., direct instruction) compared to guiding them to construct knowledge themselves (i.e., constructivism). Comparisons of these two instructional approaches have inconsistent results. Direct instruction can be more efficient for short-term performance but worse for retention and transfer. Constructivism can produce better retention and transfer, but this outcome is unreliable. To contribute to this debate, we propose a new theory to better explain these research results. Our theory, multiple conceptions theory, states that learners develop better conceptual knowledge when they are guided to compare multiple conceptions of a concept during instruction. To examine the validity of this theory, we used this lens to evaluate the literature for eight instructional techniques that guide learners to compare multiple conceptions, four from direct instruction (i.e., test-enhanced learning, erroneous examples, analogical reasoning, and refutation texts) and four from constructivism (i.e., productive failure, ambitious pedagogy, problem-based learning, and inquiry learning). We specifically searched for variations in the techniques that made them more or less successful, the mechanisms responsible, and how those mechanisms promote conceptual knowledge, which is critical for retention and transfer. To make the paper directly applicable to education, we propose instructional design principles based on the mechanisms that we identified. Moreover, we illustrate the theory by examining instructional techniques commonly used in computing education that compare multiple conceptions. Finally, we propose ways in which this theory can advance our instruction in computing and how computing education researchers can advance this general education theory.

Prior research has shown that sketching out a code trace on paper is correlated with higher scores on code reading problems. Why do students sometimes choose not to draw out a code trace, or if they do, choose a different sketching technique than their instructor has demonstrated? In this study, we interviewed 13 CS1 students retrospectively about their decisions to sketch and draw on a recent programming exam. When students do sketch, we find that their sketching choices do not always align with a strict execution of the notional machine. Sketching choices are driven by a search for a program's patterns, an attempt to create organizational structure among intermediate values, and the tracking of prior steps and results. When novices don't sketch, they often report that they've identified the goal that the code achieves. In either case, novices are searching for the functionality of code, rather than merely tracing its behavior.

Computing education research is built on the use of suitable methods within appropriate theoretical frameworks to provide guidance and solutions for our discipline, in a way that is rigorous and repeatable. However, the scale of theory covered extends well beyond the CS discipline and includes educational theory, behavioural psychology, statistics, economics, and game theory, among others. A computing education researcher's journey towards appropriate and discipline relevant theory can be challenging and, when a researcher has learned one area of theory, it can be easy to return to familiar theory, as it may not be clear what the next step could be. The periodic table is a visual arrangement of the elements to group like with like, providing insight into how families of elements will react. Could we do the same with learning theories located in the domain of computer science education, and would it be useful? The working group will identify and survey existing literature on relationships between key areas of theory in computing education, identify ways of organising these research areas to show how knowledge of one could assist another, and produce initial graphical representations of theory and their relationship groupings to assist researchers in understanding how computing theory is currently used in the discipline and what theories might become of interest.

CSin3 is a cohort-based, three-year computer science bachelor's degree program that has increased graduation rates of traditionally underrepresented computer science students. A collaborative effort between a community college and a public university, CSin3 provides a clear pathway for upward socio-economic mobility into the high-paying technology industry. CSin3 students are 90% from traditionally underrepresented groups, 80% first-generation, 32% female, and have a three-year graduation rate of 71%, compared to a 22% four-year graduation rate for traditional computer science students. Upon graduation, CSin3 students score similarly on a standardized exam of computer science knowledge as compared to traditional students who graduate in 4 years or more. The first graduates had a job placement rate of 78% within two months of graduation, including positions at large technology companies like Apple, Salesforce, and Uber. By implementing a cohort-based learning community, a pre-defined course pathway, just-in-time academic and administrative support, comprehensive financial aid, and a focus on 21st century skills, the CSin3 program has demonstrated promising results in addressing the capacity, cost, quality, and diversity challenges present in the technology industry.

Sketching out a code trace is a cognitive assistance for programmers, student and professional. Previous research (Lister et al. 2004) showed that students who sketch a trace on paper had greater success on code 'reading' problems involving loops, arrays, and conditionals. We replicated this finding, and developed further categories of student sketching strategies. Our results support previous findings that students who don't sketch on code reading problems have a lower success rate than students who do sketch. We found that students who sketch incomplete traces also have a low success rate, similar to students who don't sketch at all. We categorized sketching strategies on new problem types (code writing, code ordering, and code fixing) and find that different types of sketching are used on these problems, not always with increased success. We ground our results in a theory of sketching as a method for distributing cognition and as a demonstration of the process of the notional machine.