Training teachers to teach high-quality computer science courses is an important step towards increasing participation in computer science. To meet this need, our Research Practitioner Partnership (RPP) was formed with the primary goal of creating a comprehensive, graduate-level program to train in-service CS teachers with little to no prior CS coursework. Since its formation, the RPP has iteratively designed, implemented, and evaluated a five-course program to improve participants' knowledge of computer science content and pedagogy while allowing them to earn the state's grades 5-12 computer science endorsement. Our program has successfully scaled from a single-site pilot to a truly statewide, multi-site program, emphasizing educator-based, standards-based, and cohort-based instruction. Currently, the RPP serves 250 in-service participants.

The use of subgoal labels in introduction to programming classrooms has been shown to improve student performance, learning, retention, and reduce students' drop out rates. However, creating and adding subgoal labels to programming assignments is often hard to articulate and very time-intensive for instructors. In Computing Education Research, Large Language Models (LLMs) have been widely used to generate human-like outputs such as worked examples and source code. In this work, we explore whether ChatGPT could be used to generate high-quality and appropriate subgoal labels in two programming curricula. Our qualitative data analysis suggests that LLMs can assist instructors in creating subgoal labels in their classrooms, opening up directions to empower students' learning experience in programming classrooms.

The session will present the final report of an NSF sponsored workshop tasked with creating a vision for computing education for the next 15 years. Within several broad themes identified, the panelists were asked to probe further into where the field should be heading and what difficult questions we need to tackle. We will use the final report as a basis for discussion with the attendees and ask them the same hard questions we asked ourselves when writing this report to see how these questions shape their views of where computing education needs to be in the next 15 years.

In our previous research we found that teaching novice programmers introductory programming in Java using subgoal labels led to deeper knowledge [2] and increased persistence for students potentially at risk of dropping out or failing their first undergraduate course in CS [3]. Subgoals are an instructional tool that is designed to bridge the gap between novices and experts, i.e., students and instructors. Experts often have difficulty explaining concepts at a level that novices understand because they have automatized much low-level knowledge. The task analysis used to identify subgoals makes this knowledge explicit. Subgoals are often expressed to students through subgoal-labeled worked examples that explicitly state the conceptual knowledge expressed through examples. This instructional design of examples allows students to see past superficial details of the example to the structural problem-solving procedure being exemplified [3].

Programming is a complex task that requires the development of many skills including knowledge of syntax, problem decomposition, algorithm development, and debugging. Code-writing activities are commonly used to help students develop these skills, but the difficulty of writing code from a blank page can overwhelm many novices. Parsons problems offer a simpler alternative to writing code by providing scrambled code blocks that must be placed in the correct order to solve a problem. The extensive literature on Parsons problems documents numerous benefits to using them as both formative and summative assessments. These include more efficient learning, the possibility to dynamically adapt to learner needs, and more reliable grading. Despite these positive findings, further research is needed in order to draw broader inferences. Most work has been conducted at single institutions under unique conditions that are not easily replicated, and some prior studies have been inconclusive or had limitations that affected data validity. To address this, we propose a multi-institutional and multi-national study of the effectiveness of Parsons problems for novice programmers. We will focus on introductory programming courses (CS0/1/2) that use Java, Python, and C/C++ as these are the most common teaching languages. The working group will collaborate to refine the scope, methodology and research questions, and contribute to data collection and analysis.

Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in many disciplines, including computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, 1712231, 1927906, 2110156, 2111578), subgoal labels have been identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes) and an e-book has been created on the Runestone platform to enable students to complete practice problems using the subgoals. This workshop will introduce participants to the materials and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include nearly 50 worked examples and 300 practice problems that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will leave with access to the e-book containing worked examples and practice problems for common topics in an imperative Java-based CS1 and will also engage in an activity where they create an example for their own course using subgoal labels.

The session will present an overview of findings of a recently funded NSF workshop that set out to examine the pressing issues for computing education research for the next 15 years. Based on dialogs for scholars working in this area, the workshop participants developed a series of themes and topics that they felt should become the focus of computing education research efforts for the next 15 years. Main themes that emerged and will be discussed in this session include: diversity, equity, inclusion, ethics, broadening participation, teaching, learning, K-12, research to practice, computing's connection to other fields, and computing education research disciplinary issues. The session will focus on interesting research questions from each of these areas that are ripe to be explored as well as enablers and blockers to the progress of this work.

In this special session, we present tools and resources created from educational theory to make teaching and scholarship easier and more effective. Included in these resources are CS Teachers Association, CSforAll, CS Teaching Tips, CT Pathways Toolkit, EngageCSEdu, CS Assessments Hub, Cybersecurity, and CS Ed Research Resource Center. These tools/resources provide assistance at the district and school level (administrators, curriculum designers), classroom level (teachers), and researcher/evaluator level. In addition to these overviews, we will provide information on where to find them and will leave ample time for questions from attendees.

Computing has, for many years, been one of the least demographically diverse STEM fields, particularly in terms of women's participation [2] and those from minoritized racial and ethnic groups. In the case of higher education, one of the most powerful sites of intervention is the classroom. The last decade has seen a proliferation of research exploring new teaching techniques and course sequencing and their effect on the retention of students who have historically been excluded from computing. This research suggests interventions and practices that can affect the inclusiveness of the computer science classroom and potentially improve learning outcomes for all students. But research needs to be translated into practice, and practices need to be taken up in real classrooms. The goal of this working group (WG) is to conduct a systemic "state-of-the-art" review of recent empirical studies of teaching practices that have some explicit test of the impact on women (or other under-represented groups) in computing. The WG will produce an annotated bibliography and a report that distills the research into specific, actionable practices.

The ACM Special Interest Group on Computer Science Education (SIGCSE) is one of the oldest and largest SIGs, and the SIGCSE Technical Symposium is the oldest and largest of the four SIGCSE conferences. However, the vast majority of Symposium attendees and contributors are from the United States. Because SIGCSE is an international organization, this lack of geographic diversity and representation is troubling because it may stifle collaboration, membership, professional development, and dissemination of research, and have many other adverse effects.

In order to fully prepare high-quality teachers to deliver on the promise of CS for All, teacher preparation programs must ensure teacher candidates have mastered both content and the pedagogy to teach that content well. University CS departments are well-positioned to teach the content, but "methods courses" are an important source of pedagogical and pedagogical content knowledge for future computer science teachers. This panel will describe a variety of approaches: Colleges of Education or CS departments; in-person, hybrid, or online course delivery; and standalone "CS Methods" courses or CS integrated in broader methods courses. Each panelist will describe their course. The moderator will elicit common features between the courses as well as the strengths and weaknesses between different approaches.

Broadening participation in computing touches every aspect of the undergraduate experience. This special session highlights the initiatives undertaken by NCWIT Academic Alliance members who are working to broaden participation in computing. A mix of 3-minute lightning talks, review of resources, and Q&A will provide attendees opportunities to create connections and grapple with implementation issues at their institution. This special session is a reprise of a well-reviewed session from the NCWIT Summit, and will introduce strategies for admission to major, curriculum, pedagogy, teaching assistant selection, and undergraduate research.

The AP CS A course, taught in high schools in the U.S. and around the globe, is designed to cover the same material as an introductory programming course at the collegiate level. Every May, tens of thousands of high school students sit the AP Computer Science A (AP CS A) exam, testing their introductory programming knowledge and skills. So how would your introductory students perform on this exam? In this special session attendees will receive access to AP CS A multiple choice and free response (coding) questions and have the opportunity to judge whether or not their students could answer the questions correctly. In addition, the presenters will share how each question is mapped to specific Learning Outcomes and expected student skills, much like what must be done for ABET accreditation efforts. Attendees can expect to have their Java programming skills challenged, learn ways to construct exams to meet course learning outcomes, and development of rubrics that reinforce measurement of concept mastery.

The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). Programming education in K-12 is growing, and many international initiatives are attempting to increase participation, including curricular initiatives like Computer Science Principles and non-profit organizations like Code.org. Given that subgoal learning is designed to help students with no prior knowledge, we designed and implemented subgoals in the introduction to programming unit in Code.org's Computer Science Principles course. The redesigned unit includes subgoal-oriented instruction and subgoal-themed pre-written comments that students could add to their programming activities. To evaluate efficacy, we compared behaviors and performance of students who received the redesigned subgoal unit to those receiving the original unit. We found that students who learned with subgoals performed better on problem-solving questions but not knowledge-based questions and wrote more in open-ended response questions, including a practice Performance Task for the AP exam. Moreover, at least one-third of subgoal students continued to use the subgoal comments after the subgoal-oriented instruction had been faded, suggesting that they found them useful. Survey data from the teachers suggested that students who struggled with the concepts found the subgoals most useful. Implications for future designs are discussed.

This work extends previous research on subgoal labeled instructions by examining their effect across a semester-long, Java-based CS1 course. Across four quizzes, students were asked to explain in plain English the process that they would use to solve a programming problem. In this mixed methods study, we used the SOLO taxonomy to categorize student responses about problem-solving processes and compare students who learned with subgoal labels to those who did not. The use of the SOLO taxonomy classification allows us to look deeper than the mere correctness of answers to focus on the quality of the answers produced in terms of completeness of relevant concepts and explanation of relationships among concepts. Students who learned with subgoals produced higher-rated answers in terms of complexity and quality on three of four quizzes. Also, they were three times more likely to discuss issues of data type on a question about assignments and expressions than students who did not learn with subgoal labeling. This suggests that the use of subgoal labeling enabled students to gain a deeper and more complex understanding of the material presented in the course.

Subgoal learning has improved student problem-solving performance in programming, but it has been tested for only one-to-two hours of instruction at a time. Our work pioneers implementing subgoal learning throughout an entire introductory programming course. In this paper we discuss the protocol that we used to identify subgoals for core programming procedures, present the subgoal labels created for the course, and outline the subgoal-labeled instructional materials that were designed for a Java-based course. To examine the effect of subgoal labeled materials on student performance in the course, we compared quiz and exam grades between students who learned using subgoal labels and those who learned using conventional materials. Initial results indicate that learning with subgoals improves performance on early applications of concepts. Moreover, variance in performance was lower and persistence in the course was higher for students who learned with subgoals compared to those who learned with conventional materials, suggesting that learning with subgoal labels may uniquely benefit students who would normally receive low grades or dropout of the course.

No abstract available.

Advanced Placement (AP) Computer Science A (CS A) is an introductory high school Java course equivalent to a CS1 course at the undergraduate level. Over 66,000 high school students sat for the AP CS A end-of-course examination in May 2018. The exam contains four free-response questions (FRQs) where students are to implement methods or a full class according to specifications. Each June, these exams are graded at the AP CS A Reading. This workshop provides a behind-the-scenes look at the Reading, during which the FRQs are read (scored) by more than 325 college faculty and high school AP teachers. You will learn how the FRQs are scored, the roles of the various Reading participants, and the steps to ensure consistent grading. You will engage in an AP Reading-style training by applying a rubric based on a past free-response question and then participate in a mock reading using the rubric. After attending this workshop, you will be able to understand the role and responsibilities of the Chief Reader, Question Leader, and Table Leader at the AP Reading; develop strategies for creating rubrics for code questions; consistently apply rubrics for free-response questions and similar assignments; and apply strategies to prepare students for the free-response portion of the AP Exam or to similar style questions in an introductory college-level course.

How has CS education changed since SIGCSE first began in 1968? This panel of presenters, with 135 years of teaching instruction between them, will remind some attendees of what used to be and educate newer members on what life was like pre-internet. Topics to be discussed include changes in content, presentation medium, equipment and environment, students and audience, and pedagogy. Audience members will be engaged through peer instruction questions during and between topic discussions and may use their cell phones or laptops to respond to questions. This panel will allow our community to acknowledge the diversity and experience in our organization and celebrate our heritage. We believe old-timers who wish to reminisce and young, new instructors who are interested in a historical aspect will be interested in the session.

Computational Thinking Bins are stand alone, individual boxes, each containing an activity for groups of students that teaches a computing concept.We have a devised a system that has allowed us to create an initial set, test the set, continually improve and add to our set. We currently use these bins in outreach events for middle and high school students. As we have shared this resource with K-12 teachers, many have expressed an interest in acquiring their own set. In this paper we will share our experience throughout the process, introduce the bins, and explain how you can create your own set.

Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in disciplines both in and out of computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, #1712231), subgoal labels have been identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes). This workshop will introduce participants to the materials and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include over 100 worked examples and practice problem pairs that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels.

Subgoal labels are function-based instructional explanations that describe the problem-solving steps to the learner, highlighting the solution process. There is strong evidence that the use of subgoal labels within worked examples improves student learning in other STEM fields. Initial research shows that using subgoal labels within computer science improves student learning, but this has only been tested using a single programming concept (indefinite loops) with text-based programming languages. The proposers are currently expanding subgoal labels to the main programming concepts taught in an introductory programming course using an imperative programming language. In this BOF we seek to uncover tacit knowledge that programming instructors have in order to develop instructional materials that bridge the gap between students, who are CS novices, and instructors, who are CS experts, to improve learning for students who are under-prepared for or struggle in CS1. We will be seeking feedback on the selection of programming topics to be covered, the defined subgoals for those topics and the worked examples created for instructional purposes.

The research in both cognitive load theory and multimedia principles for learning indicates presenting information using both diagrams and accompanying audio explanations yields better learning performance than using diagrams with text explanations. While this is a common practice in introductory programming courses, often called "live coding," it has yet to be empirically tested. This paper reports on an experiment to determine if auditory explanations of code result in improved learning performance over written explanations. Students were shown videos explaining short code segments one of three ways: text only explanations, auditory only explanations, or both text and auditory explanations, thus replicating experiments from other domains. The results from this study do not support the findings from other disciplines and we offer explanations for why this may be the case.

In this workshop participants will receive an overview of teaching practices in computer science that research indicates are effective. While the field of computer science education is young, it has uncovered several teaching practices that can be adopted by instructors that can improve both the retention and performance of students. These evidence based teaching practices include active learning techniques such as peer instruction and prior-knowledge activities, pair programming, and use of subgoal labels. Participants will experience firsthand many of these techniques and will be provided with resources on where to find more information, including the original research papers, on each technique. If you want to attend a workshop that will have an immediate impact in your class -- attend this one. The workshop will be interactive, engaging, and show you how to incorporate teaching practices that are empirically proven to provide benefits. You are guaranteed to leave with a list of many freely available resources and ideas to use in your next class. You will also have the opportunity to "ask the experts" as the authors of many of these research papers will be leading that session of the workshop.

Parents are often responsible for finding, selecting, and facilitating their children's out-of-school learning experiences. One might expect that the recent surge in online educational tools and the vast online network of information about informal learning would make this easier for all parents. Instead, the increase in these free, accessible resources is contributing to an inequality of use between children from lower and higher socio-economic status (SES). Through over 60 interviews with a diverse group of parents, we explored parents' ability to find learning opportunities and their role in facilitating educational experiences for their children. We identified differences in the use of online social networks in finding learning opportunities for their children based on SES. Building upon these findings, we conducted a national survey in partnership with ACT, an educational testing services organization, to understand if these differences were generalizable to and consistent among a broader audience.

Several countries are trying to provide access to computing education for all secondary students. However, there are not enough teachers who are prepared to teach computer science. Interactive electronic books (ebooks) are a promising approach for providing low-cost professional development in computer science. Over the last four years, our research group has been conducting design-based research by iteratively developing and testing versions of a teacher ebook to help secondary teachers with no programming experience learn to teach an introductory programming course. The interactive elements in the ebook were designed based on research results from educational psychology and are intended to make learning more efficient and effective. Our goals for this effort are to increase teachers' knowledge of computer science concepts and to improve teachers' confidence in their ability to teach computer science. In this paper we summarize our previous work and report on a large-scale study of version two of the teacher ebook. We also recommend several design principles for interactive ebooks for computing teachers based on feedback from teachers, log file analyses, and randomized controlled studies.

A recent study about the effectiveness of subgoal labeling in an introductory computer science programming course both supported previous research and produced some puzzling results. In this study, we replicate the experiment with a different student population to determine if the results are repeatable. We also gave the experimental task to students in a follow-on course to explore if they had indeed mastered the programming concept. We found that the previous puzzling results were repeated. In addition, for the novice programmers, we found a statistically significant difference in performance based on whether the student had previous programming courses in high school. However, this performance difference disappears in a follow-on course after all students have taken an introductory computer science programming course. The results of this study have implications for how quickly students are evaluated for mastery of knowledge and how we group students in introductory programming courses.

The number of PhD students researching Computer Science Education worldwide is growing. This is an organized opportunity for all those attending the largest Computer Science Education Conference to meet one another. All attendees will be invited to introduce themselves along with their institution and focus of research. They may also propose the most important question to be answered by the BOF. After introductions the attendees will have the opportunity to discuss research goals, job opportunities and career trajectories, future conference and publication plans, and even compare advisor stories. Undergraduates and masters students interested in a CS Ed PhD are also encouraged to attend and ask questions. Attendees will determine the best way to stay connected and everyone will be given the opportunity to opt-in for future communications.

We report on a study that used subgoal labels to teach students how to write while loops with a Parsons problem learning assessment. Subgoal labels were used to aid learning of programming while not overloading students' cognitive abilities. We wanted to compare giving learners subgoal labels versus asking learners to generate subgoal labels. As an assessment for learning we asked students to solve a Parsons problem -- to place code segments in the correct order. We found that students who were given subgoal labels performed statistically better than the groups that did not receive subgoal labels or were asked to generate subgoal labels. We conclude that a low cognitive load assessment, Parsons problems, can be more sensitive to student learning gains than traditional code generation problems.

Educational psychology findings indicate that active processing (such as self-testing) is more effective for learning than passive reading or even rereading. Electronic books (ebooks) can include much more than static pictures and text. Ebooks can promote better learning by increasing the reader's interaction with the material through multi-modal learning supports, worked examples, and low cognitive load practice activities. For example, multiple choice questions with immediate feedback can help identify misconceptions and gaps in knowledge. Parsons problems, which are mixed up code segments that have to be put in the correct order, require learners to think about the order of the statements in a solution without having to worry about syntax errors. Our research group has been applying concepts from educational psychology to make learning from ebooks more effective and efficient. This paper reports on an observational study and log file analysis on the use of an ebook that incorporates interactive activities. We provide evidence that learners engaged in the interactive activities, but used some types of activities more than others. We also found evidence that learners encountered some "desirable difficulties" which can improve learning. This descriptive study informs a research agenda to improve the quality of instruction in computing education.

My research explores how learning computer science, specifically programming, differs from learning math or science in relation to educational psychological principles. I have replicated well established experiments from the science and math domains by using instructional design techniques that minimize the cognitive load imposed on the learner. Instead of receiving the expected results confirming that the educational psychology principles also apply to computing, I received unexpected results contrary to the original hypotheses which indicate that merely adapting these principles to a new domain is not enough. I seek to understand what differences exist in learning programming, as compared to the other problem solving domains that explain the confusing experimental results I obtained.

Recent empirical results suggest that the instructional material used to teach computing may actually overload students' cognitive abilities. Better designed materials may enhance learning by reducing unnecessary load. Subgoal labels have been shown to be effective at reducing the cognitive load during problem solving in both mathematics and science. Until now, subgoal labels have been given to students to learn passively. We report on a study to determine if giving learners subgoal labels is more or less effective than asking learners to generate subgoal labels within an introductory CS programming task. The answers are mixed and depend on other features of the instructional materials. We found that student performance gains did not replicate as expected in the introductory CS task for those who were given subgoal labels. Computer science may require different kinds of problem-solving or may generate different cognitive demands than mathematics or science.

Flipped classrooms are a new twist on an old idea: homework. The basic formula is simple: do the prep work before coming to class and come to class ready to discuss that work, do an activity to reinforce what you learned, or even take a quiz on the reading or research that was assigned. But as with all approaches to teaching, the reality is never that simple. This panel will report the experiences of four "flippers" and explore the pros and cons of those experiences. Educators who are considering flipping all or part of their courses will gain insight into how to do so to their and, more importantly, their students'-advantage, while those who have used this technique may gain new insights into approaches that might help them be more successful if they faced any issues similar to those of the panelists.

A student's capacity to learn a concept is directly related to how much cognitive load is used to comprehend the material. The central problem identified by Cognitive Load Theory is that learning is impaired when the total amount of processing requirements exceeds the limited capacity of working memory. Instruction can impose three different types of cognitive load on a student's working memory: intrinsic load, extraneous load, and germane load. Since working memory is a fixed size, instructional material should be designed to minimize the extraneous and intrinsic loads in order to increase the amount of memory available for the germane load. This will improve learning. To effectively design instruction to minimize cognitive load we must be able to measure the specific load components for any pedagogical intervention. This paper reports on a study that adapts a previously developed instrument to measure cognitive load. We report on the adaptation of the instrument to a new discipline, introductory computer science, and the results of measuring the cognitive load factors of specific lectures. We discuss the implications for the ability to measure specific cognitive load components and use of the tool in future studies.

Gamification is the buzzword for adding gaming elements such as points or badges to learning experiences to make them more engaging and to increase motivation. In this paper we explore how Khan Academy has incorporated gaming elements into its CS learning platform. By mapping the literature on motivational processes to popular games we critically analyze how successful Khan Academy is at gamifying their site.

My research seeks to adopt existing knowledge from educational psychology and instructional design and apply it to the field of computer science education in an effort to make learning programming more time efficient. Specifically I will use cognitive load theory to improve the efficiency of learning to program. I have identified my first two studies: identifying the most appropriate modality for code segment explanations and determining the benefits of worked examples in learning programming.

The Disciplinary Commons (DC) is a model of teacher professional development that encourages members of the group to reflect upon their teaching practices, develop a community, and, more broadly, to become more scholarly about their teaching. The DC involves a series of monthly meetings where university faculty members examine their course in detail while producing a course portfolio. Evaluation of the early DC's suggests that they successfully created a sense of community and sharing among the participants. We have adapted the original model to a new audience, high school computing teachers. The adapted model maintains the key aspects of the original model while adding two new, important goals for this new audience: improving recruitment and creating community. The high school teacher audience particularly needed strategies for recruiting students and was in greater need of community. We present evaluation evidence suggesting that we achieved the design goals in a replicable model, including a substantial increase (over 300%) in recruiting students.

It is important for students to be able to select and apply the appropriate data structure for the problem to be solved. Testing this knowledge on exams can be difficult, however. We examined 59 data structures final exams and found only 36 that contained questions involving the application of data structures. To promote assessment of this knowledge in the data structures course, we present a framework for classifying apply exam questions, with illustrations from the exams collected. We then show how a number of questions can be developed by varying a single rich apply question along the dimensions of this framework

In this paper, we describe our experience in supporting high school CS teachers by building a local community through the Disciplinary Commons for Computing Educators (DCCE) project. The DCCE project is an effort to explore ways of supporting these CS teachers through the creation of a local community and by promoting teacher reflection. DCCE achieved this goal through an academic-year-long program where a cohort of CS teachers engaged in collaborative portfolio creation and peer observation of classroom teaching. We describe the design of the DCCE activities and present preliminary results from initial evaluations. Our short-term evaluations indicate that this project was successful in creating a supportive community, promoting teacher reflection, and advancing change in teaching practices among a group of computing educators.

Is there consensus on what students should learn in CS2? Should they learn to use data structures, understand their specific implementation details, or both? Finally, has the computing education community's answer to the second question changed over time? In this paper, we begin to explore these questions based on an analysis of a key artifact instructors use to assess their students' performance: their final exams. Specifically, we look at two CS2 concepts as covered in those exams: stacks and hashtables.

Fun, interesting, hard, rewarding, and challenging: these are the most frequent responses of 697 students from five institutions at the end of a first programming course. Student experience with introductory programming courses is of interest to the computing education community, especially due to continued decreases in enrollments in computing degree programs. In this study, we explore one direct approach to document students' initial attitudinal experiences with programming by asking them to complete an open-ended question at the end of a first programming course. Based on content-analysis of students' responses, we find that nearly 50% of responses were positive in nature, there is significant difference in the responses of majors and non-majors, and that response characteristics correlate to earned grade in the course. We present preliminary, but inconclusive evidence on the impact of context (e.g., gaming or media computation) in a first programming course. Finally, we propose a multiple-choice question based on the most common student responses for large-scale deployment in computing courses and identify key contextual information that will inform future analysis of that data.

This paper considers how gaming has been infused into the computing curriculum of institutions in the United States. To increase motivation of students and improve retention, many programs have begun using gaming in their introductory courses, as upper level electives, or as separate degree programs. The authors review the current use of gaming within curricula and analyze the content of game development degree programs. Finally, the authors describe plans at their institution to incorporate gaming throughout the computing curriculum and present initial results.

Data sharing is common, and sometimes even required, in other disciplines. Creating a mechanism for data sharing in computer science education research will benefit both individual researchers and the community. While it is easy to say that data sharing is desirable, it is much more difficult to make it a practical reality.

No abstract available.

This paper describes a multi-institutional study that used categorization exercises (known as constrained card sorts) to investigate gender differences in graduating computer science students' learning and perceptions of programming concepts. Our results show that female subjects had significantly less pre-college programming experience than their male counterparts. However, for both males and females, we found no correlation between previous experience and success in the major, as measured by computer science grade point average at graduation. Data also indicated that, by the time students completed their introductory courses, females reported nearly equal levels of mastery as males of the programming concepts. Furthermore, females generally considered the programming concepts to be no more difficult than did the men.

Research on learning suggests the importance of helping students organize their knowledge around meaningful patterns of information. This paper reports on a multi-institutional study to investigate how senior computer science majors articulate and organize their knowledge of programming concepts using a card-sorting technique adopted from knowledge acquisition. We show that card-sorts are an effective means of eliciting students' knowledge structures and suggest they can also be used to help students organize their knowledge throughout the curriculum.

Abstract: This paper describes a multi-institutional study that used a repeated single-criterion card sort to investigate graduating computer science students' knowledge of programming concepts. The study seeks to improve computer science instruction by gaining insight into how graduating students retain and assimilate introductory programming knowledge into their broader understanding of the discipline. A total of 291 card sorts was elicited from 65 undergraduate students in their final year of study at eight colleges and universities throughout the USA. To fully exploit the rich qualitative and quantitative aspects of the card sort data, an integrative analysis process was used that combined content analysis with two measures, normalized minimum spanning tree and edit distance, both developed specifically to analyze card sort data.

Abstract: This paper presents a case study of the use of a repeated single-criterion card sort with an unusually large, diverse participant group. The study, whose goal was to elicit novice programmers' knowledge of programming concepts, involved over 20 researchers from four continents and 276 participants drawn from 20 different institutions. In this paper we present the design of the study and the unexpected result that there were few discernible systematic differences in the population. The study was one of the activities of the National Science Foundation funded Bootstrapping Research in Computer Science Education project (2003).

The current debate about the teaching of data structures is hampered because, as a community, we usually debate specifics about data structure implementations and libraries, when the real level of disagreement remains implicit -- the intent behind our teaching. This paper presents a phenomenographic study of the intent of CS educators for teaching data structures in CS2. Based on interviews with Computer Science educators and analysis of CS literature, we identified five categories of intent: developing transferable thinking, improving students' programming skills, knowing "what's under the hood", knowledge of software libraries, and component thinking. The CS community needs to first debate at the level of these categories before moving to more specific issues. This study also serves as an example of how phenomenographic analysis can be used to inform debate on syllabus design in general.