A Concept Inventory (CI) is an assessment to measure student conceptual understanding of a particular topic. This article presents the results of a CI for basic data structures (BDSI) that has been previously shown to have strong evidence for validity. The goal of this work is to help researchers or instructors who administer the BDSI in their own courses to better understand their results. In support of this goal, we discuss our findings for each question of the CI using data gathered from 1,963 students across seven institutions.

Computer Science now has a number of validated instruments available for measuring student knowledge or interest in computing including the Second CS1 Assessment (SCS1), The Basic Data Structures Inventory (BDSI), the Computing Attitudes Survey (CAS), and the Digital Logic CI. These instruments can be used by instructors to assess their students and/or their own teaching. They can also be used by researchers to measure students' learning or attitudes. The goal of this BOF is to help instructors and researchers gain a better understanding of how to use these instruments, whether that be to get started in education research, to compare student learning across terms/curricular revisions, or just to learn more about student misconceptions. We will begin by discussing the available instruments, their purpose, and how to obtain them. Then we will open the discussion to the group on what they would like to measure, how these instruments might work for them, and how to best employ them with their students.

Computer Science now has a number of validated instruments available for measuring student knowledge or interest in computing (SCS1, BDSI, CAS, Digital Logic CI, etc.). But when and how should instructors and researchers use these instruments? In this BOF, we will begin by discussing the available instruments, their purpose, and how to obtain them. Then we will open the discussion to the group on what they would like to measure, how these instruments might work for them, and how to best employ them with their students.

Concept inventories (CIs) allow researchers and practitioners to measure student conceptual learning within a course or topic area. While they have enabled meaningful pedagogical change in other disciplines, there are relatively few CIs in computer science. In this paper, we report on our experiences as recent developers of a CI for basic data structures. We discuss each step along the route to a CI and offer tips based on what we have learned. We encourage others to create CIs, and we hope that this paper will serve as a practical guide through the process.

A Concept Inventory (CI) is a validated assessment to measure student conceptual understanding of a particular topic. This work presents a CI for Basic Data Structures (BDSI) and the process by which the CI was designed and validated. We discuss: 1) the collection of faculty opinions from diverse institutions on what belongs on the instrument, 2) a series of interviews with students to identify their conceptions and misconceptions of the content, 3) an iterative design process of developing draft questions, conducting interviews with students to ensure the questions on the instrument are interpreted properly, and collecting faculty feedback on the questions themselves, and 4) a statistical evaluation of final versions of the instrument to ensure its internal validity. We also provide initial results from pilot runs of the CI.

To be effective instructors and CS education researchers, we must identify and understand student difficulties surrounding core computing topics. This study examines student difficulties with the basic data structures commonly found in CS2 courses. Initial exploration of student thinking began with think-aloud interviews with students. These interviews centered on open-ended questions that were iteratively improved upon based on analysis of interview transcripts. The revised open-ended questions were then posed to 249 students during an end-of-term final exam study session. Using the explanations and justifications included by students, responses to the questions were coded and summarized. This work characterizes the difficulties revealed by student responses, and provides details of their prevalence among the examined student population.

Establishing learning goals for a course allows instructors to design course content to address those goals, helps students to focus their learning appropriately, and enables researchers to assess learning of those goals. In this work, we propose six learning goals for a topic prevalent in CS2 courses: Basic Data Structures. These learning goals arise from reviewing several CS2 courses at a variety of institutions, surveying faculty experts who commonly teach CS2, and meeting and working closely with these experts. We outline our process for creating learning goals, identify important topics underlying these goals, and provide examples of how the goals developed on the path to consensus. We also document that the term "CS2" does not have a unified interpretation within the CS education community and describe how this hurdle influenced our decision to focus on Basic Data Structures.

The lower-division CS curriculum at the University of California, Berkeley includes a version of CS 2 that is intended to introduce students to Java as well as data structures and programming methodology. Some students in the course already have Java experience. In one course offering, students without previous Java experience received final grades that were 0.27 standard deviations below their peers who already had some Java experience (d=0.27, p<0.05). In a subsequent offering, the instructor adopted course policies and teaching strategies that made student collaboration more frequent in hopes that students without Java experience could learn from their peers with Java experience. In this highly-collaborative offering, there were no statistically significant differences in average final grades between students with and without Java experience (d=0.12, p<0.1). A smaller percentage of students dropped the highly-collaborative offering than the less-collaborative offering. This decrease in attrition was most notable for female students, from 37 percent to 5 percent.

This special session is devoted to identifying the CS education papers of the 20th century that have had the greatest influence on our practice of CS education today. The point is not primarily to produce the list of papers; rather it is to derive criteria, principles, and practices for identifying valuable contributions to CS education. This will provide a basis for establishing of awards or other recognition for influential contributions to CS education; it may also clarify the criteria for reviewing all papers. Invited participants will identify influential papers and their criteria for selecting them; the audience will also be encouraged to nominate papers and propose selection criteria. The results of this session will be communicated to the SIGCSE leadership, who will decide whether, and how, to implement any actual awards.

What should you do on the first day of your objects-first CS1 course? If you have been struggling with beginning an objects first course this special session will give you hints about how to avoid overload and make a smooth and effective start. The presenters are skilled at introducing objects to novices in interesting ways that captures the interest of the students and readies them for programming. These are gentle approaches that present deep ideas to the inexperienced. How can a course be built around objects without information overload? This session will address the opening gambit in this interesting game.

We present several interesting applications for the Pattern-Annotated Course Tool (PACT) and pedagogical design patterns in the process of curriculum design. PACT is a visual editor in which content designers can create visual representations of their courses and annotate them with references to educational theory in the form of pedagogical patterns. Each usage scenario illustrates the opportunities for learning that PACT, the annotation process, and the artifacts that users create present to experts, novices, and everyone in between. Finally, we take an in-depth look at one ongoing course annotation being crafted by an experienced curriculum designer and examine his interesting findings from the process. An analysis of these discoveries demonstrates the utility of PACT in the curriculum design process.

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This paper describes our efforts to develop a new lab-based course format for computer science instruction. Building on learning science research, we created a flexible new technology platform to support students and their instructor as they participated in this new form of instruction. Students work collaboratively on Web-based activities while the instructor interacts with students in a tutorial role. The paper describes our system in detail, outlines the organization of the course that used it, and reviews and evaluates the pilot results. We then discuss the implications for computer science instruction and research in higher education.

Empirical skills are playing an increasingly important role in the computing profession and our society. In addition to being problem-solvers and designers/engineers, computer scientists must also be capable experimenters in order to develop, test, and evaluate complex hardware and software systems. The widespread use of computers as tools for interdisciplinary research also demands a strong grounding in the scientific method.This panel is designed to promote discussion about innovative methods for integrating empirical skills within the traditional computer science curriculum. Each panelist will define a set of core empirical concepts and skills that they see as essential to computer scientists, with a brief rationale for each. In conjunction, they will describe classroom practices that serve to demonstrate the key concepts and/or develop skills they have identified. Sufficient time will be allocated for discussion and contributions from the audience.

We hope to alert attendees of this panel to a number of aspects of CS education research:• previous work that provides good models for future research;• current projects and results;• areas that deserve more inquiry;• questions for which research is unlikely at the moment to yield useful information.The panel is aimed at people who don't need to be convinced about the value of CS education research, but who perhaps are unfamiliar with what's happening or how they might get involved themselves.

A new case study for the AP curriculum, based on a marine biology simulation program will be released for the 2000-2001 academic year. The case study highlights linear and two-dimensional data structures, object interaction, object-oriented design, and discrete simulation. This seminar will introduce the new case study to AP teachers and other interested CS educators, and will discuss how it can be integrated into the AP (or CS 1 and CS 2) curricula.

In this paper, we review research results relating to the use of software design patterns and advocate knowledge integration perspectives on programming instruction. We also describe a number of components of instruction that would lead to more effective use of patterns and better integration of programming knowledge in general.

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We and others have created case studies to engage students in the process of designing solutions to complex programming problems. Case studies involve learners in activities of expert programmers such as identifying decisions, justifying choices among alternatives, and evaluating the consequences of these choices. Case studies improve computer programming courses by emphasizing the process rather than the product of problem solving. This paper describes specific uses of case studies in freshman and sophomore-level courses.

Various arguments have been made for the use of functional programming, This paper presents some additional arguments for teaching the discipline of functional programming in an introductory programming course.In particular, functional programming .focuses students on the goal of solving the problem at hand rather than distracting them with a multitude of extraneous details; .encourages the formation of templates, i.e. stereotypical chunks of programming knowledge; .provides computer experiences that reinforce and deepen students' understanding of the abstract concept of function; .leads to code that is concise and elegant.We illustrate these points and others with some of our favorite functional programming examples.Examples are given in LISP and Pascal.