Professional dispositions are important in numerous fields, including computing. However, strategies to promote dispositions are not yet well understood. In this experience report, we share what students suggest to promote five dispositions: being collaborative, meticulous, persistent, responsive, and self-directed. To get a broad perspective of students' voices, we gathered feedback over two terms in four courses at three institutions.

Dispositions are valued by employers and promoted by recent computing curricular recommendations. Yet, fostering and assessing dispositions are not well understood. In a multi-institutional study, students were asked to assess their dispositions in terms of behaviors that were identified in prior literature for those dispositions, both at the start and the end of a term. During the term, instruments were used to have students reflect on their dispositions. The research questions of the study are: 1) Do students associate behaviors with the dispositions for which they were identified in prior work? 2) Does reflecting on dispositions change how students assess themselves in terms of the behaviors? and 3) Is there a difference between introductory and upper-level students in how they assess themselves in terms of the behaviors? The findings of the study are that 1) at least 60% of the students associated the behavior statements with the dispositions for which they were identified; 2) students lowered their self-assessment of some behaviors after reflecting on dispositions; and 3) upper-level students assessed themselves more positively on some behaviors than introductory students. These results support a model of development of dispositions in which self-assessment of behaviors associated with dispositions improves with academic level, but at each level, gets revised lower after reflection.

Lifelong learning is essential in computing, given the dynamic nature of the field. Employers and curricular reviewers recognize the value of being self-directed in support of becoming a lifelong learner. The ACM/IEEE-CS Computing Curricula 2020 report identifies self-directed as having elements of self-motivation, determination, and independence. Little is known, however, about how to cultivate this disposition in computing courses. The motivation of this study is to better understand what behaviors computing students believe are self-directed. This study's research questions are: 1) What do students describe as their self-directed practices in computing? and 2) What do students report are factors that prevent them from being self-directed? Assignments in five undergraduate computing courses from four institutions included prompts to elicit student's reflections on how they were self-directed (or not). Thematic content analysis using the constant comparative method produced eight categories of self-directed behaviors (utilizing external resources, learning necessary material, working independently, assessing oneself, planning ahead, applying useful techniques, completing the assigned work, and reviewing against expectations). Thematic analysis also resulted in five categories of factors that impeded the self-directed behavior (assignment structure, unsuccessful effort, self-sufficiency, insufficient motivation, and insufficient time). Understanding how students describe self-directedness can help educators design pedagogical and assessment approaches that facilitate self-directed student behaviors in the classroom.

This full research paper contributes to current work on fostering professional dispositions in computing and engineering education by identifying the categories of behaviors that students associate with dispositions while doing course work. Professional dispositions, demonstrated through desirable behaviors in the workplace, such as being persistent or self-directed, are explicitly sought by employers. Fostering dispositions among students has been identified in various curricular recommendations as an important goal. In prior work, the authors used reflection exercises, in which students were presented with the definition of a disposition and asked to answer an open-ended reflection prompt on how they applied the disposition in their own work. Thematic analysis of student responses to reflection exercises resulted in categories of behaviors that students associated with dispositions. In the work discussed in this paper, the authors used vignette exercises to collect and analyze similar data and gain further insight into behavioral categories and students' perceptions of dispositions. Vignettes include short scenarios that demonstrate the application of dispositions in real life. A vignette exercise involves students reading a vignette scenario, identifying the disposition demonstrated by the scenario, and answering the same open-ended reflection prompt as in the reflection exercises from the earlier studies. The research question for this study is: Which behavioral categories obtained from analyzing student responses to reflection exercises were confirmed using vignette exercises (and which were not confirmed), and which behavioral categories were refined? To answer this question, researchers from four different institutions of higher education collected data in multiple courses over two semesters. The student open-ended responses to vignettes were thematically analyzed to identify behavioral categories for four dispositions: collaborative, meticulous, persistent and self-directed. The ultimate goal of this work is to create classroom inter-ventions and learning activities that foster dispositions among students based on behavioral categories. This study supports this goal in two ways. It provides another iteration of behavioral category analysis and introduces vignettes to encourage students to reflect candidly and communicate clearly how they apply dispositions in terms of behaviors. The study results and their implications for fostering dispositions in a classroom setting are presented and discussed.

Dispositions, skills, and knowledge form the three components of competency-based education. Moreover, dispositions are considered crucial for students to succeed in the workplace. Few studies investigate how dispositions manifest in the form of observable behaviors, which causes challenges for both students and educators. Computing students, for example, may not understand what is expected of them, and how to achieve dispositions. This paper presents the results of a qualitative, multi-institutional study on students' understanding of the dispositions adaptable, persistent, self-directed, meticulous, and professional. Perceptions were gathered by asking for exemplary situations of students applying each of the five dispositions in the context of assignments within computing courses. Students who indicated they did not apply the disposition were asked to describe the hindering circumstances. The data was evaluated by using Mayring's content analysis technique, resulting in the development of deductive-inductive categories of observable behaviors reflecting the student's perspective. For meticulous and professional, new categories representing observable behaviors were developed. For adaptable, persistent, and self-directed, the authors confirmed and extended prior work. Moreover, factors hindering students in applying the investigated dispositions are identified. The resulting categories with observable student behaviors are an important step toward the operationalization of competency-based learning outcomes including dispositions. A common understanding of dispositions will also help with the design of new forms of instruction and measures to foster the application of dispositions in the context of computing education.

Vignettes are short stories along with a set of questions that engage the reader to comment on the story. Vignettes have been used in professional academic programs (e.g., teacher preparation and medical education), for professional development in various fields (e.g., teaching ethics in psychology and medicine), and in various research fields for data collection. In this work, vignettes are used to elicit students' understanding of dispositions in computing education. Professional dispositions enable behaviors that are valued in the workplace, such as adaptability or self-directedness. They are often explicitly stated in computing job postings. While the relevance of dispositions is widely recognized in the workplace, only recently have curricular guidelines for computing programs recognized professional dispositions as an integral part of competencies and as complementary to knowledge and skills. There is scarce literature on the use of vignettes in teaching undergraduate computing, or on how best to foster dispositions in students. In this project, four faculty from four diverse institutions in the U.S., along with three consulting experts, have collaborated to design and evaluate the use of vignettes in the classroom. This paper documents researchers' efforts to gain insights into students' perceptions of dispositions through the use of vignettes. Such insights may guide educators to identify pedagogical strategies for fostering dispositions among students. This paper presents an iterative process for vignette design with continuous review by researchers and focus group members. The vignettes in this study use stories of situations which demonstrate the application of a disposition, drawn from various fields and walks of life to represent diverse groups and experiences. Students are presented with the vignette story and asked to identify the disposition illustrated. To elicit students' understanding of dispositions in terms of their personal behaviors, students are asked to describe a situation in which they have experienced the disposition. Lessons learned in the design and use of vignettes are discussed.

Dispositions, along with skills and knowledge, form the three components of competency-based education. Moreover, studies have shown dispositions to be necessary for a successful career. However, unlike evidence-based teaching and learning approaches for knowledge acquisition and skill development, few studies focus on translating dispositions into observable behavioral patterns. An operationalization of dispositions, however, is crucial for students to understand and achieve respective learning outcomes in computing courses. This paper describes a multi-institutional study investigating students' understanding of dispositions in terms of their behaviors while completing coursework. Students in six computing courses at four different institutions filled out a survey describing an instance of applying each of the five surveyed dispositions (adaptable, collaborative, persistent, responsible, and self-directed) in the courses' assignments. The authors evaluated data by using Mayring's qualitative content analysis. The result was a coding scheme with categories summarizing students' concepts of dispositions and how they see themselves applying dispositions in the context of computing. These results are a first step in understanding dispositions in computing education and how they manifest in student behavior. This research has implications for educators developing new pedagogical approaches to promote and facilitate dispositions. Moreover, the operationalized behaviors constitute a starting point for new assessment strategies of dispositions.

In Fall 2021, a preliminary study was conducted to gain insight into students' perceptions of the importance of professional dispositions to their computing courses and career. Students filled out a pre-survey, post-assignment reflection exercises, and a post-survey. We found that 1) students rated dispositions as being maximally important for the course and their career on the pre- and post-surveys; 2) students rated dispositions not relevant to the course lower than those that were relevant; and 3) students rated their application of dispositions in course assignments lower than they had rated the importance of the dispositions for success in the course in the pre-survey.

Dispositions are cultivable behaviors desirable in the workplace. Examples of dispositions are being adaptable, meticulous, and self-directed. The eleven dispositions described in the CC2020 report should not be confused with the professional knowledge of computing topics, or with skills, including technical skills, along with cross-disciplinary skills such as critical thinking, problem-solving, teamwork, or communication. Dispositions, more inherent to human characteristics, identify personal qualities and behavioral patterns important for successful professional careers.

Competency-based learning has been a successful pedagogical approach for centuries, but only recently has it gained traction within computing education. Building on recent developments in the field, this working group will explore competency-based learning from practical considerations and show how it benefits computing. In particular, the group will identify existing computing competencies and provide a pathway to generate competencies usable in the field. The working group will also investigate appropriate assessment approaches, provide guidelines for evaluating student attainment, and show how accrediting agencies can use these techniques to assess the level of competence reflected in their standards and criteria. Recommendations from the working group report are intended to help practical computing education writ large.

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.

This Innovative Practice Full paper introduces PolyPy, a freely available web-platform that allows instructors to automate the creation of many different coding question instances from a single template. Interactive web platforms offer a way of engaging students with practice material while also providing them with real-time feedback. However, many of these platforms depend on manually pre-setting a static pool of questions and answers, which limits 1) the number of unique coding challenges presented to students, 2) the ability of instructors to dynamically customize challenges, and 3) the ability to recognize and adapt to individual student needs. PolyPy bypasses these limitations by providing a flexible and easy-to-use framework that lets instructors define question templates. These templates contain segments that can exhibit one of many pre-set values; we refer to these segments as “randomizable elements.” The question instances are generated (from the templates) on-the-fly every time a student requests a new question via the student interface. To verify the correctness of students' answers, PolyPy interprets these question instances and compares their output to the students' submissions in real-time. All students' attempts, both correct and incorrect, are stored and can be accessed by instructors; this information can be used to gain insight on the strengths and weaknesses of student comprehension. This paper presents the PolyPy system architecture and describes the student and instructor interfaces. It reports promising preliminary results of students' use of PolyPy along with feedback obtained regarding their learning experience.

Increasing gender balance in computing is widely recognized by academic institutions, industry, and government agencies as an imperative. This paper describes how providing opportunities for creative expression early on and throughout the four years of an undergraduate computing degree achieves this goal. Prior studies have demonstrated that opportunity for creative expression has a positive effect on recruitment and retention of women, while conversely, lack of opportunity for creative expression has a negative effect. We describe our approach, integrated into a four-course sequence combining computing and the arts, so that other institutions may consider adopting it. Results of a six-year longitudinal study show that these courses attract, retain, and graduate 46% female students, while the corresponding number in our ABET-accredited Computer Science curriculum is approximately 20%. The paper concludes with general observations and suggests directions for future action in CS curricula design.

Many research studies show students benefit from instructional practices that promote student interaction within the classroom. However, recent prominent reports suggest many instructors still rely on lecture as their dominant classroom activity. This paper reports on a survey of U.S. computer science teaching practices. Responses indicate many CS instructors use student-centered instructional practices but evidence suggests students would benefit from additional use of these practices. Twenty percent of CS instructors report "student-centered activities" are prominent in their classroom. CS instructors are more likely to use student-centered practices than those in other science disciplines but less likely to do so than colleagues in non-science fields. Female CS instructors are more likely to structure their courses around student-centered practices than their male colleagues.

Computing in the Arts (CITA) is an innovative, interdisciplinary curriculum model which integrates computer science and information technology with traditional art theory and practice. At the College of Charleston, implementation of an undergraduate CITA degree program resulted in an increase in the number of female and minority students pursuing computing-related degrees. [14] With the support of the National Science Foundation (DUE 1323605) and two partner institutions, we are building a community of educators who are creating innovative instructional materials that synthesize computing and the arts. Three faculty summer workshops (Wake Forest University in 2014, College of Charleston in 2015, and University of North Carolina at Asheville in 2016) involved over 70 computer science and arts faculty from across the U.S. What has emerged are various ways of synthesizing computer science and arts, including creation of new synthesis courses, modifications to traditional computing courses, development of new CITA-like curricula, design of CITA-like project experiences for undergrads, and other creative endeavors combining computer science techniques and traditional art practices and theory. During the session, we will discuss steps involved in moving forward and keeping this community growing. The session will involve audience participation, including exchanges between the session presenters and other audience members. The goal is to share our results, hear about results from other non-presenting colleagues, and to continue to grow the teaching of computer science and computational thinking to the arts and humanities masses, as well as to further enrich traditional computer science courses with creative applications, assignments, and projects.

Student-centered instructional practices structure a class so that students interact with each other, engage deeply with the content, and receive formative feedback. These evidence-based practices benefit all students but are particularly effective with underrepresented learners, including women and members of other minority groups. To what extent have computer science (CS) faculty embraced these strategies? We surveyed over 700 U.S. faculty to find out. Results suggest that female faculty, associate professors, and those teaching courses with enrollment above 80 students are more likely to use these student-centered practices. Across all responses, 20% of faculty use student--student interaction on a regular basis during class. In contrast, 38% of faculty rely on lectures for content delivery. Results were also compared with published data for other academic disciplines. CS faculty are less likely to use these practices compared to their non-STEM colleagues but more likely to use these practices compared to other STEM discipline faculty. Overall, CS faculty have adopted student-centered practices to some degree, but our community should strive for higher adoption rates to help as many students as possible learn and remain in computer science.

This paper reports on a study of goal-plans and errors produced by students who wrote recursive solutions for a binary tree operation. This work extends a previous study of difficulties CS2 students experienced while writing solutions on paper-based exams. In this study, participants solved the same recursive binary tree problem as part of a hands-on computer-based exam where students had access to an IDE and Java API documentation. Not surprisingly, students who took the computer-based exams were more successful than those who took the paper-based exams (58% vs. 17% correct solutions). However, even with the advantage of access to an IDE, documentation, and test cases, 42% of students taking the computer-based exam still made errors, indicating that students exhibit persistent errors even with support. The most common errors observed included incorrect calculations, missing method calls and missing and incorrect base cases.

This paper discusses the learning strategies adopted in a publically available, cloud-based learning environment, Learn2Mine, which facilitates student-progress as they solve data science programming problems. The learning system has been evaluated over three consecutive terms. Learn2Mine was initially introduced in an introductory course and pilot-tested for usability and effectiveness in Fall 2013. Students reported positive opinions on usability and effectiveness of the system in their completion of programming assignments. In Spring 2014, Learn2Mine was evaluated in an upper-level data mining course by comparing student submission rates and amount of programming accomplished for a group with access to the tool versus one without access. The group with access to Learn2Mine had an average assignment submission rate of 84%, while the group without had an average submission rate of only 48% (difference significant at p < 0.01). In Fall 2014, a controlled experiment was conducted in an introductory data science course: one group of students worked on a multi-part programming task with support of scaffolding and gamification as implemented in Learn2Mine, while the other section did not. The group with access performed significantly better in overall task completion (p < 0.01).

This study compares differences in students' ability to comprehend recursive and iterative programs by replicating a 1996 study. These studies are interesting for the following reasons: (1) there have been few studies on the comprehension of recursive programs, (2) there have been few studies of recursion that involve the use of data structures, (3) subjects were asked to read and comprehend code that manipulated linked lists recursively. In a 1996 study, subjects found a recursive version of a linked list search function easier to comprehend than an iterative version. The results of this study contradict these earlier findings; subjects in this study were equally likely to correctly describe the purpose of the search algorithm, regardless of iterative or recursive implementation. Consistent with the findings in the 1996 study, subjects in this study were more likely to correctly describe the copy task using the iterative version of this problem rather than the recursive version. Subjects who correctly described the purpose of the copy method wrote abstract summaries. Many of the subjects who did not recognize the purpose of the code, and who answered incorrectly, provided a line-by-line (partial) trace of the code. While revealing some understanding of the execution of the code, these subjects were generally not able to see the forest for the trees.

A goal-plan analysis was conducted to examine the variety of plans students use in writing a recursive method for an operation on a binary search tree. Students were asked to write a recursive method to count the nodes in a binary search tree with exactly one child. The problem incorporated two goals: traversing the tree and counting nodes with one child. Three traversal plans and four counting plans were observed in student solutions. Over half of the students used the arm's-length recursion plan, which involves testing for the base case before it is actually reached in order to avoid making recursive calls. This strategy creates complex and error prone code. Making students aware of arm's-length recursion may help them avoid introducing bugs into their recursive code. Although nearly all of the 18 participants demonstrated viable plans for solving the problem, their solutions contained a variety of errors: 55 total errors of 15 types. Students had particular difficulty with base cases, misplaced calculations, and missing method calls. Knowledge of these errors can be useful for instructors when developing lecture examples, identifying distractors for peer instruction multiple-choice questions and for designing homework exercises. Instructors can counteract these problems by providing a variety of recursive examples.

Learn2Mine is a cloud-based environment developed to support the teaching of data science. This paper discusses the architecture of Learn2Mine, the research that guided its development, and the pilot implementation and formative assessment of its use in teaching data science. Learn2Mine was pilot-tested in Fall 2013 in an introductory data science source. At the end of the term, a survey of students concerning their experiences with the environment was conducted. Quantitative analysis of survey data showed that student opinion about the usefulness of the tool for learning course content was positive. Through open-ended comments, students provided constructive feedback on how the system might be improved. To collect expert opinion on both the didactic and usability aspects of the Learn2Mine system, a number of experts were enlisted to try the system. Experts responded to a survey regarding criteria typically expected of instructional software, such as system usability and flexibility, as well as accuracy and organization of content. Overall, the responses from experts were extremely positive. A plan for further development of the system, based on these results, is presented along with information on the developers' plans for making the environment available for use at other institutions.

We describe Data Science, a four-year undergraduate program in predictive analytics, machine learning, and data mining implemented at the College of Charleston, Charleston, South Carolina, USA. We present a ten-year status report detailing the program's origins, successes, and challenges. Our experience demonstrates that education and training for big data concepts are possible and practical at the undergraduate level. The development of this program parallels the growing demand for finding utility in data sets and streaming data. The curriculum is a seventy-seven credit-hour program that has been successfully implemented in a liberal arts and sciences institution by the faculties of computer science and mathematics.

Recent studies have linked the ability of novice (CS1) programmers to read and explain code with their ability to write code. This study extends earlier work by asking CS2 students to explain object-oriented data structures problems that involve recursion. Results show a strong correlation between ability to explain code at an abstract level and performance on code writing and code reading test problems for these object-oriented data structures problems. The authors postulate that there is a common set of skills concerned with reasoning about programs that explains the correlation between writing code and explaining code. The authors suggest that an overly exclusive emphasis on code writing may be detrimental to learning to program. Non-code writing learning activities (e.g., reading and explaining code) are likely to improve student ability to reason about code and, by extension, improve student ability to write code. A judicious mix of code-writing and code-reading activities is recommended.

In this paper, we describe Computing in the Arts (CITA), an innovative interdisciplinary major, which combines computer science with artistic theory and practice. CITA is relatively easy to implement across a variety of higher education institutions, because it repurposes existing courses and resources in computer science and the arts. CITA is relatively easy to implement across a variety of higher education institutions, because it repurposes existing courses and resources in computer science and the arts. At our institution, this involves three tracks (music, visual art, and theatre). CITA incorporates four synthesis courses, one per academic year, which help students interweave the two distinct curricular experiences into a cohesive whole in preparation for their capstone project. CITA has been fully implemented at the authors' institution. In the three years of its existence, it has increased participation in computer science by 23% mainly from an underserved population (students interested in the arts). Approximately 40% of these students are women. While these students are not necessarily strong in mathematics, they tend to be strong in design and creativity, thus enriching the pool of computing professionals with people able to envision new technologies and surprising innovations. Such people are fully versed in computer science so they can easily collaborate with more traditional computing professionals such as software engineers and scientific programmers. We discuss our successes, and provide pointers for others to establish similar degree programs, including potential challenges and lessons learned.

This paper reports on a mixed methods study which examines how four experienced instructors approached the grading of a programming problem. Two instructors used a detailed, analytic approach and two instructors employed a holistic approach. One instructor exhibited elements of a primary trait approach. Even though the four instructors used different grading scales and philosophies, their raw scores were highly correlated (Spearman's rho of .81) supporting the conclusion that experienced instructors usually agree on whether a program is 'very good' or 'very bad'. Clearly there is no single right way to grade programs. Further discourse should be encouraged for the benefit of both educators and students.

This study investigates the relationship between novice programmers' ability to explain code segments and their ability to write code. Results show a strong correlation between ability to correctly answer 'explain in plain English' (EiPE) questions and ability to write code indicating that there are aspects of reasoning about code that are common to both writing code and explaining code. Student explanations were categorized using the Structure of the Observed Learning Outcome (SOLO) taxonomy. The better programmers were more likely to articulate relational aspects of the algorithms. While earlier work also found such a link, the code writing in those earlier studies was done on paper. This is the first such result where the writing component was done with 'hands on' a computer. Our results add further evidence for the existence of an aspect of reasoning about code that is common to both explaining code and writing code, which in turn suggests that a judicious mix of teaching both code skills and code explaining skills may lead to a more effective process by which novices learn to reason about code.

This paper reports on the replication of a study of novice programmers, looking for relationships between ability to 'explain in plain English' the meaning of a code segment and success in writing code later in the semester. This study explores the question in a different learning environment and qualitatively evaluates 'explain in plain English' responses to identify implications for teaching. Statistical results from this study are similar to those of the earlier work. Results highlight students' fragile knowledge, particularly for students excluded from the primary analyses by a set of screening questions, and suggest the need for assessment and instruction of basic concepts later into the term than instructors are likely to expect.

This special session will recap qualitative research design and analysis as discussed during an NSF-sponsored two-part workshop for computer science education researchers held in 2009-2010. Several workshop participants will illustrate the use of qualitative methods by describing their research projects. They will briefly present their methodologies, analyses and findings. Attendees will be encouraged to ask questions about how qualitative methods can be used in their own research projects.

Previous research on the interaction between pairs suggests there is a positive relationship between transactive discussion and effective problem solving. Debugging is particularly problematic for novice programmers. Some previous studies suggest this difficulty may be lessened by working in pairs. Using transactive analysis, we examined interactions between five pairs of university-level introductory programming students as they debugged Java programs. Transcriptions of their verbal interactions were coded into transactive statement categories which revealed that the nature of participants' discourse varied. Extensions, feedback requests, critiques and completions were the most frequently observed types of transactions. Other transaction types were rarely detected in this context. The amount of discussion for the pairs varied as did the number of transactive statements. Results suggest that pairs who talked more and used completion transactions more often attempted more problems, but those who critiqued more frequently successfully debugged more problems. The teaching implications of this work and recommendations for future research are discussed.

This paper describes a process for establishing and maintaining a computer science education research group. Its purpose is to tell the story of one long-standing group, describe the benefits afforded by collaborative research groups and provide advice about how to form such a group. The intended audience is faculty members at teaching-intensive institutions who wish to establish or revive a research program in computer science education. We discuss establishing and maintaining a computer science education research agenda in a teaching-intensive environment.

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.

We present what students say about their preferred learning style to succeed in introductory programming. Using the Felder-Silverman learning styles, students contrasted the 'best' learning style for programming with the 'best' for learning mathematics. Overall students believe that while they learn mathematics using a reflective style, learning to program is significantly more active. They also believe learning mathematics has a strong verbal component, whereas learning programming is primarily visual.

We collected advice on how to succeed at learning to program from 164 CS1 students at 3 institutions during a "saying is believing" intervention designed to encourage a growth mindset. More students gave general advice (63%) than programming-specific (23%) or attitudinal advice (34%), despite being prompted to encourage future students to develop a growth mindset toward programming. Advice categories and quotes offer educators insights into student beliefs and practices and suggest a framework for considering how best to advise students. We discuss the implications of students offering advice to other students and provide a handout of representative advice intended for distribution to students in introductory programming courses.

Jane sees 50 compiler errors as a challenge. John sees them as defeat. Psychology research suggests these contrasting reactions may stem from students' self-theories, or their beliefs about themselves. Jane's reaction is characteristic of a growth mindset, the idea that with hard work and persistence, one's intelligence can increase. John's behavior is in line with a fixed mindset, the belief that individuals are born with a certain amount of intelligence and there is little they can do to change it. Numerous studies of self-theories have shown that students with a growth mindset perform better in academic settings; they cope more effectively with challenges, maintain higher grades, and are less susceptible to stereotype threat. In this study we attempted a "saying is believing" intervention to encourage CS1 students to adopt a growth mindset both in general and towards programming. Despite notable success of this type of intervention in a non-CS context, our results offered few statistically significant differences both from pre-survey to post-survey and between control and intervention groups. Further, the statistically significant results we did find differed in direction between institutions (some students exhibited more growth response, others less). We analyzed further evidence to explore possible confounding issues including whether our intervention even registered with students and how students interpreted the questions which we used to assess their self-theories.

No abstract available.

A qualitative analysis of debugging strategies of novice Java programmers is presented. The study involved 21 CS2 students from seven universities in the U.S. and U.K. Subjects "warmed up" by coding a solution to a typical introductory problem. This was followed by an exercise debugging a syntactically correct version with logic errors. Many novices found and fixed bugs using strategies such as tracing, commenting out code, diagnostic print statements and methodical testing. Some competently used online resources and debuggers. Students also used pattern matching to detect errors in code that "just didn't look right". However, some used few strategies, applied them ineffectively, or engaged in other unproductive behaviors. This led to poor performance, frustration for some, and occasionally the introduction of new bugs. Pedagogical implications and suggestions for future research are discussed.

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).

This paper presents information from a survey of departments offering accredited computer science degree programs.It discusses demographic information relakl to curricula, faculty, and students for all such programs, and identifies vkious trends based on these data.These results are of interest &d use to all accredited and non-accredited programs alike, since they facilitate monitoring -and comparisons among individual degree programs in the nation in terms of curriculum issues, faculty composition and salaries, student enrollment and graduation rates.Academic hit lnformatih (Tables l-3) -1 : This section pre$ents hiformatioh on the academic units ?ffering $13 accredited degree programs.! Table 1 diSplayq the dep@me@ names of these units.Fii-four of the surveyed academic units (97%) specified a department name.Two u&s did not specify a department naqe; iinstead they identified their academic unit as College of Co,mputer Science.',This survey was Bent to 140 university units.Of these, fifty-six (40%) returned the survey inskument.* 2 These' are divisions whose title includes the word 'engineering."SIGSCE 98 AtlantaGA USA 3, These are divisions whose title .includes"science", but not Copyright 1998 O-89791-994-7/98/ 2..%5.00 "engineering" or "arts."

No abstract available.

This paper discusses various strategies for introducing and reinforcing software engineering principles in the undergraduate Computer Science curriculum. These strategies are based on a set of standards for internal/external documentation and organization of software which may be implemented quickly and inexpensively without a complete overhaul of courses. This provides a flexible framework for the development of a wide variety of carefully-planned programming assignments/projects.

This paper presents results of a survey of departments offering accredited Computer Science degree programs. It discusses demographic information related to curricula, faculty, and students for all such programs, and identifies various trends based on this data. These results are of interest and use to all accredited and non-accredited departments alike, since it provides the information necessary to monitor and compare individual degree programs to other programs in the nation in terms of faculty composition, student enrollment and graduation rates, faculty salaries, and curriculum issues.

This paper focuses on documentation standards and their employment throughout the undergraduate computer science curriculum. Specifically, it presents the motivation for a set of well-defined documentation standards for programming-intensive computer science courses. Additionally, it discusses the establishment of standards addressing both the procedure- and object-oriented programming paradigms. Finally, it relates the authors' experiences in using such standards to teach many software engineering principles along with required concepts in the undergraduate computer science curriculum.

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No abstract available.