Learning Abstraction in Computer Science

The pipeline of three introductory computer science courses at our university have low pass rates. As part of our efforts to improve the pass rates in these courses, we examine the students' mathematics preparation and their progress from one course to the next. We find that the minority of students who enter the university with good mathematics preparation are likely to succeed in our introductory computer science courses. For the majority of students, we suggest a series of interventions throughout the pipeline. Prior to taking Computer Science 1, students could gain more experience with problem solving during the Introduction to Computer Science and Introduction to Computer Engineering courses. We suggest adding precalculus as a prerequisite for Computer Science 2. For students who do not earn an A in Computer Science 2, we plan to examine whether taking a class in a second programming language before taking Computer Science 3 will improve performance in Computer Science 3. Our goal is to improve student learning and retention through these efforts, combined with continual innovation in the classroom.

This study investigated whether student academic achievement in college computer science programs in Taiwan could be predicted by factors reported to be effective in U.S. studies. Subjects were 940 college students enrolled in five universities offering computer science programs. A correlation study was conducted and prediction models were established. The predictive powers of college entrance examination (CEE) scores in relation to subsequent college performance appeared to be limited. Overall high school achievement and math course averages were identified as effective performance predictors. The close relationship between performance in introductory computer science courses and performance in complete computer science programs was validated. Significant linear prediction models with limited predictive powers were generated for overall performance but not for introductory computer science course performance. Model predictive powers were significantly improved when performance in introductory computer science courses was included in the models. No significant gender differences were found for CEE performance. However, female subjects outperformed male counterparts in course performance at both the high school and college levels.

The proportion of computer science (CS) bachelor's degree recipients who are women has consistently been small and is declining. This study investigates factors that predict performance and persistence in an undergraduate CS program and explores why even high-achieving students leave the undergraduate "CS pipeline." The factors that predict achievement and retention sometimes interact in complex, unexpected ways. Male students who earned less than a B in an introductory CS course were more likely to take the next course in the curriculum than were women who earned less than a B. Achievement is a factor in even high-achieving students' decision to leave CS; loss of interest can accompany loss of confidence. Level of achievement was predicted by various background factors including Scholastic Aptitude Test scores, the number of Calculus courses taken before entering the CS program, amount of access to a computer at home, prior computing experience, and having a mentor or role model during high school. Most of these factors also predicted persistence beyond the first two courses required for a CS major. Curiously, women in the introductory CS course who reported having low exposure to specific programming skills outperformed women who reported having a high level of programming experience. The reverse was true of men. Further investigation provided evidence that women who develop programming skills while in high school might do so at the expense of developing other skills that strongly predict CS achievement, particularly math skills.

It is common knowledge that enrollments in computer science have plummeted and educators are challenged to find ways to engage and promote success and retention of students while maintaining standards in introductory computer science courses. This study focuses on the implementation of a collaborative, modified peer-led team learning (PLTL) instructional approach in a large sized introductory computer science course. The site is a major southeastern university in the United States where all students are required to take one of three introductory computer science classes. The course version selected for this study specifically targets computer science majors and the study spans three years of data, and involves 591 students. Students who experienced the student-centered instruction and worked in small groups facilitated by a peer leader (treatment) in years 2006-07 and 2007-08 were compared with students who experienced a traditional recitation lecture section (control) in 2005-06. The content and the course owner was the same for all three years. Quantitative data analysis show marked and statistically significant improvements in student performance, for both male and female students. These findings suggest that using undergraduate leaders to implement a peer-led team learning model can be as effective in promoting achievement and retention in computer science education as it has shown to be in math and science classes over the past several years.

Many colleges and universities offer an introductory computer science course based on a specific programming language. The Department of Computer Science at the University of Kansas has recently reorganized its introductory computer science course to include such topics as algorithms, top-down algorithmic design, elementary programming techniques, and introduction to time-sharing. Besides the discussion of this transition, the paper discusses a questionaire that was developed and utilized during this transition. It is hoped that others will benefit from our experiences.

Introductory computer science courses offer an excellent test bed for utilizing a personalized system of instruction. The ubiquity and operating hours of computer terminals and computer centers in educational institutions is ideal for student self-pacing. The rapid response time of computers is a positive reinforcement when solving problems. The recent advent of the PLATO computer-based educational system provides many opportunities for students and faculty in computer science and many other disciplines. PLATO terminals provide a powerful motivational tool for PSI. Motivation is a critical part of computer science courses. Students are expected to acquire synthesis-level skills in the cognitive domain. This level of knowledge is higher than the level of skill, memorization or rule application, required for many other introductory courses. An Excellent Mixture for PSI: Computer Science, PLATO, and Knowledge Levels.

Pascal’s long and successful run at West Point illustrated Winston Churchill’s dictum that, “The absence of alternatives clears the mind wonderfully.” However, by the mid-90s, the United States Military Academy was finding that Pascal, long the educational workhorse, was no longer the clear choice for serious computer science education. The decision was taken to evaluate alternatives to Pascal. The criteria selected for programming language evaluation were student learning outcomes based on course goals and objectives. The controlled educational environment of a service academy provides an excellent opportunity to conduct research using control groups and experimental groups. We conducted a side by side comparison of the use of Pascal, which was specifically designed for instructional purposes, and Ada in an introductory course. The experiment demonstrated that students were much more successful in Ada than in Pascal, and led to the revision of the Academy core curriculum to use Ada 95 in our introductory computer science class. This paper discusses the details of our comparison, citing specific examples to illustrate a rational basis for evaluating programming language features against course objectives.

Full Paper. Research. We discuss possible ways to direct students to right level of introductory programming. While many schools offer college preparatory or advanced placement courses in computing, there is still, unfortunately, a large part of the college-ready population that has no opportunity to learn computing at all before they arrive. Regulation of CS education at the state/province or national level is still rare (but growing). Thus incoming students possess a wide range of skills and knowledge. When coupled with increasing enrollments, this diversity of experience can result in courses having large numbers of both absolute beginners and seasoned coders. Such courses are difficult to teach, intimidate novice students, and bore those with more experience. This can result in low engagement and retention.Unlike mathematics and language arts, introductory courses in CS vary widely from one institution to another in both conceptual material and programming language used. A standard point of entry to college mathematics is a calculus course, with some students instead starting earlier with pre-calculus or an algebra refresher, and others starting out in the second-term calculus course. There is rarely a concern about student skill being hidden by notational or other language differences, because the language of mathematics is close to universal. Similarly, freshman language arts courses in reading and/or writing assume a certain level of skill and maturity of comprehension and expressiveness in the target language; otherwise remedial courses are provided.We investigate placement of incoming first year students into appropriate introductory computer science courses at higher education institutions where there is more than one choice of first course. The goal is to determine the best way to decide which first course would be the most helpful for each student.

Academic service-learning (ASL) focuses on clearly aligning course learning objectives with community needs and has evolved into a highly effective pedagogical approach across the spectrum of disciplines and institutional types. Unfortunately, there are not many examples of its use in computer science. If service learning is incorporated into computer science, it is generally in the upper level courses where students have a larger skill set. This paper presents an academic service-learning approach in an introductory computer science course. This is important on multiple fronts. With the declining enrollments, especially among females, coupled with students' desire to make a difference in their world, we need to insert this pedagogical approach as early into the curriculum as possible. We will show an example of what can be done in an introductory course and discuss the positive effects on female enrollments.

article A comparison of Ada and Pascal in an introductory computer science course Share on Authors: Jeanne L. Murtagh Software Professional Development Program, Air Force Institute of Technology, Wright-Patterson AFB, OH Software Professional Development Program, Air Force Institute of Technology, Wright-Patterson AFB, OHView Profile , John A. Hamilton D/Electrical Engineering & Computer Science, United States Military Academy, West Point, NY D/Electrical Engineering & Computer Science, United States Military Academy, West Point, NYView Profile Authors Info & Claims ACM SIGAda Ada LettersVolume XVIIIIssue 6Nov./Dec. 1998 pp 75–80https://doi.org/10.1145/301687.289535Online:01 November 1998Publication History 1citation275DownloadsMetricsTotal Citations1Total Downloads275Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The number of computer science (CS) courses has been dramatically expanding in U.S. high schools (HS). In comparison with well-established courses in mathematics and science, little is known about how the decisions made by HS CS teachers regarding how and what to teach impact student performance later in introductory college CS courses. Drawing on a large sample of 2,871 introductory college CS students at 115 U.S. institutions who had taken a CS course in HS, we examined the topic coverage and prevailing instructional methods in the HS course and investigated how these experiences influenced student performance in college CS. Controlling for differences in student background, we find two predictors of higher grades in college CS: greater frequency of coding-related activities in HS (programming, debugging, studying algorithms) and lower frequency of “non-coding” computer use (e.g., data analysis, computer security). Interaction models revealed a more complex story. Coding-related activity more heavily benefited students who did not have coding help available at home. In the 28% of college CS courses in which instructors employed innovative pedagogies, students with higher ACT or SAT mathematics scores had a greater advantage than in traditionally taught courses. Finally, in the innovative college courses, students whose HS CS exams had typically included testing on vocabulary did worse than students whose exams had not included such tests.

ABSTRACTBackground and Context: The relationship between novices’ first programming language and their future achievement has drawn increasing interest owing to recent efforts to expand K–12 computing education. This article contributes to this topic by analyzing data from a retrospective study of more than 10,000 undergraduates enrolled in introductory computer science courses at 118 U.S. institutions of higher education.Objective: We explored the relationship between students’ first programming languages and both their final grades in an introductory computer science course and their attitudes about programming.Method: Multiple matching techniques compared those whose first language was graphical (e.g., Scratch), textual (e.g., Java), or absent prior to college.Findings: Having any prior programming experience had positive effects on both attitudes about programming and grades in introductory computer science courses. Importantly, students whose first language was graphical had higher grades than did students whose first language was textual, when the languages were introduced in or before early adolescent years.Implications: Learning any computer language is better than learning none. If programming is to be taught to students before early adolescence, it is advised to start with a graphical language. Future work should investigate the transition between different types of programming languages.

Realizing the importance of teaching efficiency at early stages of the program of study in computer science (CS) on one hand, and the difficulties encountered when introducing this concept on the other, we advocate a different didactic approach in the introductory CS course (CS1). This paper describes the approach as it is used at the Open University of Israel (OUI). The OUI, a distance teaching institution with no prior educational requirements, runs a full-fledged CS program. Because of its open door policy, the dropout rate, especially in introductory courses, as well as the percentage of failures, is very high. Using the new approach has raised the percentage of students who pass the course. The new approach advocates integrating three-part questions which ask not only to identify the algorithmic problem that a given algorithm solves and to analyze its complexity, but also to design a new algorithm that performs the same task, while increasing efficiency by an order of magnitude, not only by a constant factor. The research we conducted to examine the implications of using this approach is described here.

Students in today's introductory Computer Science courses are often already experienced with sophisticated collaborative technologies and social computing systems. Among these popular systems are the online community building and information sharing sites that have become an intrinsic part of a new cyber-culture that continues to emerge. The involvement of students in collaborative technologies can be responsible in some cases for why certain students are inspired to pursue careers in computing disciplines. Yet very few CS 1 and CS 2 textbooks contain content related to collaborative software systems, presumably because of the extreme complexity involved in developing software that communicates with other software over a network. While social computing activities are very popular, the fact that it is not very prevalent in CS 1 and CS 2 course material can discourage students who have particular interest in that area of computing. In this paper, I present a system that I have developed that offers a simple way to incorporate class work and projects that involve collaborative networking and social computing paradigms at the level of introductory Computer Science courses.

This study was conducted to determine factors that promote success in an introductory college computer science course and to determine what, if any, differences appear between genders on those factors. The model included math background, attribution for success/failure, self-efficacy, encouragement, comfort level in the course, work style preference, previous programming experience, previous non-programming computer experience, and gender as possible predictive factors for success in the computer science course. Subjects included 105 students enrolled in an introductory computer science course. The study revealed three predictive factors in the following order of importance: comfort level (with a positive influence), math background (with a positive influence), and attribution to luck (with a negative influence). No significant gender differences were found in these three factors. The study also revealed that both a formal class in programming (which had a positive correlation) and game playing (which had a negative correlation) were predictive of success. The study revealed a significant gender difference in game playing with males reporting more experience with playing games on the computer than females reported.