Using collaborative, modified peer led team learning to improve student success and retention in intro cs

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.

In a previous study, we investigated a framework for teaching abstraction in computer science (CS) in the context of an introductory CS course for 7th-grade students. The framework was found effective for developing CS abstraction skills. In this paper, we describe a study that investigated teaching abstraction in CS to 7th-grade students through the perspective of gender. Specifically, we compared the CS abstraction abilities of boys and girls taking the introductory CS course in 7th grade (using the Scratch environment). We also examined the difference in the effect of the teaching strategy on CS abstraction abilities between boys and girls. Our findings show a significant advantage of girls over boys regarding several aspects of CS abstraction. We also found that the framework for teaching CS abstraction, which was already established in our previous study as effective in the context of an introductory course for 7th-grade students, has an enhanced effect in this context when it comes to girls. This enhancement was manifested by a better overall performance by the girls, when comparing boys and girls that took an introductory CS course in which this framework was used. It was also manifested by the girls' deeper and more reliable perception of CS. We conjecture that this framework has the potential to affect girls' self-efficacy, and as a result to create a positive effect by encouraging and motivating girls to pursue CS education beyond this introductory course. That is, this framework has the potential to contribute to solving the issue of underrepresentation of females at the different stages of CS education, or in other words, to widening the CS pipeline for females.

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.

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.

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.

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

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.

As part of an ongoing initiative to continually revise and improve its introductory computer science courses, the Electrical Engineering and Computer Science Department at the United States Military Academy has added the use of LEGO Mindstorm robots as part of the active-learning environment used to teach Information Technology (IT) and programming basics. It is critical for the Army and the Nation that its future leaders understand and are capable of taking advantage of IT. All cadets at the United States Military Academy at West Point are required to take a course on IT and problem solving using computer programming. This course is an important first, and sometimes only, opportunity to expose undergraduate students to technology and concepts that will be a part of their daily lives and future careers. The LEGO Mindstorm robots are used in the introductory computer science course to teach fundamental, computer programming concepts and introduce the concepts of autonomous vehicles, embedded computer systems and computer simulation. The positive short-term impact on the students taking the course has been substantial and while the long-term impact has yet to be measured, it also has the potential to be substantial. Members of the faculty at West Point developed a computer simulation of the robot environment as well as a Java programming language translator for the LEGO programmable brick, called the RCX. These two tools are combined into a unique programming and teaching environment that we named Jago. Jago enables the robots and robot simulator to be used to teach fundamental programming concepts visually, which some students can more easily grasp and all are clearly excited to use. Based on these results we have incorporated Jago into the core IT course taught at West Point.

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.

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.

In this paper, we present our experience with redesigning an introductory computer science course for (electrical) engineers with blended learning concepts. It is a large mandatory course with eight credit points over the course of two semesters, where first year electrical engineers learn how to program with Arduino, C and Python. Additionally, they need to cover basic computer science concepts such as binary numbers, Boolean algebra, encodings, finite state machines and object-oriented programming. We designed the course to cover the fields that are most relevant to our students’ future careers. With the new format, students declare they are much more engaged with the course, they drop the course less often and they actually achieve better exam results. We completely re-structured the course, introduced inverted classroom elements and hackathons and we continuously optimized and adapted the course. The main focus is on hands-on-experience and teamwork, which we mostly achieve by the use of hackathons. In this paper, we described the contents and teaching concepts of the course and we discussed the achieved results.

There are numerous ways to evaluate student programming ability, all of which have benefits and drawbacks. In this paper we discuss how we have combined a number of those evaluation techniques to assess student programming ability in an introductory computer science course and statistically analyze the relationships of student performance using the different evaluation techniques.

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.