Professors are continually exploring ways to increase the engagement of their students, but are sometimes concerned that doing "fun" things in class could adversely affect student learning. Over the course of several semesters, we had the same professor teach several CS1 and CS2 courses for computer science and game development majors. As part of his classroom approach, the professor used a student response system to engage the students in the flow of the lecture. In this paper, we examine the relationships between student participation using the student response system and student performance in the course assessments. We also explore the relationship between each student's perceived mastery of course topics and their demonstrated mastery of those topics on the Final Exam. Finally, we explore several differences between the multiple courses included in the study.

Professors often develop anecdotal guidelines about how each student's past performance in their academic major relates to their performance in later courses. While these guidelines can be useful, a more formal statistical analysis of these relationships can provide valuable insight into predicted student performance, which can help professors guide their students to focus on potential areas of difficulty. In addition, such analyses can identify which courses are key indicators of later performance in the major. This additional insight into the relationships between the courses in the curriculum can help professors implement curriculum changes and measure the effects of those changes. In this paper, we present the results of such an analysis for computer science majors at the U.S. Air Force Academy.

Many incoming college freshmen have accumulated a significant number of hours of experience playing computer games. Extending that experience to actual game creation activities can be highly motivational for these students. Most of these activities require some level of programming expertise, however, making them activities too advanced for the majority of incoming students.In this paper, we describe a freshman-level course called Problem Solving through Game Creation. Students learn to use a number of drag-and-drop game creation tools to develop both 2D and 3D games, with no programming required in the course. We also cover a variety of other topics and tools related to game development.Our experience has been that students enjoy the course, but we have more formal course goals as well. Specifically, we hope to motivate students to declare and complete the computer science major and to better prepare students for the initial required computer science courses. We describe these goals in detail and discuss the process we have initiated to continually evaluate achievement of those goals.

Recent research has shown that a student's learning style - essentially, the way a student approaches and masters new material - can affect student performance in introductory computer science courses. We show here that a student's learning style can also affect student performance across the courses in the computer science curriculum.This paper presents the results of a case study in which we collected learning style data for students completing the required courses in a typical computer science curriculum. We then used a wide range of statistical analyses to check for bias in the dataset and to examine the relationships between student learning style and student performance in those courses.Our analysis identified a number of statistically significant relationships between student learning style and performance. We examine potential explanations for those relationships and discuss ways in which the results can be used to enhance student learning.

One of the largest challenges facing educators teaching undergraduate software engineering courses is providing the students with meaningful experiences they will find useful when they complete their undergraduate education. Those experiences should include all phases of the software development process, and to be as realistic as possible they should also include the uncertainty and continual change present in any real project. In addition, those experiences need to include working with others in a team, which can affect the morale of some students and also poses challenges to the professor when the time to assign grades arrives. In this paper we discuss how we have tailored the software engineering capstone sequence at the U.S. Air Force Academy to address these issues.

Many colleges and universities throughout the world offer introductory computer science courses with significant student enrollment. There are certainly plenty of challenges for the administrators of those courses; ensuring equitable grading across different instructors and offering times for the course, accomplishing the massive material preparation and other logistical tasks required for the course, grading the large number of assessments associated with the large enrollment, and managing the numerous instructors in the course are a few examples. This paper discusses the processes we have implemented to address these and other management challenges at the U.S. Air Force Academy.

One of the largest challenges facing educators teaching courses with a significant programming component is deciding how to evaluate each student's programming ability. In this paper we discuss how we have addressed this challenge in an introductory computer science course and statistically analyze the results to examine potential inequities in our approach.

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.

Because learning styles affect how students approach new material, a deeper comprehension of these styles can help professors enhance student learning experiences. In this paper we discuss how learning style data can be used to help guide student study habits and instructional strategies. Additionally, we examine whether or not there is a correlation between learning style and academic performance in an introductory computer science course.

Spurious results are an inherent problem of most static analysis methods. These methods, in an effort to produce conservative results, overestimate the executable behavior of a program. Infeasible paths and imprecise alias resolution are the two causes of such inaccuracies. In this paper we present an approach for improving the accuracy of Petri net-based analysis of concurrent programs by including additional program state information in the Petri net. We present empirical results that demonstrate the improvements in accuracy and, in some cases, the reduction in the search space that result from applying this approach to concurrent Ada programs.