Real women don't write programs

Aim/Purpose: The purpose of this study is to identify how Advanced level Mathematics and Mathematics course units offered at university level do impact on the academic performance of theoretical Computer Science course units. Background: In Sri Lankan state universities, students have been enrolled only from the Physical Science stream to do a degree program in Computer Science. In addition to that, universities have been offering some course units in Mathematics to provide the required mathematical maturity to Computer Science undergraduates. Despite of this it is observed that the failure rates in fundamental theoretical Computer Science course units are much higher than other course units offered in the general degree program every year. Methodology : Academic records comprised of all 459 undergraduates from three consecutive batches admitted to the degree program in Computer Science from a university were considered for this study. Contribution: This study helps academics in identifying suitable curricula for Mathematics course units to improve students’ performance in theoretical Computer Science courses. Findings: Advanced level Mathematics does not have any significant effect on the academic performance of theoretical Computer Science course units. Even though all Mathematics course units offered were significantly correlated with academic performance of every theoretical Computer Science course unit, only the Discrete Mathematics course unit highly impacted on the academic performance of all three theoretical Computer Science course units. Further this study indicates that the academic performance of female undergraduates is better than males in all theoretical Computer Science and Mathematics course units. Future Research: Identifying other critical success factors contributing to the students’ academic performance of the theoretical Computer Science through empirical studies

This paper describes our experiences using Active Learning in four first-year computer science and industrial engineering courses at the School of Engineering of the Universidad Católica de la Santísima Concepción, Chile. This work corresponds to the implementation stage of the curriculum reform using a CDIO-based approach currently underway at the School of Engineering. Before this reform, students had only one introductory course to their field of study, meeting one hour a week. After the reform, computer science students take an Introduction to Computer Science course and a Programming Laboratory. Similarly, the industrial engineering program now includes an Introduction to Industrial Engineering and an Engineering Communication course. These courses have been designed using CDIO standards 1, 4 and 8 as guidelines and have been formulated to include active learning in its many forms such as project-based learning, problem-based learning, case studies, small group discussions, oral presentations and reflective memos. The redesign impact was assessed via anonymous student surveys and reflective memos. Our results show improved student understanding of their professional endeavour and increased student motivation for their engineering programs. Student surveys registered high degrees of satisfaction with active learning techniques, working in teams, and receiving immediate feedback from instructors and peers.

This study investigated the relationships between computer science (CS) course effectiveness and academic performance in a Guangdong Province private school through an integrated environmental and social psychology framework, examining collaborative learning environment and technology environment perception as mediating mechanisms. A quantitative cross-sectional design surveyed several hundred Grade 4 international high school students enrolled in computer science courses at Guangdong Country Garden School during the 2024-2025 academic year. Results revealed that all five CS course effectiveness dimensions—hands-on experiences, real-world applications, collaborative learning, problem-solving abilities, and technology landscape readiness—demonstrated strong positive correlations with academic performance (r=.790-.879, p<.01), with problem-solving abilities emerging as the strongest predictor (β=.342). Mediation analysis using Hayes' PROCESS macro with bootstrapping procedures confirmed that collaborative learning environment (14.7%-23.7% mediation) and technology environment perception (14.7%-23.9% mediation) functioned as significant partial mediators, with real-world applications showing strongest social-psychological mediation and hands-on experiences exhibiting strongest environmental mediation. Hierarchical regression demonstrated that environmental and social-psychological factors contributed unique variance beyond course effectiveness dimensions (ΔR²=.043, p<.001). Findings provide empirical support for person-environment fit theories and social cognitive frameworks, highlighting the importance of optimizing both technological infrastructure and interpersonal climate for CS education effectiveness in private school contexts.

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.

The adoption of innovative online teaching tools in Computer Science (CS) courses provides opportunities for data-informed instruction as a regular teaching practice in CS classrooms. In this paper, we present a design study for an interactive visual analytics dashboard, called UserFlow, that supports feedback collection from teaching documents and assists instructors in interpreting feedback and acting on it in a timely manner. The design study is conducted with eight domain experts comprising of four teaching instructors, two learning analytics (LA) experts and two instructional designers. UserFlow offers a set of novel visualization designs for presenting the four interleaving aspects of document engagement (i.e., annotations, document traversal path, reading/focus time and student information). We evaluated UserFlow in an undergraduate computer science course with over 700 students. Our results demonstrate the usefulness and need for such a tool for CS educators to inform teaching approaches and courseware improvement.

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 research shows an audience of fellow computer science teachers that there is benefit in testing college student's scientific reasoning skills and that the students increase their logical thinking skills in computer science classes when they are presented with real world problems, engaged in group activities, and given hands-on experiments.Students' scientific reasoning skills from several different computer science courses are analyzed and tested before and after taking the computer science course to measure their improvement in logical thinking. The higher order thinking skills test results are compared, and the overall class and individual student improvement between the classes that use different teaching strategies are analyzed. Also, the test results are compared with students' ability to perform adequately in a computer science course. This information is valuable to advisors when placing students in computer science courses.In the assessments of students' entering knowledge and evaluations of students' thinking improvement, the students' individual higher order thinking skills as well as their overall level of cognitive development are analyzed. Through the analysis of their individual skills and level of thinking, one can expand conclusions about the students' and teacher's performance over time.

Supporting computer science and computer application courses requires spending many resources on specialized software. Courses, such as database concepts and applications, depend upon hands-on experience with software to help students understand important principles. Both the university computer labs and students need to install software for homework assignments. Problems include software availability, cost, installation, platform compatibility, and configuration. While academic licensing provides software to students at significantly lower prices, the price may still be too high for some students. Installation and configuration on client/student side may also be a problem. Further, IT departments face the challenge of installing, upgrading and patching the software in student labs.

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 purpose of this study was to justify of the formation technique of representation of modeling methodology at computer science lessons. The necessity of studying computer modeling is that the current trends of strengthening of general education and worldview functions of computer science define the necessity of additional research of the problems relating to study of computer modeling bases within the basic course of computer science. Such necessity is caused by that “Mathematics and Computer Science” education range is a backbone line of the course. Consideration of modeling issues within computer science course is the basis of cogitative operations development called in pedagogies as methods of mental actions or intellectual abilities. But at schools and in pedagogical higher education institutions of Republic of Kazakhstan this section of computer science course is not considered properly. The issues related to a formation technique of basic concepts of modeling, and also usage of applied software environments for development and research of models except an Excel editor are not developed. Practically there are no techniques creating a broad view on concept of model, there is no methodically acceptable models’ typology. The research results indicate that when disclosing the material on themes about modeling stages the process is should be considered as cyclic, there is need of interpretation of models’ relevance concept, “undelivered”, “vital” tasks are should be considered, which fully reflect intersubject links.

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.

The article is devoted to the problem of optimizing the teaching of a Computer Science course in high school and is aimed at demonstrating the methodology for using blended learning technology in a school Computer Science course using the CLIL approach. A number of blended learning models were studied and the Flipped Classroom model was chosen for our research work. According to the “Flipped Classroom” model, students learn new material and complete assignments using the Internet at home, consolidate and practice the material they have covered during face-to-face learning in the classroom. In addition, given that there is currently a strong demand for a CLIL approach based on a trilingual policy aimed at mastering Kazakh, Russian and English, in a blended learning environment there are opportunities to apply the CLIL approach in a high school computer science course. The article shows the stages of CLIL classes, the actions in which are revealed using specific examples. The results of the study led to the conclusion that the use of two different tools in a school Computer Science course, consisting of the Flipped Classroom and CLIL methods used in educational activities, will help cover voluminous material in English, create conditions for independent study, and facilitate the process of monitoring knowledge for the teacher students and get students interested in learning the subject.

This paper seeks to combine a traditional computer science course with software engineering process. A computer science course such as computer graphics, digital image processing, compiler or operating systems gives a chance to students working in a group to write a realistic large project of almost 3000 lines and make it function correctly. The course involves a team programming project. The software engineering lifecycle addresses issues on design, coding, documenting, unit testing and integration testing. Innovative and active learning based educational methods are adopted for teaching such a course.

Natural language processing has taken enormous steps during the last few years. The development of large language models and generative AI has elevated natural language processing to the level that it can output coherent and contextually relevant text for a given natural language prompt. ChatGPT is one incarnation of these steps, and its use in education is a rather new phenomenon. In this paper, we study students’ perception on ChatGPT during a computer science course. On the course, we integrated ChatGPT into Teams private discussion groups. In addition, all the students had freedom to employ ChatGPT and related technologies to help them in their coursework. The results show that the majority of students had at least tested AI-powered chatbots, and that students are using AI-powered chatbots for multiple tasks, e.g., debugging code, tutoring, and enhancing comprehension. The amount of positive implications of using ChatGPT takes over the negative implications, when the implications were considered from an understanding, learning and creativity perspective. Relatively many students reported reliability issues with the outputs and that the iterations with prompts might be necessary for satisfactory outputs. It is important to try to steer the usage of ChatGPT so that it complements students’ learning processes, but does not replace it.

There has been much debate in the past few years about the appropriate mathematics requirements for an undergraduate computer science major. The discussion has focused primarily on two issues: (1) the underlying mathematical content of computer science courses and (2) the content of mathematics courses which would serve as appropriate cognate requirements for computer science major programs. While this discussion has been helpful, it has been too narrowly focused--it has not started from an understanding of the relationship between the disciplines of mathematics and computer science, but rather has sought to identify mathematical prerequisites that computer science majors need in order to take existing computer science courses. This paper is a small step in seeking to apply an understanding of the relationship between the disciplines of mathematics and computer science to the undergraduate computer science curriculum.