Computer Science Courses Research Articles

AI-Based Applications Enhancing Computer Science Teaching in Higher Education

The incorporation of Artificial Intelligence (AI) in higher education has gained significant attention as it presents new opportunities to improve the teaching and learning process. This paper aims to analyze how AI applications can support the teaching and learning of computer science in higher education. By reviewing various scientific publications, this paper offers an in-depth analysis of how AI-driven tools and applications have been successfully integrated into computer science courses. Key AI applications considered include intelligent tutoring systems, assessment, performance prediction, academic management, educational innovation, and adaptive learning. These tools have been shown to increase student engagement, provide tailored instruction, offer timely feedback, and enable the scalability of high-quality education. Also, the paper addresses the challenges associated with AI in education, such as diversity of educational contexts, security and data privacy, algorithmic bias, and the importance of faculty preparation. This article emphasizes the transformative potential of AI to enhance computer science education in the context of higher education and identifies mechanisms for research and practice to take full advantage of AI capabilities in designing effective and inclusive learning environments, guided by a comprehensive synthesis of current research and case studies.

Engineered Prompts in ChatGPT for Educational Assessment in Software Engineering and Computer Science

AI Assess, a ChatGPT-based assessment system utilizing the ChatGPT platform by OpenAI, composed of four components, is proposed herein. The components are tested on the GPT model to determine to what extent they can grade various exam questions based on learning outcomes, generate relevant practice problems to improve content retention, identify student knowledge gaps, and provide instantaneous feedback to students. The assessment system has been explored using software engineering and computer science courses and is successful through testing and evaluation. AI Assess has demonstrated the ability to generate practice problems based on syllabus information and learning outcomes. The components have been shown to identify weak areas for students. Finally, it has been shown to provide different levels of feedback. The combined set of components, if incorporated into a complete software system and implemented in classrooms with proposed transparency mechanisms, has vast potential to reduce instructor workload, improve student understanding, and enhance the learning experience. The potential for GPT-powered chatbots in educational assessments is vast and must be embraced by the education sector.

Exploratory factor analysis of a precollege quantum information science and technology survey: exploring career aspiration formation and student interest

The present study examined student outcomes from a quantum information science and technology (QIST) summer outreach program for U.S. secondary students. The program focused on foundational principles and skills from classical physics, quantum physics, and quantum computing. Students’ attitudes towards QIST learning and careers were measured through a pretest/posttest research design. Exploratory factor analysis was utilized to identify latent attitudinal themes, followed by comparisons of means to measure changes in these factors and analysis of covariance to assess whether these changes were related to student demographics and prior academic coursework. Two latent themes were identified: (1) QIST career aspiration formation and self-concept, and (2) QIST interest and behavioral intentions. Results indicated that students improved their QIST career aspiration formation and self-concept with a medium to large effect size, yet their QIST interest and behavioral intentions were unchanged. These results were independent of student demographics (gender, ethnicity, grade level) and prior mathematics and computer science course enrollment; however, students who had previously taken chemistry and physics were more likely to improve QIST career aspiration formation and self-concept. Students also increased their intention to take four years of elective mathematics and science with a small effect size. These results suggest that early exposure to QIST principles, skills, and applications may increase students’ consideration of related careers and academic coursetaking plans; however, their interest in QIST may be independent of career aspiration formation. Further research is needed to measure attitudinal sub-domains that may be influenced by early QIST education and specific programmatic elements.

Connections between students’ perceptions of the motivational climate in a course and their motivational beliefs and academic and career goals

ABSTRACT To address the demand for engineers and computer scientists in the workforce, and the fact that some engineering students dropout or change majors, we explored how the motivational climate in an undergraduate computer science (CS) course was related to students’ motivational beliefs and academic and career goals. Participants included 310 students enrolled in a CS course within a university College of Engineering. The survey included measures that assessed constructs related to the MUSIC Model of Motivation theory, situated expectancy-value theory, and school belonging. Regression analyses identified several significant relationships between the course-level motivational climate variables and students’ CS motivational beliefs. However, the pattern of relationships between variables often differed by academic major for each of the CS belief variables. Students’ CS motivational beliefs predicted their academic and CS career goals, but the patterns were somewhat different across academic majors. We did not identify many differences across sex and race/ethnicity.

AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education

This study investigates the impact of AI-generated contexts on preservice teachers’ computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experiment was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18–19 years, who were training in the Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key factors: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement in perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude toward technology integration in education. The findings highlight the importance of providing appropriate context and support when encouraging future educators to build confidence and embrace educational technologies. This study adds to the expanding research connecting AI, robotics, and education, emphasizing the need to incorporate these tools into teacher training programs. Further studies should investigate the lasting impact of such approaches on computational thinking skills and teaching methods in a variety of educational environments.

Preparation of Schoolchildren for the Olympiad on Artificial Intelligence

The article presents a small part of the research results devoted to the development of scientific and methodological support of variant teaching of the basics of artificial intelligence in the course of computer science of basic general and secondary general education, concerning the preparation of schoolchildren for the Olympiad on artificial intelligence. The aim of the article is to actualize the problem of developing or selecting the necessary task material for purposeful preparation for school Olympiads on artificial intelligence. The content of theoretical and practical modules of the basics of artificial intelligence developed as a result of the research includes olympiad training in artificial intelligence for basic and high school students studying computer science at an advanced level. The research methodology consists of: integrative approach to the development of the methodology of variant teaching of the basics of artificial intelligence and the implementation of possible educational trajectories in the basic educational programs of basic general and secondary general education; experience in the development of the concept and content of the first All-Russian Olympiad of schoolchildren of grades 8-11 on artificial intelligence; expert activity in the Russian Union of Schoolchildren’s Olympiads and the results of surveys of teachers. General theoretical and empirical research methods were used. The conclusion about possible solution of the problem of development of training tasks for purposeful preparation of schoolchildren for the Olympiad on artificial intelligence and realization of the course on choice of participants of educational relations with the use of standard tasks of three stages of the Olympiad on artificial intelligence is made in the conclusion.

Pascal as the Driving Force of Informatics Education in Schools in Poland

The first books in Polish about the Pascal programming language appeared in the late 1970s, and were soon followed by a Polish translation of Niklaus Wirth’s book Algorithms + Data Structures = Programs. At that time, many efforts were made to prepare teachers to teach informatics in schools, and Pascal was one of the topics taught, often with no access to computers. The situation improved significantly after the first informatics curriculum was approved for schools by the Ministry of National Education (MEN) in 1985, in response to the increasing number of microcomputers in schools and students’ interest in programming. As a confirmation of the conclusions drawn from the discussion in the ABACUS magazine (1984) on “Which is the best programming language for a first course in computer science,” Pascal very quickly became the basic programming language in Polish schools as a tool for practicing algorithmics. The first informatics textbook was published in 1988 and in it, after a short Logo course, Pascal took the main place as a language for developing solutions to algorithmic problems in the form of computer programs. In the following years, many teaching aids were developed for programming education in schools, as well as other aids, e.g., an authoring system, programmed in Pascal. Pascal was the primary programming language in informatics education until the end of the first decade of the 21st century, subsequently replaced by Python. It is interesting that Polish winners of national and international Olympiads in Informatics for many years used Pascal, demonstrating the enormous professional values of this language. In this article we present in detail, among others things: the role and influence of Pascal on the development and level of informatics education in schools in Poland, as well as other scientific activities and initiatives supported by this language. Many students educated in this technical and mental environment found their place in professional development teams in the best computer and technology corporations, and evidently most of them joined the national working force.

Providing tailored reflection instructions in collaborative learning using large language models

AbstractThe relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well‐established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to design database access strategies. Forty‐four teams were randomly assigned to three reflection conditions ([GEN] directive to generate a contrasting case to the student solution and evaluate their trade‐offs in light of the principle, [CONT] directive to compare the student solution with a provided contrasting case and evaluate their trade‐offs in light of a principle, and [NSI] a control condition with a non‐specific directive for reflection evaluating the student solution in light of a principle). In the CONT condition, as an illustration of the use of LLMs to exemplify knowledge transformation beyond knowledge construction in the generation of an automated contribution to a collaborative learning discussion, an LLM generated a contrasting case to a group's solution to exemplify application of an alternative problem solving strategy in a way that highlighted the contrast by keeping many concrete details the same as those the group had most recently collaboratively constructed. While there was no main effect of condition on learning based on a content test, low‐pretest student learned more from CONT than GEN, with NSI not distinguishable from the other two, while high‐pretest students learned marginally more from the GEN condition than the CONT condition, with NSI not distinguishable from the other two. Practitioner notesWhat is already known about this topic Reflection during or even in place of computer programming is beneficial for learning of principles for advanced computer science when the principles are new to students. Generation of contrasting cases and comparing contrasting cases have both been demonstrated to be effective as opportunities to learn from reflection in some contexts, though questions remain about ideal applicability conditions for adult learners. Intelligent conversational agents can be used effectively to deliver stimuli for reflection during collaborative learning, though room for improvement remains, which provides an opportunity to demonstrate the potential positive contribution of large language models (LLMs). What this paper adds The study contributes new knowledge related to the differences in applicability conditions between generation of contrasting cases and comparison across provided contrasting cases for adult learning. The paper presents an application of LLMs as a tool to provide contrasting cases tailored to the details of actual student solutions. The study provides evidence from a classroom intervention study for positive impact on student learning of an LLM‐enabled intervention. Implications for practice and/or policy Advanced computer science curricula should make substantial room for reflection alongside problem solving. Instructors should provide reflection opportunities for students tailored to their level of prior knowledge. Instructors would benefit from training to use LLMs as tools for providing effective contrasting cases, especially for low‐prior‐knowledge students.

Getting it Wrong: Student Estimations of Time and the Number of Drafts in Linked Computer Science and Technical Communication Courses

Getting it Wrong: Student Estimations of Time and the Number of Drafts in Linked Computer Science and Technical Communication Courses

Teaching Reform and Practice of Data Structure Course Based on Smart Teaching Platform

With the rapid development of information technology, smart teaching platforms have become important tools for higher education teaching reform. As a core course of computer science and technology-related majors in higher education, the data structure course lays a solid foundation for students’ professional learning and plays an important role in promoting their future success in technology, research, and industry. This study conducts an in-depth analysis of the pain points faced by the data structure course, and explores a teaching reform and practice of integration of theory and practice based on the system application of a smart teaching platform before class, during class, and after class. The reform practice shows that this teaching mode improves students’ learning initiative, learning motivation, and practical skills. Students not only achieved better results in knowledge mastery but also significantly improved in problem analysis and solution.

How self-beliefs, values, and belonging change and relate with performance during introductory computer science

ABSTRACT Background and Context First-year university computer science courses can provide barriers or bridges into CS for students. Understanding student motivation within this context can enable instructors to better support learning. Objective To understand how student motivation changes across the semester and relates to course performance and student background. Method Students were surveyed four times during the course on their motivation for CS and for the course. Differences across groups and time were analyzed, as were links with performance. Findings Starting the course, women reported lower belonging and self-beliefs than men. Student value for CS declined during the course, whereas self-beliefs and belonging increased. Self-beliefs, course importance, and cost predicted final course grades; interest predicted fluctuations in grades across the semester. Implications Intervening to bolster course value and reduce perceptions of course cost may be most fruitful for increasing final grades; fostering interest may be best to improve engagement and within-course performance.

Artificial intelligence literacy in primary education: An arts-based approach to overcoming age and gender barriers

Artificial Intelligence (AI) literacy education was previously taught primarily at the university and secondary school levels but has recently started to be expanded to primary school settings. When available at the primary school level, AI literacy is often taught within computer science courses, which may potentially reinforce gender stereotypes and discourage female students' engagement. In AI literacy education, the predominant teaching methods are constructivist approaches, which, while effective in fostering active learning, heavily emphasize technical skills and are therefore limited in their pedagogical scope, as they underplay other important questions, such as disinformation, data justice, and AI's ethical and societal implications. The lack of a clear definition of AI literacy for primary education also raises questions about what to teach and how to teach it. Additionally, little attention has been devoted to understanding gender differences in learning outcomes within AI literacy primary education.This study advocates the use of an arts-based transdisciplinary approach for teaching AI literacy to 25 primary school students. A pilot study utilizing mixed methods was conducted to assess the effectiveness of this arts-based approach. Quantitative analysis through a paired t-test revealed a statistically significant improvement in AI literacy among students participating in knowledge tests. Moreover, the results of the Mann‒Whitney and Kruskal‒Wallis tests indicated that gender and age did not impact pre- and post-knowledge test scores. Qualitative analyses further revealed the pedagogical benefits of the arts-based approach, demonstrating that students enhance their conceptual understanding of AI literacy by reflecting on their artifacts. This study contributes to the literature by providing evidence with a small sample size that the arts-based approach can overcome age and gender barriers to accessing AI literacy education and can serve as a means to teach AI thinking.

INVESTIGATING THE RELATIONSHIP BETWEEN SELF-EFFICACY AND STUDENTS’ LEARNING SATISFACTION WITH THE ONLINE LEARNING ENVIRONMENT

Online learning has become important in education because of the changes brought about during the COVID-19 pandemic. The change in the conventional way of face-to-face learning to online learning also affects the satisfaction of learning for students. Therefore, this study aims to determine the relationship between student satisfaction and self-efficacy in online learning, and to examine the self-efficacy factors that most influence online learning satisfaction. A sample of 187 students from Diploma in Computer Science course was selected to complete an online survey questionnaire. The survey used in this study consists of an online learning self-efficacy questionnaire and an electronic learning satisfaction questionnaire. Data was analyzed using SPSS statistical tools. Descriptive statistics, including means and standard deviations, were used to summarize and describe demographic data. In addition, correlation analysis is used to find out the relationship between variables while regression analysis is used to analyze which factors most influence online learning satisfaction. The results show that all three factors show a significant relationship. This means that self-efficacy in learning, time management, and technology play an important role in student satisfaction in online learning. Meanwhile, self-efficacy in learning is the factor that most influences online learning satisfaction.

Prize‐Based Learning in an Introductory Computer Course—A Case Study

ABSTRACTIntroduction to Computer Science is traditionally the first course that all computer science and software engineering majors take. The course introduces many problem‐solving techniques which can be challenging for many freshman students. In order to mitigate some of the issues of this course, we, at the Higher Education Institute, introduced a new prerequisite course, Introduction to Programming Logic, which is a required course for all students who have not taken any previous computer science course. In the Summer Session of 2022, we included prize‐based learning in one of the sections of the course. Prize‐based learning is similar to both problem‐based learning and project‐based learning in many aspects, including the principle of student‐centred learning. However, it differs with respect to the motivation for student success. This approach utilises the students' ambition to win, to encourage students to work harder and learn more both inside as well as outside the classroom.

Engagement Dynamics in Information Technology Education: A Comparative Analysis of Online vs. Face-to-Face Instruction

The purpose of this study was to investigate whether taking a course online or face-to-face matters in terms of student engagement and achievement. Gender differences were also examined. The level of student engagement in an information technology course in a freshman sample from a school of education was surveyed and compared in two consecutive years where the course was taught online in the first year and face-to-face in the second year. There were a total of 129 students, 62 in the online mode and 67 in the face-to-face mode. Data were collected using a survey that included a student engagement scale, as well as midterm and final exams. Non-parametric analyses were used due to data with non-normal distributions for some of the dependent variables, with the Mann-Whitney U test being the main form of analysis for group comparisons. Regarding gender, female students who received face-to-face instruction lost interest in the course toward the final exams, as evidenced by significantly lower engagement scores for both active learning and paying attention. The results show that online versus face-to-face delivery of the computer science course can be more efficient in terms of student engagement, and a similar performance score as in the face-to-face mode can be achieved with less effort to be present at school. In addition, it is evident that female students need more support for greater engagement when the mode of delivery must be face-to-face.

Causes and consequences of stereotypes: interest stereotypes reduce adolescent girls’ motivation to enroll in computer science classes

Motivating girls to enroll in computer science (CS) courses is critically important. Stereotypes that girls are less interested than boys in CS may deter girls. Three preregistered experimental studies (N = 1,053) examined causal links between gender-interest stereotypes and middle school students’ CS motivation. Experiment 1 showed that stereotypes reduced girls’ motivation to enroll, mediated by a lower sense of belonging. Experiment 2 showed that underrepresentation is a cue to stereotypes. Experiment 3 demonstrated that providing information about other girls’ interest countered stereotypes and promoted motivation. Directly addressing stereotypes may be instrumental in promoting equity for all in CS.

Computer science for English learners: supporting teacher learning and improved practice to engage multilinguals in AP computer science principles

Multilingual learners, also known as English learners (ELs), are underrepresented in K-12 computer science (CS) courses, where they often struggle. This paper demonstrates how a Computer Science for English Learners (CSforEL) professional development (PD) program supported Advanced Placement CS Principles (APCSP) teachers in attending to both content and language demands while teaching ELs. We highlight qualitative findings showcasing how CSforEL PD yielded (micro)shifts in teacher practice and EL Student-Content interactions. Specifically, we discuss how unique instructional scaffolds offered within the PD shifted teachers’ awareness regarding EL students’ needs, and helped them support their EL students more effectively.

Do we have the capacity? The policy imperative for equity‐focused K‐12 computer science education

AbstractAccess to computer science education (CSEd) has greatly expanded in recent years. Starting with the Computer Science for All Initiative, launched in 2016 under President Obama, CSEd has expanded across the U.S. with over half of high schools offering at least one computer science course. Although there has been growth in computer science course offerings, disparities exist when it comes to ‘who’ enrolls in CSEd courses, and ‘where’ those courses are being offered. These disparities highlight that while funding and support for CSEd has increased, little is known about the capacity of states to operationalize (and implement) policies to achieve equitable CSEd. Furthermore, how recent changes at the federal and state level(s) are directly impacting minoritized populations' access, participation, and engagement in CSEd. This commentary focuses on the state‐level capacity to implement equity‐focused CSEd policies that can lead to improvements in student opportunities and outcomes. We argue that state CSEd leaders engage at the intersection of four areas: (1) growing state investment in CSEd, (2) increasingly polarizing political environments, (3) securing human and fiscal resources to implement CSEd policy with fidelity, and (4) supporting Culturally Responsive Computing that sustains equitable teaching and learning practices in schools and classrooms. This commentary also serves as a Call to Action for STEM education researchers to listen closely and carefully to the communities served by CSEd research, including students, families, and educators.

Exploration of Integrating Ideological and Political Education into Computer Science Courses in Teaching

Exploration of Integrating Ideological and Political Education into Computer Science Courses in Teaching

The use of the gamification strategy in the discipline of Entrepreneurship in the Computer Science course

The use of the gamification strategy in the discipline of Entrepreneurship in the Computer Science course