Computer Science Courses Research Articles

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.

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.

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.

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

The Impact of Project-Based Learning through Integrating the Use of Technology in Computer Science Courses on Students' Acquisition of Programming Skills

The Impact of Project-Based Learning through Integrating the Use of Technology in Computer Science Courses on Students' Acquisition of Programming Skills

Empowering Learners in Higher Education: Redesigning an Online Computer Science Course Through Universal Design for Learning Implementation

This article focuses on the practical implementation of Universal Design for Learning (UDL) in an online computer science course, articulating the collaborative efforts between the instructional designer (the first author) and the faculty member to redesign the course using UDL principles. Specific instances of redesigned learning modules and artifacts in adherence to UDL principles and results and insights gained from the implementation are reported. The article concludes with recommendations for higher education faculty and instructional designers to consider when applying UDL in their online courses.

Improving student learning performance in machine learning curricula: A comparative study of online problem‐solving competitions in Chinese and English‐medium instruction settings

AbstractBackgroundNumerous higher education institutions worldwide have adopted English‐language‐medium computer science courses and integrated online problem‐solving competitions to bridge gaps in theory and practice (Alhamami Education and Information Technologies, 2021; 26: 6549–6562).ObjectivesThis study aimed to investigate the factors influencing the use of online competitions in machine learning courses and their impact on student learning. We also analyse disparities in learning outcomes and instructional language effects (Chinese vs. English).MethodsAmong 123 participants at northern Taiwan university, 74 chose Chinese instruction (CMI), and 49 opted for English instruction (EMI). The course spanned 18 weeks: team formation in week one, data analysis, machine learning, and deep learning from week 2 to 8, draft proposals and oral presentations by week 9, instructor guidance in weeks 9–17, followed by off‐campus competitions. In week 18, students presented projects for evaluation by judges.ResultsThe results showed improved scores in competition proposal writing and oral presentations, especially for CMI students, who excelled in these areas and in terms of creativity. CMI students emphasized domain knowledge, implementation completeness, and technical depth in proposals. The EMI students focused on implementation completeness and artificial intelligence model accuracy, along with creativity.ConclusionCMI students achieved superior outcomes in machine learning courses, particularly in terms of competition proposals, oral presentations, and increased creativity. Instructional language choice significantly influenced learning trajectories, leading to distinct knowledge development focuses for CMI and EMI.

Making Computer Science Accessible through Universal Design for Learning in Inclusive Education

The field of technology and computer science (CS) is developing dynamically. Just as anyone can learn computers at any age, students with special educational needs (SEN) also aspire to acquire IT (information technology) knowledge on an equal footing with all other students. However, one of the obstacles facing students with SEN is the lack of educational materials and programs for CS in secondary schools. The authors have designed teaching materials and assignments that promote inclusion. This study aims to evaluate the impact of teaching resources developed based on universal design for learning (UDL) to make the school’s CS course accessible to all students. The experiment involved 16 students and five teachers. For 8 weeks, students studied computer science using training materials based on UDL. Assessment of knowledge outcome indicators, particularly programming skills, was conducted before and after the experiment. After studying computer science through specific tasks, the interviewees demonstrated a higher level of assimilation of the subject, as indicated by the subsequent test results (mean = 12.13, standard deviation = 1.20), compared to the pre-experimental test (mean = 8.94, standard deviation = 1.12). The study demonstrated that using special UDLbased tasks to teach CS makes it more accessible and has a positive impact on students with special educational needs.

AUGMENTED REALITY AS A WAY TO PRESENT EDUCATIONAL CONTENTIN THE MOBILE APPS

This article deals with the issue of implementing the capabilities of augmented reality in learning mobile applications, which is very topical for modern education. The aim of the article is to consider the application of augmented reality in the learning content of mobile applications. This article is devoted to the main aspects of the educational mobile technologies and practical application of the research results in the educational process of the general education institutions. The article deals with the application of the educational content of the mobile application module with the application of augmented reality on the example of teaching the subjects ―Informatics‖ and ―Digital literacy‖ in the secondary general education schools of the Republic of Kazakhstan. The basic definitions associated with educational mobile applications and augmented reality technologyare given, an analysis of the subject area is carried out. The international experience of applying this technology in the educational process is considered. The stages of studying the subjects ―Informatics‖ and ―Digital Literacy‖ using mobile technologies of educational direction, such as mobile applications for the organization of the learning process and assessment of knowledge, skills and abilities of students are described. As a result of the experiment, the mobile application was practically realizedand the adapted author's program ―Application of mobile technologies at the lessons of informatics and digital literacy‖ was developed. The received results of application of mobile technologies in studying of a school course of computer science and digital literacy confirm the relevance of the carried out research.

Efficient Data Structures & Algorithms Hunter: A Study in Optimization

Abstract: Data Structures and Algorithms is a fundamental course in Computer Science. However, many students find it difficult because it requires abstract thinking. It would be very helpful if there was a visualization tool of data structures such as arrays, queues, stacks, trees, and graphs for students to manipulate. The tool would allow students to see how an element is inserted into or deleted from different data structures, how a tree is traversed in different order (preorder, in-order, post-order, level-order), etc. Moreover, this tool would provide a simple language, by which students can write their own algorithms so that the execution of the algorithm is animated. This project is intended to create an exploration environment, in which students can learn through experimentation. This tool can be used as an effective supplement to the traditional classroom education and textbook for Data Structures and Algorithms courses. In this paper, a software application that features the visualization of commonly used data structures and their associated insertion and deletion operations is introduced. In addition, this software can be used to animate user-defined algorithms.

Study on Teaching Database Principles Course based on OBE Concepts

With the rapid development of information technology, database principles, as one of the core courses of computer science and technology majors, the quality of its teaching is directly related to the cultivation of students' practical work ability and innovation ability. However, the traditional teaching mode has problems such as emphasizing theory over practice, single assessment method and neglecting ability cultivation, which is difficult to meet the current social demand for high-quality technical talents. Therefore, this paper introduces the OBE (Outcome-Based Education) education concept into the teaching of "Principles of Database" course, and puts forward a series of teaching reform measures based on the OBE concept, including the formulation of specific teaching objectives, optimization of teaching content, innovative teaching methods, diversified course evaluation and assessment mechanism, etc., to realize the training of students' ability and innovation through the construction of student-centered and output-oriented teaching mode, and to realize the training of students' ability and innovation. By constructing a student-centered and output-oriented teaching mode, it realizes the enhancement of students' independent learning and innovation and practice ability, so as to make students become excellent talents of the new century with innovative consciousness and ability.

Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses

This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course evaluation criteria was formulated by integrating topics (sub-criteria) identified through topic modeling and interpreted based on transactional distance and technology acceptance theories. Second, the weights of the criteria in the hierarchical structure were determined based on topic proportions. Third, the AHP was employed to determine the overall relative advantage of online courses and their relative advantage within each criterion based on the hierarchical framework and criterion weights. The proposed approach was implemented on the datasets of 6940 reviews for knowledge-seeking courses in Art, Design, and Humanities (D1) and 44,697 reviews for skill-seeking courses in Computer Science, Engineering, and Programming (D2) from Class Central to determine ranking positions of nine courses from both D1 and D2 as alternatives. Results revealed common concerns among knowledge and skill-seeking course learners, encompassing “assessment”, “content”, “effort”, “usefulness”, “enjoyment”, “faculty”, “interaction”, and “structure”. The article provides valuable insights into the online course evaluation and selection processes for learners in D1 and D2 groups. Notably, both groups prioritize “effort” and “faculty”, while D2 learners value “assessment” and “enjoyment”, and D1 learners value “usefulness” more. This study demonstrates the efficacy of leveraging online learner reviews and topic modeling for automating MOOC evaluation and informing learners’ decision-making processes.

A Human-Centric Approach towards Equity and Inclusion in AI Education

Artificial Intelligence (AI) has become pervasive in modern lives, with AI generative tools driving further transformation. However, a notable issue persists: the underrepresentation of females and individuals from ethnic and racial minorities in the tech industry. Despite generally positive attitudes toward technology among young students, this enthusiasm often does not extend to aspirations for careers in the field. To address this disparity, many schools in the United States are now offering computer science and AI courses at the high school level. Nevertheless, students from underrepresented groups often feel disconnected from these subjects, leading to low enrollment rates. Research underscores that students' career aspirations are solidified between the ages of 10-14 yrs, highlighting the importance of engaging them with computer science and computing skills during this formative period. Leveraging the Bourdieusian concept of social capital, this paper proposes educational interventions tailored for elementary schools. By nurturing students' technical social capital, these interventions aim to foster an inclusive ecosystem from an early age, when aspirations are taking shape. Ultimately, the goal is to enhance the accessibility of computer science education and related skills, empowering young students from underrepresented groups to pursue higher studies and careers in computer science and AI fields.

Enhancing active learning through collaboration between human teachers and generative AI

To address the increasing demand for AI literacy, we introduced a novel active learning approach that leverages both teaching assistants (TAs) and generative AI to provide feedback during in-class exercises. This method was evaluated through two studies in separate Computer Science courses, focusing on the roles and impacts of TAs in this learning environment, as well as their collaboration with ChatGPT in enhancing student feedback. The studies revealed that TAs were effective in accurately determining students’ progress and struggles, particularly in areas such as “backtracking”, where students faced significant challenges. This intervention’s success was evident from high student engagement and satisfaction levels, as reported in an end-of-semester survey. Further findings highlighted that while TAs provided detailed technical assessments and identified conceptual gaps effectively, ChatGPT excelled in presenting clarifying examples and offering motivational support. Despite some TAs’ resistance to fully embracing the feedback guidelines-specifically their reluctance to provide encouragement-the collaborative feedback process between TAs and ChatGPT improved the quality of feedback in several aspects, including technical accuracy and clarity in explaining conceptual issues. These results suggest that integrating human and artificial intelligence in educational settings can significantly enhance traditional teaching methods, creating a more dynamic and responsive learning environment. Future research will aim to improve both the quality and efficiency of feedback, capitalizing on unique strengths of both human and AI to further advance educational practices in the field of computing.