Computer Science Majors Research Articles

Students create prize‐winning app to keep everyone safer on campus

Four students from the University of Nevada, Reno, for their senior project in computer science class, created an app designed specifically to help women feel safer on campus. Kathleen Scafidi, Anna Desorcy, Michael Munakash, and Kurtis Bertauche were all computer science majors when they created the app Ur Next Route.

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.

“ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI

This study investigates how U.S. college students (N = 1001) perceive and use ChatGPT, exploring its relationship with societal structures and student characteristics. Regression results show that gender, age, major, institution type, and institutional policy significantly influenced ChatGPT use for general, writing, and programming tasks. Students in their 30s–40s were more likely to use ChatGPT frequently than younger students. Non-native English speakers were more likely than native speakers to use ChatGPT frequently for writing, suggesting its potential as a support tool for language learners. Institutional policies allowing ChatGPT use predicted higher use of ChatGPT. Thematic analysis and natural language processing of open-ended responses revealed varied attitudes towards ChatGPT, with some fearing institutional punishment for using ChatGPT and others confident in their appropriate use of ChatGPT. Computer science majors expressed concerns about job displacement due to the advent of generative AI. Higher-income students generally viewed ChatGPT more positively than their lower-income counterparts. Our research underscores how technology can both empower and marginalize within educational settings; we advocate for equitable integration of AI in academic environments for diverse students.

Students create prize‐winning app to keep everyone safer on campus

Four students from the University of Nevada, Reno, senior project in computer science class created an app designed specifically to help women feel safer on campus. Kathleen Scafidi, Anna Desorcy, Michael Munakash, and Kurtis Bertauche were all computer science majors when they created the app Ur Next Route.

A Study on English Learning Motivation of Computer Science Majors in China

The process of English learning among college students has a profound impact on their psychological factors and learning outcomes. English plays a crucial role in the field of computer science. It not only builds upon basic English but also encompasses a comprehensive understanding of computer science. Computer English serves as a vital tool for Computer Science (CS) students to improve their expertise and achieve better self-development. This paper primarily examines the current state of insufficient English learning motivation among college students majoring in CS in China, analyzes the significance and influencing factors of learning English, and subsequently proposes practical strategies to stimulate and cultivate college students' English learning motivation.

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.

A matching model for major orientation of computer science students based on random forest model

With the advancement of computer innovation, there are increasingly major computer bearings, such as software development, network security, artificial intelligence, data science, etc. When choosing the direction of computer science, students have to consider their interests, techniques, soft skills and numerous other components to utilize their qualities and accomplish their career objectives. This article centres on how to use machine learning methods to anticipate the direction of computer science students. Theres a solid relationship between students interests, techniques, and soft skills when choosing the direction of computer science majors. By employing a random forest machine learning show to prepare students ability characteristics, it is conceivable to anticipate which direction of specialization students are more appropriate for. Within the explore, we isolated the training, validation, and test sets, agreeing to the proportion of 6:2:2. We utilized the exactness rate to judge the models forecast exactness. After preparing, the expectation precision of this show increments, and the precision rate of the test set comes to 96%. This implies that this machine learning show can reasonably foresee computer science students heading and give them way better career improvement proposals. In the future, we will encourage optimising the demonstration to make strides in the forecast exactness and apply it to a broader run of areas.

Increasing Diversity in Lifelong AI Education: Workshop Report

AI is rapidly emerging as a tool that can be used by everyone, increasing its impact on our lives, society, and the economy. There is a need to develop educational programs and curricula that can increase capacity and diversity in AI as well as awareness of the implications of using AI-driven technologies. This paper reports on a workshop whose goals include developing guidelines for ensuring that we expand the diversity of people engaged in AI while expanding the capacity for AI curricula with a scope of content that will reflect the competencies and needs of the workforce. The scope for AI education included K-Gray and considered AI knowledge and competencies as well as AI literacy (including responsible use and ethical issues). Participants discussed recommendations for metrics measuring capacity and diversity as well as strategies for increasing capacity and diversity at different level of education: K-12, undergraduate and graduate Computer Science (CS) majors and non-CS majors, the workforce, and the public.

Rethinking Undergraduate Computer Science Education: Using the 4Es Heuristic to Center Students in an Introductory Computer Science Course

Rethinking Undergraduate Computer Science Education: Using the 4Es Heuristic to Center Students in an Introductory Computer Science Course

Science, technology, engineering, and mathematics undergraduates’ knowledge and interest in quantum careers: Barriers and opportunities to building a diverse quantum workforce

Efforts to build the workforce in support of the second quantum revolution are growing, including the creation of education programs that will prepare students for jobs in this area. We surveyed 186 undergraduate students with majors across the science, technology, engineering, and math (STEM) disciplines and followed up with group interviews to understand their perspectives. The project was designed to understand what these STEM students know about quantum and quantum career opportunities and their level of interest in pursuing a career related to quantum. We found that most of the students know very little about quantum. Nevertheless, except for students in the life sciences, there was an interest in quantum careers. Across STEM majors, women were less likely to express interest in quantum careers than men, but this difference disappeared when we examined only physical and computer science majors. Of the few students who had knowledge of quantum concepts, most learned about this topic from online media, especially online videos. Some students reported learning about quantum in high school classes, where it was taught as an extension beyond the usual topics of the course. The undergraduate STEM students in our study identified multiple ways they would like to learn more about quantum, including short videos, seminars, courses, certificates, and degree programs. Published by the American Physical Society 2024

A Comparative Analysis of Programming Language Preferences Among Computer Science and Non-Computer Science Students

In the face of the growing importance of programming skills across various fields, understanding student preferences for programming languages becomes crucial. This study delves into this very topic, examining which languages resonate most with computer science majors and students from non-computer science backgrounds. We don’t just identify the popular choices; we also explore the underlying reasons behind these preferences through surveys. The analysis reveals a fascinating interplay between factors like a student’s learning experience, their career aspirations, and even their interests, all of which influence their preference for specific programming languages. This newfound knowledge empowers us to refine programming education for a diverse student body, ensuring they’re well-equipped for the demands of the digital world. Our findings hold value for curriculum designers, educators, and industry professionals alike. By understanding the evolving demands and preferences of students, these stakeholders can craft more relevant and engaging programming education experiences. Ultimately, this fosters interdisciplinary collaboration in the digital age, a key element for success in today’s interconnected world. This research not only contributes to the growing body of knowledge on programming language preferences but also offers practical insights for the betterment of programming instruction and the promotion of collaboration across disciplines within the digital landscape.

Effective deep learning based grade prediction system using gated recurrent unit (GRU) with feature optimization using analysis of variance (ANOVA)

The prediction of final semester grades is a crucial undertaking in education, offering insights into student performance and enabling timely interventions to support their academic journey. This paper employs a deep learning approach, specifically gated recurrent unit (GRU), in conjunction with feature optimization using analysis of variance (ANOVA), to forecast final semester grades. The predictive model is trained and evaluated on a handcrafted grade prediction dataset, which contains the academic performance of the students during Plus 2 and from Semester 1 to Semester 4 of a group of Computer science and Engineering majors in Kerala. By processing historical academic records and contextual information, the GRU model learns to predict future performance accurately. To enhance the model's efficacy and interpretability, ANOVA is applied to optimize the feature selection process. This statistical technique identifies the most influential factors contributing to final grades, refining the model's predictive power while reducing dimensionality. The experimental results showcase the model's effectiveness in predicting final semester grades, demonstrating superior accuracy and performance compared to grade prediction using CNN with Bayesian optimization and LSTM with L1-Norm optimization.

Transfer Student Receptivity in Patriarchal STEM Contexts: Evidence of Gendered Transfer Student Stigma in Computer Science From a Mixed Methods Study

Research Questions: While community college transfer (i.e., upward transfer) represents an important mechanism for advancing equity across STEM fields, existing studies of gender and women’s participation within computer science have largely excluded the perspectives of upward transfer students. We address this gap in the literature by exploring transfer receptivity and gender discrimination within computer science, guided by the following questions: (1) How do upward transfer computer science students report their receptivity experiences, and how might this differ by gender? (2) How do upward transfer computer science students make meaning of receptivity experiences, and how might that meaning making be shaped by gender? Methods: We use a sequential mixed methods design, relying on longitudinal survey and interview data from upward transfer computer science majors, collected throughout students’ first year at the receiving university. Results: Findings reveal that, relative to men, upward transfer women report greater experiences of transfer stigma and challenges accessing resources at the receiving university. Qualitative findings document additional nuances in how upward transfer students—especially women—describe resilience as they navigate the university campus, encounter navigation challenges at the university, and make meaning of various manifestations of transfer stigma on campus. Contributions: In addition to implications for research and theory, we discuss what universities can do to foster a more receptive environment for upward transfer women. Specific recommendations focus on ensuring that spaces for women in computing are inclusive of transfer students and, likewise, creating supportive transfer cohort communities that are inclusive of women.

Research on cultivating innovative practical ability of students majoring in computer science and technology under the background of integration of specialization and innovation

Based on the perspective of the integration of specialization and innovation, in order to improve students' innovative practical abilities, this paper innovates the talent cultivation mode of computer science and technology majors, proposes the "three carriers" co creation progressive talent cultivation mode, optimizes the curriculum system of specialization and innovation integration, scientific research and education projects, and scientific and technological innovation activities as carriers, and jointly cultivates students' innovative practical abilities at the hierarchical level of school enterprise and teacher-student collaboration. At the same time, from the curriculum system propose relevant strategies in terms of innovative practice platforms, diverse assessment standards for innovative talents, etc., to promote the cultivation of innovative applied talents in computer science and technology majors.

An Exploration of the Integration of Marx’s Philosophical Thought and Modern Ethical Decision-Making Models for Artificial Intelligence

Abstract Artificial Intelligence is involved in more and more fields, and the debate of whether human beings will be replaced by it is more and more intense. This paper explores the paradigm of ethical decision-making of artificial intelligence from the perspective of Marx’s philosophical thought. Based on the basic elements of ethical decision-making, three decision-making methods are proposed to complete the judgment of decision-making consequences, and the calculation formula for the consequences of these methods is derived. Using the Bayesian learning method, a utility-maximizing agent effect function is constructed to implement the moral embedding policy. Measure the ethical fairness of decision-making algorithms based on statistical fairness orientation to propose fair decision-making methods. Through the example analysis, the practical application effect of the model is analyzed from two perspectives: public policy implementation and education. More than 40% of the users are more satisfied with the public policy implementation made by AI, and they are relatively satisfied with the implementation of AI governance policy at this stage, but there is still room for improvement. There is a difference in the cognition of students of education-related majors and computer science majors on the ethics of identity intelligence, and education majors pay more attention to the educational level and social virtue issues, such as educational sovereignty, educational purpose, educational value, educational evaluation, educational fairness, as well as the development of students, and integrity issues.

Exploring the Teaching Mode of English for Specialized Purposes (ESP) in Colleges and Universities under the PDCA Model

Abstract In this paper, ant colony algorithm and genetic algorithm are combined and applied to the teaching mode of English for Special Purposes (ESP) in colleges and universities, and the optimization of the teaching mode is realized through the PDCA model. The iteration speed of ant colony algorithm is better than that of genetic algorithm in the early stage. In comparison, the iteration speed of genetic algorithm is better in the later stage. By fusing these two algorithms, their respective advantages can be maximized. The study takes two computer science classes as an example, first conducts a needs analysis of the ESP course, and finds that the ratings in English foundation, learning materials, learning mode, and quiz mode are all higher than 5.5. By comparing the final exam scores of the experimental and control classes, it was found that the average score of the practical class was 3.5 points higher than that of the control class, which indicated that the ESP teaching of the PDCA cycle effectively improved the students’ academic performance. The average score of students’ recognition of the ESP teaching model of PDCA cycle was 4.252, while the ratings in satisfaction, participation in classroom interactions, and learning effect were all over 4.1. The analysis of learning motivation shows that “for better employment”, “for promotion”, and “to pass the exam” are the top three motivations for students to choose to learn English for computer science majors, with scores of 175, 174, and 170, respectively. This study effectively demonstrates the potential of combining ACO and Genetic Algorithm in education.

Development of Civics Course for College Students Based on Integrated Transfer Learning Strategy

Abstract This paper explores how to effectively predict and analyze students’ Civics course grades and their correlation with students’ behaviors based on the integrated transfer learning strategy to improve the teaching quality of Civics courses for college students through the integrated transfer learning strategy. The feature engineering method is used to extract the achievement features, and the achievement analysis and prediction model is established by Stacking integrated learning method combined with various algorithms such as KNN, linear regression and decision tree. By predicting and analyzing the Civics grades of college A students, the results show that the average error between the expected grades and the measured grades of computer science majors is 4.00 points. In contrast, biological science majors underestimate it by 5.41%. In addition, the cluster analysis of students’ behaviors using the k-mediods algorithm revealed significant differences between students with excellent academic performance and those with poorer performance in behaviors such as frequency of consumption and number of books borrowed. The integrated transfer learning strategy has a better application effect in the prediction and analysis of the performance of the Civics course, which can provide a powerful support to improve the quality of teaching.

Knowledge Base Development for Second Language Learning in the 3D Virtual Space

In our study first we provide a short overview of the 3D virtual library project which started about ten years ago as part of the Cognitive Infocommunications (CogInfoCom) research. The current implementation of the virtual library model exploits the 3D features of the MaxWhere Seminar System. In our study we would like to summarize the classroom experiences that we brought together in teaching English as a second language for students of computer science majors at the Faculty of Informatics, University of Debrecen in the academic year of 2020 and 2021. Our main purpose was to improve the students’ linguistic knowledge and to collect their opinions and suggestions about the content of the learning material of our virtual library and where they think is necessary to modify it. Summarizing their views, we decided to add further vocabulary items, contexts and explanations to the material. In addition, their achievements proved that successful language learning needs carefully prepared tests and exercises which support self-assessment and increase motivation. In general, the more tests are available the more efficient the learning process is. But preparing good and varied tests manually is a relatively slow and exhausting work. Therefore, we intended to use JavaScript technology to develop an algorithm which can generate tests and exercises automatically, based on the knowledge base of the virtual library.

Design of an Online Programming Platform and a Study on Learners’ Testing Ability

Online judge (OJ) systems are essential in programming education as they efficiently evaluate learners’ programming skills and reduce instructor workload. However, these systems often overlook the importance of software-testing concepts. To address this gap, we developed a system called Pytutor that integrates software-testing concepts to assess learners’ programming abilities and proficiency by exploring students’ test cases and learning behaviors. Drawing on software engineering theory and practical techniques, test capabilities are evaluated by analyzing the code coverage and mutation testing of Defining Test Cases. Since our experiment is conducted in an online environment, we can collect students’ learning behaviors and further analyze the relationship between software engineering abilities and learning behaviors. We also analyzed the differences in programming and testing abilities between computer science majors and non-computer-science majors. Our findings suggest that better testing abilities may contribute to the improvement in programming abilities, whereas in the current Taiwanese education context, computer science majors do not necessarily have better testing abilities. This result provides suggestions for us to strengthen software-testing education no matter which type of students it is targeted at.

Discussion on the Teaching Reform of Java Programming Course for Computer Science Majors in Colleges and Universities

In the new era, society has put forward higher requirements for the training of talents in colleges and universities, which needs to keep pace with the development of the times in the Java programming course for college computer majors, and constantly improve and optimize the teaching strategy. From the current situation, there are some problems in the teaching of Java programming courses for computer science majors in colleges and universities, such as the teaching content is not updated in time, the teaching method is too single, etc., which needs to be addressed in the teaching reform to solve the current problems. The article explores the importance of teaching reform of Java programming courses for computer majors in colleges and universities and gives effective teaching strategies according to the current teaching situation.