Computer Science Degree Research Articles

Factor structure and invariance of the scale to measure teaching performance in the area of social sciences

The use of scales to evaluate teaching from the students’ perspective is a method frequently used in educational systems around the world. The objective of this study is to analyze the factorial structure of the Teaching Performance Evaluation Scale (EEDDocente, by acronyms in Spanish) designed with the purpose of providing information that favors decision-making based on evidence for the improvement of teaching in the area of Social Sciences, as well as measuring the invariance by School stage and Educational Program. The sample consisted of 1,849 students of the Bachelor’s Degrees in Law, Psychology, Accounting, Administration, Education Sciences, Communication Sciences, Computer Science, and Sociology of the School of Social and Administrative Sciences (FCAyS) of the Autonomous University of Baja California, Mexico. Based on a three-factor model that meets the fit and quality criteria, a Multi-group Confirmatory Factor Analysis (MGCFA) was performed to measure the invariance of the EEDDocente by School stage and Educational program. It is concluded that the three-factor model can be used to measure, from the students’ perspective, the performance of teachers in the Area of Social Sciences. Likewise, it is concluded that the invariance of the simultaneous measurement is achieved, providing evidence to perform mean difference analysis between the different Educational programs.

Analysis of algorithms as a teaching experience

Teaching analysis of algorithms to students in Computer Science degrees, using the approach popularized by Knuth in his series of books ``The Art of Computer Programming" and later by Sedgewick and Flajolet in the book ``An Introduction to the Analysis of Algorithms", is not a simple task since, in general, these students are more interested in the implementation of an algorithm than in the corresponding theoretical aspects. This approach concentrates on precisely characterizing the performance of algorithms by determining their best, worst and average case performance using a methodology based on symbolic tools such as recurrence relations and generating functions.The most difficult aspect is to understand the average case since this corresponds to studying the algorithm as its possible inputs vary: this represents the most important goal since generally students have no difficulty in understanding the best and worst cases, corresponding to particular input configurations.A compromise that has been successful over the years consists in teaching students the analytical aspects of the problem and then organize a simulation of the algorithm with a system of symbolic computation in order to precisely check the theoretical results.

An experience in the implementation of the flipped classroom instructional model in the computer science degree

AbstractIn recent years, education has undergone a profound transformation process, having gone from relying only on the traditional lecture to making full use of digital formats. This gradual process, accelerated by the COVID‐19 pandemic in the last years, has triggered innovative changes in the educational process. Within this context, many lecturers have adopted the flipped classroom instructional model, aiming to improve the motivation and involvement of the students. In this model, students must acquire certain theoretical knowledge at home, and the classes, with the help of the lecturer, are used for the more practical part. This article presents the results obtained when the flipped classroom model was implemented in the computer science degree. Specifically, during 2020–2021 and 2021–2022 courses, this instructional model was assessed in 15 subjects. Results show that the flipped classroom instructional model can be stated that it has improved the students' perception of the learning experience, students' dedication and engagement has been improved, students' perception of their understanding of the subject has improved. The faculty considers that the teaching experience has improved and is in favor of continuing with the experience in future courses.

The gap between bachelor’s degree graduates in health informatics and employer needs in Saudi Arabia

BackgroundIn the field of health informatics (HI), there is a crucial gap between employers’ needs and the output of academic programmes. Although industrial organisations and government agencies recognise the importance of training and education in the development and operation of health-information systems, advancements in educational programmes have been comparatively slow in terms of investment in healthcare information technology. This study aims to determine the gap between employer demands and academic programmes in HI in Saudi Arabia.MethodsThis mixed-methods study collected both qualitative and quantitative data. A qualitative content analysis was performed to identify the role of advertised HI jobs using two sources: Google and LinkedIn. In addition, university websites were searched to determine job opportunities for graduates with a bachelor’s degree in HI. Next, a quantitative, cross-sectional self-report questionnaire was administered to validate the findings of the qualitative data. Data obtained were analysed using SPSS, N-Vivo, and Microsoft Excel.ResultsThe study’s data were obtained from four sources: Google search engine, LinkedIn, five Saudi university websites, and 127 HI experts. The results show a discrepancy between academic programmes’ outputs and employer recruitment needs. In addition, the results reveal a preference for post-graduate degrees, either a master’s or PhD degree, with a bachelor’s degree in a health or medical discipline.ConclusionsEmployers tend to prefer applicants with a bachelor’s degree in computer science or information technology over those with a degree in HI. Academic programmes should incorporate more practical applications and provide students with a thorough understanding of the healthcare industry to better equip them as efficient future HI professionals.

Complete Integration of Team Project-Based Learning Into a Database Syllabus

Team project-based learning (TPBL) combines two learning techniques: 1) project-based learning (PBL) and 2) teamwork. This combination leverages the learning outcomes of both methods and places students in a real work situation where they must develop and solve a real project while working as a team. TPBL has been used in two advanced database subjects in Jaume I University (UJI)’s Computer Science degree program. This learning method was used for four years (academic years from 2018/2019 to 2021/2022) with positive outcomes. This study presents the project development, which includes teamwork formation, activities, timetable, and exercised learning competencies (both soft and specific). Further, the project’s results were evaluated from three different perspectives: 1) teamwork evaluation by teammates; 2) students’ opinions on the subject and project; and 3) subject final grades.

Guest Editorial: Blockchain and decentralised solutions for social good

Guest Editorial: Blockchain and decentralised solutions for social good

Origin Story: Denise Kuo

Attending college in the 1990s, my focus was financial independence and the possibility of travel funded by my employer. I believed the best route to both was in science, technology, engineering, and mathematics (STEM) and therefore I obtained a Bachelor’s degree in computer science. Entering the workforce in 1994 with knowledge and skills in software development worked out well. While working for consulting firms that specialized in custom software development and traveling weekly to some fun locations (Toronto, ON, Canada) and some less-fun locations (Warsaw, IN, USA), I also relocated to Jackson, Mississippi, USA. Most of the friendships I developed while living in Jackson were with people I met through my spouse, and most of them worked at the University of Mississippi Medical Center (UMMC), including Gerry McAlpin.1 In 2003, when my software consulting job ceased to exist,* Gerry had an idea for my next position. In her role as the managing editor of Hypertension, an American Heart Association (AHA) journal, Gerry was in the midst of a transition to use BenchPress for online peer review. Gerry made the case to the Editor-in-chief, Dr. John Hall, that someone with strong computer skills was just what the journal needed to develop and maintain the configurable letter templates. It only took a couple of weeks working part-time to set up the templates, and then Gerry was eager for help in clearing the backlog of email in the journal inbox. Answering email was surprisingly satisfying. Problem-solving and strong communication skills acquired in my […]

An Evaluation Tool for Extracurricular Activities to Reduce the Gender Gap in Computer Science

In the last few years, workforce with STEM (Science, Technology, Engineering and Math) competencies has proved to be crucial for countries' innovative capacity and global competitiveness. Yet women are vastly underrepresented in STEM and, in particular, in ICT and computer science fields, both among workers and degree holders: this gap hinders the possibility for ICT employment to be strengthened and for women to take advantage of career opportunities, thus perpetuating gender inequalities in these disciplines. To counteract these effects and attract girls towards ICT-related fields of study and careers, several initiatives have been organised all around the world, such as summer camps and dedicated extracurricular activities. However, these initiatives are usually not supported by proper evaluation tools allowing researchers and practitioners to understand the actual benefits of the carried-out activities on girls' competencies and future attitudes. In this paper, we propose an evaluation tool for extracurricular activities aimed at reducing the gender gap in ICT. The proposed tool aims at capturing both a quantitative and a qualitative evaluation, including an Implicit Association Test (IAT) along with a more traditional questionnaire consisting of thematic sections designed to analyse various aspects of the activities' impact on girls. The tool has been applied in the context of two summer camps related to national and international projects aimed at attracting girls towards computer science and STEM disciplines: the ‘Digital Girls’ project, organised since 2014 by the University of Modena and Reggio Emilia in collaboration with other universities and local institutions, and the “STEM for Future” Erasmus+ project. Based on the results obtained by the summer camp case studies, we discuss some critical elements that can hinder the efficacy of the evaluation tool, giving suggestions to overcome these potential issues.

Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks

Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks

USQA: A User Story Quality Analyzer prototype for supporting software engineering students

AbstractThe Standish Group Reports 83.9% of IT Projects fail, and one of the top factors in failed projects is the incomplete requirements or user stories. Therefore, it is essential to teach undergraduate students from computer science degree programs how to create complete user stories. Computer science programs include some subjects or topics involving requirements or user stories collection and writing, such as Requirements Engineering, Software Engineering, Project Management, or Quality Software Assurance. For that reason, we designed a web application called User Story Quality Analyzer (USQA) that uses Natural Language Processing modules to detect errors regarding aspects of usefulness, completeness, and polysemes in the user stories creation. The tool was proved from three perspectives: (1) a reliability test, where 35 user stories developed by experts were tested in the app to prove the prototype's reliability; (2) usability and utility analysis; 48 students interacted with the tool and responded a Satisfaction Usability Scale and an open‐ended question, the students reported a high usability score; (3) finally, error classification, we gathered 159 user stories processed by the system, and we classified the students' common errors considering incompleteness and polysemes. After the evaluations, we concluded that USQA could evaluate the user stories as an expert, which could help the professors/teachers/instructors in their courses by providing feedback to the students when they are writing user stories.

Comparing Boys’ and Girls’ Attitudes Toward Computer Science

Women are severely underrepresented in computer science (CS) degrees and careers. While student interest is a key predictor of success, little is known about how elementary students from underserved groups, such as girls, develop their interest in CS. To address this issue, we examined the differences in attitudes between upper elementary girls and boys towards CS after participating in a yearlong, inquiry-based CS curriculum designed for diverse learners. Pre-and-post surveys on students’ attitudes towards CS (n = 108) were delivered before and after student participation in the curriculum. Results from the survey showed only two demonstrated significant differences between boys and girls, favoring girls talking more with friends and family about CS and boys believing that computer scientists make a difference in the world. Even for these two items, the differences were of marginal significance and that significance would not survive correction for multiple comparisons. Follow-up, semi-structured interviews with 18 students painted a different picture. Girls displayed decreased interest in CS compared to boys with regard to self-efficacy and overall identification with the discipline. These differences highlight the importance of early intervention programs in leveraging the strengths and interests of participating girls. Based on our findings, we provide recommendations for CS educators and curriculum designers on better engaging girls in the discipline.

Women in Computer Science Degrees: A Cross-National and Longitudinal Study

This study focuses on the numerical gap in CS from different angles, whether it presents a similar behavior in all countries or not, and how it evolves over time. We have analyzed OECD statistics from 1998 to 2016 in twenty-nine countries and present results that show that this is a global problem. The average of women who graduated in CS is lower in 2016 than it was in 1998 and the tendency is to continue to diminish, although the situation is very different from some groups of countries to others. We have made statistical analysis that has allowed to present a country classification. This analysis has shown that there is a wide difference between countries such as Mexico, Italy and Belgium for example. We have also carried out statistical analysis of possible relations between economic and social variables for the countries studied along the almost 19 years’ span and have found no statistically significant correlation in global terms, which shows that the problem is complex and that the solutions adopted are only partial.

The STEM paradox: Factors affecting diversity in STEM fields

Women obtain more than half of U.S. undergraduate degrees in biology, chemistry, and mathematics, yet they earn less than 20% of computer science, engineering, and physics undergraduate degrees (NSF). Why are women represented in some STEM fields more than others? The STEM Paradox or Gender Equality Paradox show that countries with greater gender equality have a lower percentage of female STEM graduates. This phenomenon as well as other factors explaining gender disparities in STEM participation and some possible solutions are discussed.

DevOps: Is there a gap between education and industry?

AbstractDevOps has been identified by industry as one of the cornerstones of their development process. It is not just a set of tools but a set of principles and practices to build an efficient team improving the communication and collaboration. Its importance has greatly impacted even hiring processes being DevOps Engineer among the most recruited jobs according to LinkedIn. But it is not clear whether universities have noticed the magnitude this movement has attained in industry, despite the need of higher education and industry building up advances together. This led us to determine whether there is a gap between the training provided by higher education and the one expected from industry. For this aim, a questionnaire, defined after a careful review of the literature, has been run worldwide to answer the four research questions. The analysis arose several conclusions, such as higher prevalence of use than of training for most of the analyzed technological practices, except for those related to architecture, probably due to the migration cost these require. It was also found that the two practices with higher prevalence in industry, feedback and limit‐WIP, are scarcely trained in higher education. These conclusions provide interesting advice for future adaptations of computer science degrees.

Online Education vs Traditional Education: Analysis of Student Performance in Computer Science using Shapley Additive Explanations

Nowadays, the rapid development of ICT has brought more flexible forms that push the boundaries of classic teaching methodology. This paper is an analysis of online teaching and learning forced by the COVID-19 pandemic, as compared with traditional education approaches. In this regard, we assessed the performance of students studying in the face-to-face, online and hybrid mode for an engineering degree in Computer Science at the Lublin University of Technology during the years 2019-2022. A total of 1827 final test scores were examined using machine learning models and the Shapley additive explanations method. The results show an average increase in performance on final tests scores for students using online and hybrid modes, but the difference did not exceed 10% of the point maximum. Moreover, the students' work had a much higher impact on the final test scores than did the study system and their profile features.

Gamification and discovery learning: Motivating and involving students in the learning process

The widespread use of new technologies by students has forced universities to include active methodologies in their pedagogy. This process has accelerated due to the COVID-19 pandemic, introducing innovative changes in pedagogy. This has motivated many lecturers to increase student motivation. The need to keep the students' attention during long and tedious theoretical sessions has motivated this contribution. Furthermore, the need for students to develop their Web search skills and development of individual expertise and participation in a final group process that attempts to transform newly acquired information into a more sophisticated understanding has inspired this contribution. This paper presents the results obtained from the implementation of gamification and Discovery Learning instructional model in the Software Engineering subject of the Computer Science degree in the course 2021/2022. The joint use of gamification and the Discovery Learning instructional model through Webquests has never been studied before. They help students to better acquire the knowledge taught in class. The gap in the combination use of gamification and Discovery Learning instructional model compared to previous studies using one single method show an improvement in academic results, greater motivation on the part of the students, greater creativity and ability to put what they have learned into practice.

Development of a Predictive Model of Student Attrition Rate

Enrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model.

Design, development, and evaluation of a virtual reality game-based application to support computational thinking

Computational thinking (CT) has become an essential skill nowadays. For young students, CT competency is required to prepare them for future jobs. This competency can facilitate students’ understanding of programming knowledge which has been a challenge for many novices pursuing a computer science degree. This study focuses on designing and implementing a virtual reality (VR) game-based application (iThinkSmart) to support CT knowledge. The study followed the design science research methodology to design, implement, and evaluate the first prototype of the VR application. An initial evaluation of the prototype was conducted with 47 computer science students from a Nigerian university who voluntarily participated in an experimental process. To determine what works and what needs to be improved in the iThinkSmart VR game-based application, two groups were randomly formed, consisting of the experimental (n = 21) and the control (n = 26) groups respectively. Our findings suggest that VR increases motivation and therefore increase students’ CT skills, which contribute to knowledge regarding the affordances of VR in education and particularly provide evidence on the use of visualization of CT concepts to facilitate programming education. Furthermore, the study revealed that immersion, interaction, and engagement in a VR educational application can promote students’ CT competency in higher education institutions (HEI). In addition, it was shown that students who played the iThinkSmart VR game-based application gained higher cognitive benefits, increased interest and attitude to learning CT concepts. Although further investigation is required in order to gain more insights into students learning process, this study made significant contributions in positioning CT in the HEI context and provides empirical evidence regarding the use of educational VR mini games to support students learning achievements.

About The Author

About The Author

Factors Affecting Computer Science Student's Academic Performance During Covid-19

Abstract : In this research, the factors that affect the computer science student's academic performance are investigated in the scenario of online learning induced by the COVID-19 pandemic. It is found that while staying at home, and getting tutored online, the academic achievement of the students of Computer Science gets affected by the following group of factors: Family characteristics (family causal factors), Student's academic characteristics, Social and personal characteristics, Psychological and Health Related Factors, Institutional characteristics, Teachers (academic causal factors), Perceptions about Online Learning, Perception about Computers. As opposed to the general usage of only the CGPA as a measure of the academic performance, this research introduces the online self-study time (OSST) as well, as a measure of the academic performance. A total of 170 students had responded to a questionnaire administered to them to assess the effects of the identified factors. The questionnaire was checked for reliability and validity as well, using construct reliability, indicator reliability, convergent validity, and discriminant validity. The students were from various semesters of Bachelor of Computer Science degree program. The questionnaire data is subjected to the multinomial logistic regression analysis. Results have shown that there is relation that exists between the identified factors and the academic performance of the students that is reflected in their CGPA and the OSST. Keywords : COVID-19, Academic performance, Online learning, Online Self-study Time (OSST).