Undergraduate Computer Science Programs Research Articles

STUDENT DROPOUT PREDICTION USING MACHINE LEARNING

In a higher education environment, we considered the likelihood of probable dropouts from a first-year undergraduate Computer Science program. In order to achieve this, data from five academic sessions were obtained from the Department of Computer Science, University of Benin, Nigeria. Out of nine hundred and forty seven (947) data obtained, only a total of nine hundred and six (906) was usable after cleaning and preprocessing. Six distinct classifiers including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANN) were modeled for the prediction of student success and dropouts. The performance six were stated to have performed on average at 90.4%, 98.9%, 98.5%, 97.4%, 96.0% and 97.3% respectively. Although there wasn't much of a performance difference between the DT, SVM, and LR, the LR model was chosen for deployment since it performs better than the other two models in terms of F1_score and Recall.

Bolstering the Persistence of Black Students in Undergraduate Computer Science Programs: A Systematic Mapping Study

Background: People who are racialized, gendered, or otherwise minoritized are underrepresented in computing professions in North America. This is reflected in undergraduate computer science (CS) programs, in which students from marginalized backgrounds continue to experience inequities that do not typically affect White cis-men. This is especially true for Black students in general, and Black women in particular, whose experience of systemic, anti-Black racism compromises their ability to persist and thrive in CS education contexts. Objectives: This systematic mapping study endeavours to (1) determine the quantity of existing non-deficit-based studies concerned with the persistence of Black students in undergraduate CS; (2) summarize the findings and recommendations in those studies; and (3) identify areas in which additional studies may be required. We aim to accomplish these objectives by way of two research questions: (RQ1) What factors are associated with Black students’ persistence in undergraduate CS programs?; and (RQ2) What recommendations have been made to further bolster Black students’ persistence in undergraduate CS education programs? Methods: This systematic mapping study was conducted in accordance with PRISMA 2020 and SEGRESS guidelines. Studies were identified by conducting keyword searches in seven databases. Inclusion and exclusion criteria were designed to capture studies illuminating persistence factors for Black students in undergraduate CS programs. To ensure the completeness of our search results, we engaged in snowballing and an expert-based search to identify additional studies of interest. Finally, data were collected from each study to address the research questions outlined above. Results: Using the methods outlined above, we identified 16 empirical studies, including qualitative, quantitative, and mixed-methods studies informed by a range of theoretical frameworks. Based on data collected from the primary studies in our sample, we identified 13 persistence factors across four categories: (I) social capital, networking, & support; (II) career & professional development; (III) pedagogical & programmatic interventions; and (IV) exposure & access. This data-collection process also yielded 26 recommendations across six stakeholder groups: (i) researchers; (ii) colleges and universities; (iii) the computing industry; (iv) K-12 systems and schools; (v) governments; and (vi) parents. Conclusion: This systematic mapping study resulted in the identification of numerous persistence factors for Black students in CS. Crucially, however, these persistence factors allow Black students to persist, but not thrive, in CS. Accordingly, we contend that more needs to be done to address the systemic inequities faced by Black people in general, and Black women in particular, in computing programs and professions. As evidenced by the relatively small number of primary studies captured by this systematic mapping study, there exists an urgent need for additional, asset-based empirical studies involving Black students in CS. In addition to foregrounding the intersectional experiences of Black women in CS, future studies should attend to the currently understudied experiences of Black men.

Analisis Tracer Study Alumni Program Studi S1 Ilmu Komputer UIN Sumatera Utara

This tracer study collects information and analyzes the profiles of alumni of the Computer Science undergraduate study program at UIN North Sumatra. The method used in this research is a descriptive method that will describe the alumni profile. Data collection in this research used an online survey method by distributing a questionnaire consisting of several questions as the main instrument. The aim of this research is to evaluate the effectiveness of the UIN North Sumatra Computer Science undergraduate study program in providing education and preparing graduates who are competent and ready to work and to evaluate the quality of education received by alumni during the study period. This research analyzes the suitability of alumni to market needs by distributing questionnaires to assess the Cumulative Achievement Index obtained by alumni at the end of their studies, the length of the alumni's study period, the length of the alumni's waiting period to get a job, the suitability of the alumni's field of work and the scope of alumni's work. The results of this research can provide a better understanding of how study programs can produce graduates who are competent and ready to face challenges in the world of work and can also provide recommendations for institutions to improve the quality of education and relevance of the curriculum to better suit the needs of the current world of work. Keywords: tracer study, alumni, graduates, balance of work fields, Computer Science

Computing trust: on writing ‘good’ code in computer science education

ABSTRACT What does it mean to produce trustworthy code for computer scientists? Based primarily on ethnographic fieldwork in an undergraduate computer science program in Singapore, this article explores what it means for computer science students to write ‘good code.’ In doing so, it explores the values that underlie ideas of trust in the computer science discipline. Drawing on the work of Rebecca Bryant, this article shows how, as students learn to become ‘good at’ writing code that is technically functional, aesthetically un-individuated, and decontextually efficient, they also learn to become ‘good’ computer scientists. These standards of good code are distributed across human and nonhuman actors and provide a framework for ‘trustless trust’ in code. That is, while computer science often assumes an omnipresence of mistrust, this article argues that the production of ‘good’ code and ‘good’ computer scientists works to build a system of distrust for computer scientists. At the same time, becoming a good computer scientist is intimately intertwined with students’ selfhoods, undermining the foundation of trustless trust even as the ideal of objectively ‘good’ and trustworthy code cuts this contradiction from view.

NNeduca: A software environment to teach artificial neural networks

AbstractArtificial neural networks are an integral part of the curriculum in many undergraduate computer science programs. This paper introduces NNeduca, a simulation software system designed to teach undergraduate students the fundamental concepts of artificial neural networks. The system is written in Java and aims to provide an intuitive user interface, support for research and learning activities, and the ability to expand. This tool allows students to design and train a neural network with any architecture they desire and visualize some artificial neural network theoretical concepts such as definitions, topologies, training methods, and structure. An overview of educational software systems to aid in learning the fundamental concepts of neural networks was created based on a systematic literature review. A comparison of existing simulation systems and the developed NNeduca tool was performed in this paper. The evaluation was carried out using a newly developed model for assessing the quality of selected systems using established criteria formed as a result of a thorough examination of relevant methods of evaluating educational software. The usability evaluation results show that the NNeduca tool received the highest ratings, indicating that it can significantly improve the teaching process in artificial neural network courses.

PROSPER (Project, Sustainability, and Perseverance) Learning Model in English for Computer Science

We propose the PROSPER (Project, Sustainability, and Perseverance) Learning Model in English for Computer Science (ECS), a course provided by undergraduate Computer Science programs in Indonesian Higher Education Institutions. PROSPER has eight competencies: (1) System Thinking; (2) Critical Thinking; (3) Envisioning for the Future; (4) Personal Involvement; (5) Interpersonal Relations and Collaboration; (6) Tolerance for Ambiguity and Certainty; (7) Communication and Use of Media; and (8) Strategic Action. The syntax refers to Project-Based Learning and has eight stages: (1) choosing the theme of the project; (2) open-class discussion on the correlation between the topic and sustainability; (3) meeting the experts; (4) structuring the project by considering its contribution to sustainability; (5) executing the project in a sustainable way; (6) presenting the project; (7) evaluating the project; and (8) publishing the project. PROSPER model highlights the concept of sustainability and increases awareness, which has real-world relevance on a global scale. The urgency of incorporating sustainability into ECS is intimately tied to student perseverance, which is anticipated to educate them to be change agents for a better world in the present and future.

CS202X and SEP-KA Update

In a nutshell, CS202X refers to the current effort to revise the curricular guidelines for undergraduate degree programs in computer science. These revisions are being managed by a Steering Committe comprised of representatives from the ACM, IEEE-Computer Society, and AAAI. Following the previous curricular revisions in 2001 (CS'01) and 2013 (CS'13), the proposed guidelines are broadly partitioned into categories referred to as Knowledge Areas (KAs). Each KA has an associated sub-committee consisting of a Chair and members from the Steering Committee, as well as subject matter experts from the computing community. SEP refers to the Society, Ethics, and Profesionalism KA within the proposed CS202X curricular report.

Peningkatan Skil Presentasi Ilmiah Berbahasa Inggris Mahasiswa di Universitas Bumigora Berbasis Strategi Graphic Organizers (GOs)

The GOs (Graphic Organizers) strategy, which was used in reading comprehension and writing skills research, was then developed into a strategy that could be used as a medium for developing ppt materials. and as an effective and efficient presentation strategy to be used in scientific presentations. This GOs strategy is applied in guiding students in the computer science undergraduate study program in the form of community service (PkM) aimed at increasing student creativity as participants in developing GOs-based creative materials and developing communication skills in delivering scientific presentation materials in class. . This activity was carried out for level I students at Bumigora University with a total of 30 participants. The following are the solutions and steps involved in this PkM activity, namely the use of Graphic Organizers as an effective solution in providing guidance and improvement in conducting scientific presentations carried out by PkM partners. . There are two methods used in providing guidance on the use of the GOs strategy, namely the lecture method where they are given an understanding of the basic concepts of GOs and the simulation method or practice of implementing the Graphic Organizers strategy directly which is carried out by the facilitator/PKM implementation team and followed by the participants. in a participatory manner. The results of this activity show good results and the level of achievement of its use is high. Based on structured observations, it reached a score of 3.7 with a percentage of 73%. The second evaluation was carried out by measuring the perceptions of GOs users, which was a score of 3.8 with a percentage of 77%. Evaluation based on this perception value is high (3.01-4.0).

Improved Yuanpei computer science teaching: For secured development of developing countries

This paper digs into the developmental strides achieved by some countries through the use of skill-oriented computer education. The involvement of the various governments through policy making, implementation, monitoring, funding, curriculum amendments, liberalization of computer science undergraduate program structure, the introduction of skill-oriented projects, is x-rayed. These parameters provide insight into the successful outcomes of these economies through investments in Computer education. The methodology adopted is a review of literature on various Computer Education Policies, Implementation methods adopted, as well as monitoring methods for sustainability. Our findings show that three reviewed Countries were able to develop skills through the researcher’s computer education, that took over the technology world and helped advance their economies. While China focused on Hardware through task-free and funding of hardware-producing companies, the UK, through Raspberry Pi, focused on teacher and students’ training in Software development. The United States on the other hand focused on Research and development and making ICT passing a prerequisite for college. The researcher recommends: Redesign of computer curriculum to teach coding to primary and secondary school students, training of teachers in specialized computer areas, and have them replicate the knowledge to other teachers, federal funding and monitoring of new computer education policies, including a minimum of a credit pass in computer subject as a prerequisite for studying computer science. Finally, the adoption of accelerated skill-based teaching of Computer science in core areas of beneficial disruptive Technology, through an intensive program such as “Yuanpei” would transform and cause desired sustainable development.

Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

The AI-Atlas: Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid

We present the “AI-Atlas” didactic concept as a coherent set of best practices for teaching Artificial Intelligence (AI) and Machine Learning (ML) to a technical audience in tertiary education, and report on its implementation and evaluation within a design-based research framework and two actual courses: an introduction to AI within the final year of an undergraduate computer science program, as well as an introduction to ML within an interdisciplinary graduate program in engineering. The concept was developed in reaction to the recent AI surge and corresponding demand for foundational teaching on the subject to a broad and diverse audience, with on-site teaching of small classes in mind and designed to build on the specific strengths in motivational public speaking of the lecturers. The research question and focus of our evaluation is to what extent the concept serves this purpose, specifically taking into account the necessary but unforeseen transfer to ongoing hybrid and fully online teaching since March 2020 due to the COVID-19 pandemic. Our contribution is two-fold: besides (i) presenting a general didactic concept for tertiary engineering education in AI and ML, ready for adoption, we (ii) draw conclusions from the comparison of qualitative student evaluations (n = 24–30) and quantitative exam results (n = 62–113) of two full semesters under pandemic conditions with the result of previous years (participants from Zurich, Switzerland). This yields specific recommendations for the adoption of any technical curriculum under flexible teaching conditions—be it on-site, hybrid, or online.

Is GPA Enough? A Platform for Promoting Computer Science Undergraduates’ Pursuit of Career Related Extracurricular Activities

Despite the perceived value of extracurricular experience, higher education relies heavily on grade point average (GPA) as a measure of undergraduates’ academic success. When used as a singular standard assessment, GPA, which is based on student progress and completion of coursework, may inadvertently steer undergraduates away from valuable out-of-class experiences that might enhance their employability after graduation. With this premise in mind, the current study proposes to supplement GPA scores in an undergraduate Computer Science program with a wholistic assessment known as an Innovation, Competency, and Experience (ICE) score. The ICE score is a point system for documenting and rewarding students’ extracurricular activities, in addition to their GPA scores. We designed and developed a web-based technology platform called RadGrad to implement ICE scores and promote student engagement and participation in extracurricular activities. Preliminary data shows that over 36% of students started to use RadGrad, even though its use was completely voluntary. More than half of those students planned and participated in various extracurricular activities, ultimately earning ICE points. Importantly, the ICE score deployed through RadGrad demonstrates the potential value of a supplemental assessment to GPA, which can promote extracurricular experiences relevant to students’ future careers beyond coursework.

The Impact of Appropriate Planning and Robust Evaluation Strategies on Continuous Improvement of Student Learning

This paper discusses a unified framework for systematic assessment and evaluation of student outcomes for a modern computer science (undergraduate) program seeking ABET accreditation. The framework uses a carefully designed sampling plan, informed by robust analysis and experiences over more than five years, to evaluate and assess student outcomes. The proposed framework is expected to enhance student learning and faculty satisfaction. The effectiveness of the proposed framework is validated using direct and indirect assessment data gathered from different stakeholders. Moreover, two consecutive, full six-years each, ABET accreditations (2013-2019 and 2019-2025) ascertain the robustness of the proposed framework.

Collaborative and teamwork software development in an undergraduate software engineering course

Two key elements of modern software development are collaboration and teamwork. Current methodologies (e.g., agile) and platforms are based on these key elements. This paper describes our experience in stimulating collaboration and teamwork activities of students in the context of a software engineering course at the third year of an undergraduate program in computer science at the University of Milano-Bicocca in Italy. The students were asked to develop a software project in teams of 3 to 5 students for the final exam of the course. The students used GitHub as a collaborative software development platform. In addition, they analyzed the quality of the developed software through SonarQube. The students were also asked to perform project management tasks (e.g., the Gantt) using the Microsoft Project tool. At the end of the course, we gathered the student feedback through a questionnaire on their collaboration and teamwork experience (through GitHub and Microsoft Project tools) and on the use of a software analysis assessment tool, i.e., SonarQube. From their feedback, the students were enthusiastic about working in teams for their project development and about learning how to use tools which are exploited not only in the academic world but also in industry.

Designing and running a pre-college computing course

Enrollments in undergraduate computer science programs have declined steadily over the past few years. Overall, high school computer science curriculum is limited to either basic usage of Microsoft...

Generation CS

Across North America, universities and colleges are facing a significant increase in enrollment in both undergraduate computer science (CS) courses and programs. The current enrollment surge has exceeded previous CS booms, and there is a general sense that the current growth in enrollment is substantially different from that of the mid-1980s and late 1990s. For example, since the late 1990s, the U.S. Bureau of Labor data shows that the number of jobs where computing skills are needed is on an upward slope [1], illustrating the increased reliance our society has on computing. We also know that more disciplines are becoming increasingly reliant on large amounts of data, and that handling this data effectively depends on having good computational skills. This makes computer science courses at all levels of greater interest to students from other majors.

High Performance Computing education in an Indian engineering institute

Quality of education can be enriched with better outcome in undergraduate (UG) computer science and engineering program with the introduction of High Performance Computing (HPC) courses into the UG curriculum. The paper gives the background of the author’s institution, processes of curricula enhancement, infrastructure development to enable students to carry out the work and records the students’ achievements academic year wise. The paper also discusses the evolution and continuous upgrading of the HPC courses along with the issues and challenges experienced. The paper gives the details about the courses along with the process of teaching and evaluation. It lists the students’ research achievements with a focus on HPC, for each academic year. Although the paper has focused on UG HPC education for the past seven academic years, the author also provides additional information about HPC subjects introduced to postgraduate (PG) students of computer science and engineering at various levels in the academic year 2015–16. The paper aims to serve as reference for those faculty and students who wish to introduce similar HPC courses at their schools by making use of the best practices that helped us in providing better inputs to our students to attain laudable results.

An Approach for Attainment of Relevant Program Outcomes through Microcontroller Laboratory Course

Embedded product design has emerged as an important area of study in the curriculum for undergraduate program in Computer Science. This necessitates the inclusion of hardware courses like Digital Electronics, Computer Organization and Microcontrollers as pre-requisite courses, in the lower semesters. This helps students to develop keen interest, skill and proficiency in the area of Embedded system design. This also helps them to compete with peers from other engineering branches in the domain of Embedded Systems. Here we present a set of activities, their related outcome based assessment techniques and outcome based strategies applied to the laboratory course on 8051 Microcontroller laboratory in Computer Science&Engineering at the IV Semester level. This laboratory was an extension of the hands on experience the students had in the course on Digital Electronics in the III Semester. The laboratory course on 8051 Microcontrollers was based on the usage of 8051 compatible Atmel 89C51ED2. The course was designed to consist of initially, conducting simple programming exercises using 8051 assembly language and then simple interfacing exercises using Embedded C programming. Later when they developed some proficiency and familiarity in 8051 environment student' teams were made to work on course projects which required them to design and build projects using 8051 or any other microcontroller of their choice. This promoted their self-learning, improved their knowledge of programming the microcontroller to interface with the outside world to develop useful applications and provide solutions to some common problems in society. This activity resulted in enhanced motivational levels amongst students, increased their involvement in the team and improved their knowledge due to self learning.

Practical coinduction

Induction is a well-established proof principle that is taught in most undergraduate programs in mathematics and computer science. In computer science, it is used primarily to reason about inductively defined datatypes such as finite lists, finite trees and the natural numbers. Coinduction is the dual principle that can be used to reason about coinductive datatypes such as infinite streams or trees, but it is not as widespread or as well understood. In this paper, we illustrate through several examples the use of coinduction in informal mathematical arguments. Our aim is to promote the principle as a useful tool for the working mathematician and to bring it to a level of familiarity on par with induction. We show that coinduction is not only about bisimilarity and equality of behaviors, but also applicable to a variety of functions and relations defined on coinductive datatypes.

Software Project Management as Team Building Intervention

The courses of software project management (SPM) and Software engineering (SE) are regularly taught at undergraduate degree program in computer science. Students of these programs also have to undertake projects as part of various courses during their degree program. The purpose of this research is to assess whether the software engineering or the software project management course had enhanced the cohesion among team members while undertaking a six month or a year-long project. The teaching of these two courses is considered as a team building intervention in this research. A total of 167 students returned a modified version of Group Environment Questionnaire distributed to 200 students. Off these, 81 were those who had taken the SE course and subsequently did a project before they had taken SPM course. The rest of the students (85) consisted of those who had taken SPM and had undertaken final project. The results of this paper indicates that the SPM as compared to SE as a team building intervention had a better effect on team cohesion. The paper has successfully identified a single course that can enhance the performance of students as a team in contrast to proposing all the courses taught at the undergraduate computer science degree program as intervention for better team building and team work as proposed by Hogan & Thomas, 2005.