Capstone Project in Computer Science, Engineering, and Data Science: Learning Through Multiple Teaching and Learning Activities

In the universities where undergraduate Computer Engineering (CE) and Computer Science (CS) programs are offered, people often wrongly argue that the CE program should be offered under College of Engineering instead of under the college where CS program is offered, this is mostly because they think CE program has “Engineering” word in it. This school of thought does not realize that by this, it will take CE program apart from CS program, which will break the natural alliance of both of these programs. In this paper, this natural alliance of CE and CS programs is discussed, which gives strong argument that both CE and CS programs should be put together under the same college. To further support these arguments of putting CE and CS programs together, the best practices adopted in this regards by the top 10 universities in KSA, USA, UK and other European Universities are studied and results are provided to prove that CE and CS programs should be put together under the same college. Since, this is the current debate at AlBaha University in Saudi Arabia (KSA), therefore the case of AlBaha University is presented to settle the argument.

[This Proceedings paper was revised and published in the 2018 issue of the journal Issues in Informing Science and Information Technology, Volume 15] ABSTRACT Mathematics is fundamental to the study of Computer Science. In Sri Lankan state universities, students have been enrolled only from the Physical Science stream with minimum ‘C’ grade in Mathematics in the advanced level examination to do a degree program in Computer Science. In addition to that universities have been offering some course units in Mathematics covering basis in Discrete Mathematics, Calculus, and Algebra to provide the required mathematical maturity to Computer Science under-graduates. Despite of this it is observed that the failure rate in fundamental theoretical Computer Science course units are much higher than other course units offered in the general degree program every year. The purpose of this study is to identify how Advanced level Mathematics and Mathematics course units offered at university level do impact on the academic performance of theoretical Computer Science course units and to make appropriate recommendations based on our findings. Academic records comprised of 459 undergraduates from three consecutive batches admitted to the degree program in Computer Science from a university was considered for this study. Results indicated that Advanced level Mathematics does not have any significant effect on the academic performance of theoretical Computer Science course units. Even though all Mathematics course units offered in the first and second year of studies were significantly correlated with academic performance of every theoretical Computer Science course unit, only the Discrete Mathematics course unit highly impact-ed on the academic performance of all three theoretical Computer Science course units. Further this study indicates that the academic performance of female undergraduates is better than males in all theoretical Computer Science and Mathematics course units.

Several institutions have recently introduced Introduction to Data Science courses that involve a substantial programming component and do not require CS1 as a prerequisite. Programming and computational thinking are central to the emerging discipline of data science, and so there is overlap between traditional CS1 courses and Introduction to DS.Partly because of the evident societal significance of data science and because data science does not have the problematic reputation of computer science, Intro to DS can attract new and diverse audiences that may not have been interested in taking CS1.We explore Intro to DS as a possible alternative path into computing: what are the learning goals in Intro to DS that involve programming and/or computational thinking? How generalizable are the problems students solve in Intro to DS to what students would encounter in future studies? Is it feasible for students to pursue a data science sequence rather than CS1-CS2 and be prepared for a career that uses data science? To what extent can a pathway through a data science sequence diversify the population of students who graduate from degree programs in computer science and data science?We survey the Introduction to Data Science courses offered in North American post-secondary education, and focus on a data science sequence that uses the R programming language and does not require CS1 at an R1 institution as a case study.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Attracting and Retaining Women in Computer Science and Engineering: Evaluating the Results Abstract Computer science and engineering communities have been exploring a variety of activities and techniques to attract and retain more students, especially women and minorities, to computer science and computer engineering degree programs1. This paper briefly describes the efforts and results of a plan for actively recruiting young women into undergraduate computer engineering and computer science programs hosted by the University of North Texas (UNT). It also describes a series of activities aimed at improving the retention rate of students already in our programs, particularly during the freshman year. Such recruitment and retention efforts are critical to the country’s efforts to increase the number of engineering professionals, and are a priority for the Computer Science and Engineering (CSE) Department at UNT. We initially designed a three-part plan to achieve our recruitment and retention goals: • Sponsorship of portable and mobile summer computer engineering robotics camps for middle and high-school women students, coordinating with the regional Girl Scouts councils and other organizations to help with the recruitment; • Creation of an Ambassador program using young women currently enrolled in the upper division of current computer science and computer engineering programs as a form of outreach to area high schools and junior colleges; and • Expansion of our successful undergraduate mentoring program by using the Ambassador cohort mentioned above as mentors for incoming freshman and first-year transfer women entering our programs. This paper will focus primarily on the summer camp program and present an analysis of the results we have seen to date after 2 years of camp activities. Results from student surveys and parent surveys will be presented. The paper concludes with recommended changes and improvements in the program, as well as a discussion of adaptations that would create programs suitable for implementation at other institutions as well as programs aimed at a different target population. The three phases previously enumerated each build upon currently accepted educational practices. The robotics summer camp in particular used problem-based learning approaches, similar to those described in The Power of Problem-Based Learning2. This study clearly demonstrates the significant benefits of teaching students how to think by using a problem-based approach. We also developed a mobile laboratory so that the summer camp can be held at several locations, thus broadening and expanding the reach of the camps to traditionally underserved populations and geographical regions. Similarly, both the ambassador and mentoring programs derive from research showing how the recruitment and retention of women in engineering- related fields can be increased by providing opportunities to develop student-to-student relationships3, 4.

As part of the development of a new interdisciplinary initiative in data science that draws from statistics, mathematics, computer science, and the social sciences, we have developed a new introductory CS course that emphasizes data science and that we refer to as DataCSCi. Unlike other introductory data science courses, such as Berkeley's Data 8, our course retains the broad array of concepts necessary not only to introduce programming principles related to data science, but also to prepare students for the second course in our standard introductory computer science sequence. In particular, the course includes coverage of recursion (numeric and structural), unit testing, linked data structures, and other concepts we rely upon in subsequent courses in computer science. At the same time, we introduce students to a wide variety of techniques and approaches that support them in their subsequent work in data science, including techniques for wrangling, cleaning, and visualizing data. We achieve this combination of breadth and depth through two core approaches: We focus on a spiral use then implement approach and we focus on a functional model of programming using Scheme/Racket. While Python and R are the most commonly used languages for data science, we find that Scheme works particularly well to introduce students to concepts both complex, like map-reduce, and simple, like list filtering. In this paper, we report on the design of the curriculum, particularly the capstone project and the ways in which we incorporate the burgeoning subfield of data science for social good.

Aim/Purpose: The purpose of this study is to identify how Advanced level Mathematics and Mathematics course units offered at university level do impact on the academic performance of theoretical Computer Science course units. Background: In Sri Lankan state universities, students have been enrolled only from the Physical Science stream to do a degree program in Computer Science. In addition to that, universities have been offering some course units in Mathematics to provide the required mathematical maturity to Computer Science undergraduates. Despite of this it is observed that the failure rates in fundamental theoretical Computer Science course units are much higher than other course units offered in the general degree program every year. Methodology : Academic records comprised of all 459 undergraduates from three consecutive batches admitted to the degree program in Computer Science from a university were considered for this study. Contribution: This study helps academics in identifying suitable curricula for Mathematics course units to improve students’ performance in theoretical Computer Science courses. Findings: Advanced level Mathematics does not have any significant effect on the academic performance of theoretical Computer Science course units. Even though all Mathematics course units offered were significantly correlated with academic performance of every theoretical Computer Science course unit, only the Discrete Mathematics course unit highly impacted on the academic performance of all three theoretical Computer Science course units. Further this study indicates that the academic performance of female undergraduates is better than males in all theoretical Computer Science and Mathematics course units. Future Research: Identifying other critical success factors contributing to the students’ academic performance of the theoretical Computer Science through empirical studies

Background The last 20 years or so has seen a dramatic growth in the demand for professionals trained in information and communication technologies. This growth was partly driven by tremendous technological advances, such as the emergence of computer networking, including wireless networking, graphical user interfaces, and the Internet, the World-Wide Web, and their applications. But the growth in demand was also driven by a much greater realization on the part of many organizations and individuals of the importance of information and communication technologies for their well being, and the more widespread use of these technologies by individuals whose primary area of expertise was not in information technology (Dalhom & Mathiassen, 1977; Denning, 2001a; Denning 2001b). While programs in traditional computing disciplines such as computer engineering and computer science responded to these new trends, albeit often slowly, employers started to demand skills that graduates typically did not get out of a traditional computer science or computer engineering program. For example, many traditional computer science programs did not equip their graduates with the practical network or system administration skills that organizations needed to expand and maintain their IT infrastructures, or the web development skills required to take advantage of the many opportunities opened up by the Internet. Moreover, because of the more theoretical emphasis of computer science programs, graduates often did not acquire a sufficient understanding of organizational processes to be able to support IT applications from a user or organizational perspective (Denning, 2004). While programs in Information Systems are able to offer material to their students that allow them to develop a better understanding of organizations and the way in which IT applications can support them, they often do so at the expense of a sufficiently thorough coverage of the technology. This has been exacerbated by the fact that a good number of programs in Information Systems were offered in business schools and were limited in the number of courses they could offer in IT because of accreditation standards for business schools in place at the time. It was in this environment that a number of universities started to offer undergraduate programs in IT. While there was, and still is, far less homogeneity among undergraduate programs in IT than there is among programs in computer science or information systems, all IT programs show a family resemblance to each other. Most cover areas such as networking, web development and system administration in some detail, while very few pay particular attention to the theoretical foundations in complexity theory that is so prevalent in computer science programs. Until fairly recently, most IT programs arose in isolation from each other, often evolving out of existing programs in computer science, computer engineering or computer engineering technology, or information systems (Lunt et al., 2003a; Reichgelt et al., 2004). However, there was also a growing realization on the part of those programs that were aware of each other's existence that a forum was needed in which they could discuss the various features of their programs, and improve their programs based on the experiences of IT programs at other institutions (Lunt et al., 2003b). Thus, in early 2001, a steering committee composed of five universities offering or getting ready to offer IT programs compiled a list of IT programs across the United States with the intention of organizing an invitation-only conference. These efforts came to fruition in December 2001, when representatives from 15 colleges/universities attended the first Conference for IT Curriculum (CITC-1) in Provo, Utah. The primary aims of this conference were to establish a national organization of IT educators and begin to establish academic standards for this rapidly emerging discipline (Lunt et al. …

This report describes the results of a survey of the Forsythe list of computing Departments 1 , completed in December, 1989. The survey concerns the production and employment of Ph.D.s that graduated in 1988-89 2 and the faculty of Ph.D.-granting computing departments during the academic year 1989-90. All 129 Computer Science (CS) departments (117 U.S. and 12 Canadian) participated. In addition, 29 of 32 departments offering the Ph.D. in Computer Engineering (CE) were included 3 . Throughout this report, CE statistics are reported separately so that comparisons with previous years can be made for CS, but the intention is to merge all statistics for CS and CE in a few more years. Some highlights from the survey are: The growth in Ph.D. production to 625 is less than what was expected (650-700). Still, a growth of almost 50 Ph.D.s is substantial, and it will mean an easier time for departments that are trying to hire and a harder time for the new Ph.D.s. There is still a large market. The new Ph.D.s. however, cannot all expect to be placed in the older, established departments, and more will take positions in the newer departments and in the non-Ph.D.-granting departments. Growth of Ph.D. production seems to have slowed enough so that over production does not seem to be a problem in the near future. There will not be enough retirements, however, to offset new Ph.D. production for ten years. (In the 158 departments, 22 faculty members died or retired last year.) We believe that many of the new Ph.D.s would benefit from a year or two as a postdoc, and perhaps it is time for the NSF to institute such a program in computer science and engineering. The percentage of CS Ph.D.s given to foreign students remained about the same at 40 percent. In CE, the percentage was much higher, at 65 percent. The field continues to be far too young, a problem that only time is solving. CS continues to have more assistant professors than full professors, which puts an added burden on the older people, but there was substantial growth this year in the number of associate professors (as assistant professors were promoted). But the ratio of assistant to full professors in CS has not changed appreciably in four years. As we have mentioned in previous Taulbee Reports, no other field, as far as we know, has this problem. In fact, most scientific fields are 80 to 90 percent tenured in many universities. In CS, this problem is more severe in the newer and lower-ranked departments. In fact, the top 24 departments now have 223 assistant, 176 associate, and 290 full professors. The CE departments have far more full professors than assistant professors, mainly because many are older EE departments offering CE degrees. As we have indicated, Afro-Americans and Hispanics simply are not entering computer science and engineering. It is high time that we did something about it, and we hope the CRB will take the lead in introducing programs to encourage more participation from these minorities. There was a slight growth in the percentage of female Ph.D.s in CS, from 10 to 14 percent. Still, there are far too few women in our field, and our record of retention of women in the faculty is abysmal. There are only 33 female full professors in the 158 CS and CE Ph.D.-granting departments! Again, we hope the CRB will help introduce programs to encourage more women to enter computing and to remain in academia over the years. The signs are that the NSF is interested in this problem as well.

Many Library and Information Science (LIS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both “classic” information science competencies as well as core data science competencies among their students. Since data science competencies are often associated with mathematical and computational thinking, departmental officials and prospective students often raise concerns regarding the appropriate background students should have in order to succeed in this newly introduced computational content of the LIS training programs. In order to address these concerns, we report on an exploratory study through which we examined the 2020 and 2021 student classes of Bar-Ilan University's LIS graduate training, focusing on the computational data science courses (i.e., supervised and unsupervised machine learning). Our study shows that contrary to many of the concerns raised, students from the humanities performed as well (and in some cases significantly better) on data science competencies compared to those from the social sciences and had better success in the training program as a whole. In addition, students’ undergraduate GPA acted as an adequate indicator for both their success in the training program and in the data science part thereof. In addition, we find no evidence to support concerns regarding age or sex. Finally, our study suggests that the computational data science part of students’ training is very much aligned with the rest of their training program.

This white paper argues that formal methods need to be better rooted in higher education curricula for computer science and software engineering programmes of study. To this end, it advocates improved teaching of formal methods; systematic highlighting of formal methods within existing, ‘classical’ computer science courses; and the inclusion of a compulsory formal methods course in computer science and software engineering curricula. These recommendations are based on the observations that formal methods are an essential and cost-effective means to increase software quality; however computer science and software engineering programmes typically fail to provide adequate training in formal methods; and thus there is a lack of computer science graduates who are qualified to apply formal methods in industry. This white paper is the result of a collective effort by authors and participants of the 1st International Workshop on Formal Methods – Fun for Everybody which was held in Bergen, Norway, 2–3 December 2019. As such, it represents insights based on learning and teaching computer science and software engineering (with or without formal methods) at various universities across Europe.

This thesis presents an analysis of a range of social, psychological and motivational factors which accounted for the inequitable representation of females in Computer Science (CS) courses in both the Australian (AU) and Taiwanese (TW) educational contexts. It draws on the literature on educational and vocational achievement related choices and decision-making to identify the key factors and how they influence males and females in making educational choices in their respective educational contexts. A mixed methods approach was chosen, using surveys for gathering the general characteristics of the undergraduates studying CS and non Computer Science (NCS) courses by educational context during the first phase (Phase 1), then interviews to examine in more detail the reasons for individuals’ course participation in the second phase (Phase 2). Results of surveys (Phase 1: AU=106, TW=52) and interviews (Phase 2: AU=7, TW=10) were analysed and presented by gender, group membership (CS and NCS) and educational context. This research offers a cross-national insight into the reasons behind females’ participation and non-participation in CS courses in two different educational contexts, which is not captured in the existing literature. The study found that an interest—enjoyment value attached to IT and/or CS encouraged Australian students to pursue CS courses. In contrast, Taiwanese students’ high self-efficacy beliefs in mathematics and programming skills encouraged their choice of CS courses. However, Taiwanese students’ course selection was dependent on the attainment value they attached to attending particular institutions, whether or not in CS. Stereotypical notions about CS related courses and careers were found to have discouraged both Australian and Taiwanese NCS females from enrolling in CS courses. This thesis concludes that although the trend of inequitable female representation in tertiary CS courses exists internationally, including in Australia and Taiwan, the factors accounting for the gender imbalance in CS vary across countries. Therefore, the strategies to address the issue of gender inequity should also reflect these differences across countries. Recommendations for the Australian context call for schools to re-consider the nature of the tasks provided in IT classrooms. As for the Taiwanese context, building female confidence in general IT learning through pre-tertiary and on-going programs, as well as instituting positive discrimination for females in CS course enrolment in Taiwanese universities, may increase the likelihood of Taiwanese females studying CS courses. Females in both contexts should be provided with broader and more accurate information regarding CS courses and careers rather than being left ill-informed with stereotypical perceptions of CS and thus choosing NCS courses. An understanding of the motivational and discouraging factors in females’ participation in CS courses in both the Australian and Taiwanese contexts provides a starting point for tackling the gender imbalance in the CS field in both contexts.

Background: Software engineering are courses comprising various project types, including simple assignments completed in supervised settings and more complex tasks undertaken independently by students, without the oversight of a constant teacher or lab assistant. The imperative need arises for a comprehensive assessment framework to validate the fulfillment of learning objectives and facilitate the measurement of student outcomes, particularly in computer science and software engineering. This leads to the delineation of an appropriate assessment structure and pattern. Objective: This study aimed to acquire the expertise required for assessing student performance in computer science and software engineering courses. Methods: A comprehensive literature review spanning from 2012 to October 2021 was conducted, resulting in the identification of 20 papers addressing the assessment framework in software engineering and computer science courses. Specific inclusion and exclusion criteria were meticulously applied in two rounds of assessment to identify the most pertinent studies for this investigation. Results: The results showed multiple methods for assessing software engineering and computer science courses, including the Assessment Matrix, Automatic Assessment, CDIO, Cooperative Thinking, formative and summative assessment, Game, Generative Learning Robot, NIMSAD, SECAT, Self-assessment and Peer-assessment, SonarQube Tools, WRENCH, and SEP-CyLE. Conclusion: The evaluation framework for software engineering and computer science courses required further refinement, ultimately leading to the selection of the most suitable technique, known as learning framework. Keywords: Computer science course, Software engineering course, Student assessment, Systematic literature review

This paper describes two models for data science courses offered by computer science programs at two different universities. At both institutions the data science course serves as an elective cours...

The student projects done in Computer Science and Engineering suffers from the following problems: poorly managed requirements, poor or no design and poor or no testing. Students generally tend to focus more on coding phase of the project, since the misconception is that a large code constitutes good software. Unlike other branches of engineering, the engineering processes are not visible here. Further, management of large number of student projects poses a challenge in terms of guidance, progress monitoring and assessment. The student projects are observed to be ending up incomplete and are rarely deployed. The feedback from industry side also speaks about the gaps in terms of these skills. A mini project course for fifth semester is designed in the undergraduate programme in Computer Science and Engineering to address the lacunae observed above by tightly integrating it with the theory course on Software engineering. The focus of the mini project is not on what the problem definition is, instead it is on how it is done. The implementation plan addressed the issues and challenges listed above faced by the faculty. Assessment rubrics are written to guarantee proper understanding of the expectations among the stake holders resulting in fair assessment. This approach improved students understandability in Software engineering concepts and also the quality of their capstone projects. The paper discusses the design, implementation and assessment details of mini project along with the experience gained.

Retaining students in computer science (CS) courses and majors is a concern for many undergraduate CS programs in the United States. A large proportion of students who initially declare a major in CS do not complete a CS degree. The impact of future-oriented motivational constructs such as career aspirations and future connectedness on retention has received relatively little research attention, but these are potential contributors to students' retention in CS courses. The purpose of this study was to investigate how future-oriented motivation related to CS students' retention in CS courses over three consecutive semesters. Students enrolled in CS courses (four 100-level courses, one 200-level course, three 300-level courses, and five 400-level courses) completed survey measures of future-oriented motivation, and course enrollment data were collected for the three semesters. Logistic regression was used to determine whether motivation variables could distinguish between students who were enrolled in at least one CS course during a given semester and students who were not enrolled in any CS courses. Results indicate that, across all three semesters, career aspirations and knowledge of CS career paths were associated with a greater likelihood of continuing to take CS courses, and stronger future connectedness was associated with a lower likelihood of continuing to take CS courses. Implications for CS educators are discussed.