Undergraduate Computer Science Curriculum Research Articles

“Hear” and “Play” Students Misconceptions on Concurrent Programming using Sonic Pi

Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a domain-specific music language, provides great support for “playing” with concurrency and “hearing” common problems such as data races and lack of synchronization among different concurrent threads. More specifically, the paper focuses on students’ misconceptions regarding concurrency in Sonic Pi, and compares them to those arising in traditional concurrent programming languages. In addition, it preliminarily explores knowledge transfer from Sonic Pi to C/C++. The approach has been applied in two teaching experiments with undergraduate students in our University involving 184 participants. Our investigations bring out the need to address misconceptions through targeted interventions for a clear understanding of concurrent programming concepts. Sonic Pi’s simplified abstraction and domain-specific flavor has demonstrated to be effective, especially for first-year students.

Artificial Intelligence in the CS2023 Undergraduate Computer Science Curriculum: Rationale and Challenges

Roughly every decade, the ACM and IEEE professional organizations have produced recommendations for the education of undergraduate computer science students. These guidelines are used worldwide by research universities, liberal arts colleges, and community colleges. For the latest 2023 revision of the curriculum, AAAI has collaborated with ACM and IEEE to integrate artificial intelligence more broadly into this new curriculum and to address the issues it raises for students, instructors, practitioners, policy makers, and the general public. This paper describes the development process and rationale that underlie the artificial intelligence components of the CS2023 curriculum, discusses the challenges in curriculum design for such a rapidly advancing field, and examines lessons learned during this three-year process.

Undergraduate Computer Science Curricula

First-job readiness versus long-term career preparation.

Figuring and Drawing

A standing challenge in undergraduate Computer Science curricula is the teaching and learning of computer programming. Through this paper which is an essay about programming, we aim to contribute to the plethora of existing pedagogies, approaches and philosophies, by discussing a specific feature of our approach in teaching principled programming to undergraduate students, in their first semester of studies, namely the utilization of pictures, both text-based and raster-based graphics. Although the given course has evolved substantially over the thirty years of its delivery regarding the programming languages (Miranda, C, C++, Java) and paradigms (functional, imperative, object-oriented, combination of procedural and object-oriented) used, the discussed visual feature has been maintained and steadily strengthened. We list abstraction, problem decomposition and synthesis, information hiding, reusability, modularity and extensibility as key principles of problem solving and algorithmic thinking. These principles are closely aligned with the advocated computational thinking techniques of problem decomposition, pattern recognition, pattern generalization and algorithm design. We aim for our students to familiarize themselves with all the above principles through practical problem solving. Our ongoing inquiry has been whether the problem domain of pictures is contributing valuably towards this aim. Moreover, an added-value is that students get a glimpse of computational complexity in a visual, empirical way. The presented work is not related to visual programming, since the students write their programs textually and not graphically; it's the output of their programs which is in visual form. Our approach though is loosely related to the classical paradigm of turtle graphics. However, our focus is Computer Science majors, who should be able to design and build turtles and other objects and not just use them. Indeed, the programming principles course helps them to do both and also to appreciate the multitude of algorithmic ways for producing the same visual output. Currently the given programming principles are approached both from a procedural, process-based and an object-oriented, concept-based perspective and the course uses the Java language. Through the presented example problems, we aim to show the appropriateness of the visual domain of pictures for supporting the learning of principled programming. The problem domain of pictures is abundantly rich with potential examples to draw from. Moreover, as reported in the literature, female students may show higher interest towards visual problem domains in programming classes, in relation to other problem domains. We plan to investigate this conjecture in the context of our broader aim to encourage more females to follow university studies in computer science; in this paper only a cursory finding is presented, that bears some relation to what is reported in the literature.

Science Fiction as an Introduction to AI Research

The undergraduate computer science curriculum is generally focused on skills and tools; most students are not exposed to much research in the field, and do not learn how to navigate the research literature. We describe how science fiction reviews were used as a gateway to research reviews. Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related their chosen science fiction book, movie, or TV show.

A module-based introduction to heterogeneous computing in core courses

Heterogeneous architectures have emerged as a dominant platform, not only in high-performance computing but also in mobile processing, cloud computing, and the Internet of Things (IoTs). Because the undergraduate computer science curriculum includes so many topics, adding a new course as a required part of the curriculum without increasing the number of hours to graduation is difficult. Integration of heterogeneous computing requires a module-driven approach in which coverage of the topics is broken down into smaller units and dispersed throughout the curriculum. The module-driven approach has been successfully implemented in introducing parallel and distributed computing concepts.In this paper, we present a set of four teaching modules that introduce fundamental concepts in heterogeneous computing in lower-division computer science courses. The goal of these modules is not to teach students how to program heterogeneous systems but rather to expose them to this emerging trend and prepare them for material in future classes. Although concepts are covered at a high level, the modules emphasize active learning and include lab assignments that provide students with hands-on experience. We also present initial evaluation results for two of these modules based on their use in undergraduate courses at Texas State University. The results are quite encouraging both in terms of learning outcomes and student engagement and interest.

A two-phase machine learning approach for predicting student outcomes

Learning analytics have proved promising capabilities and opportunities to many aspects of academic research and higher education studies. Data-driven insights can significantly contribute to provide solutions for curbing costs and improving education quality. This paper adopts a two-phase machine learning approach, which utilizes both unsupervised and supervised learning techniques for predicting outcomes of students following Higher Education programs of studies. The approach has been applied in a case-study which has been performed in the context of an undergraduate Computer Science curriculum offered by the University of Thessaly in Greece. Students involved in the case study were initially grouped based on the similarity of specific education-related factors and metrics. Using the K-Means algorithm, our clustering experiments revealed the presence of three coherent clusters of students. Subsequently, the discovered clusters were utilized to train prediction models for addressing each particular cluster of students individually. In this regard, two machine learning models were trained for every cluster of students in order to predict the time to degree completion and student enrollment in the offered educational programs. The developed models are claimed to produce predictions with relatively high accuracy. Finally, the paper discusses the potential usefulness of the clustering-aided approach for learning analytics in Higher Education.

Computing's social obligation

The cover of the August issue of Communications of the ACM showcases Harvard's impressive experiment integrating ethics throughout its undergraduate computer science curriculum [6]. Anyone currently grappling with exploding enrollments must applaud Harvard's deliberate use of resources to broaden CS education beyond its traditional technical components. In a similar vein, ACM recently updated its professional code of ethics, calling on CS professionals to acknowledge "all people are stakeholders" and to "ensure that the public good is the central concern during all professional computing work," [1] possibly adding to the pressure many in CS education may be feeling to 'up their game' when it comes to their programs' attention to ethics. Unsurprisingly, doing ethics in CS can seem like yet another requirement tasked to an already overburdened system. If one is operating under constraints, perhaps limited by shortages in money and personnel, ethics, or put another way, instilling concern for computing's impact on society, starts to seem like a luxury: something that we all agree is valuable and that would be nice to have if we can afford it. But, what happens if learning about ethics is just as fundamental to software engineering as procedural abstraction or just as crucial to data science as learning to avoid spurious correlation?

PAVT: a tool to visualize and teach parsing algorithms

A parsing algorithm visualizer is a tool that visualizes the construction of a parser for a given context-free grammar and then illustrates the use of that parser to parse a given string. Parsing algorithm visualizers are used to teach the course on compiler construction which in invariably included in all undergraduate computer science curricula. This paper presents a new parsing algorithm visualizer that can visualize six parsing algorithms, viz. predictive parsing, simple LR parsing, canonical LR parsing, look-ahead LR parsing, Earley parsing and CYK parsing. The tool logically explains the process of parsing showing the calculations involved in each step. The output of the tool has been structured to maximize the learning outcomes and contains important constructs like FIRST and FOLLOW sets, item sets, parsing table, parse tree and leftmost or rightmost derivation depending on the algorithm being visualized. The tool has been used to teach the course on compiler construction at both undergraduate and graduate levels. An overall positive feedback was received from the students with 89% of them saying that the tool helped them in understanding the parsing algorithms. The tool is capable of visualizing multiple parsing algorithms and 88% students used it to compare the algorithms.

Denial of service lab for experiential cybersecurity learning in primarily undergraduate institutions

Cybersecurity is a broad, dynamic, and ever changing field that is difficult to integrate into undergraduate Computer Science curriculum. The absence of sanitized labs coupled with the requirement ...

Proust: A Nano Proof Assistant

Proust is a small Racket program offering rudimentary interactive assistance in the development of verified proofs for propositional and predicate logic. It is constructed in stages, some of which are done by students before using it to complete proof exercises, and in parallel with the study of its theoretical underpinnings, including elements of Martin-Lof type theory. The goal is twofold: to demystify some of the machinery behind full-featured proof assistants such as Coq and Agda, and to better integrate the study of formal logic with other core elements of an undergraduate computer science curriculum.

Scrum in the undergraduate computer science curriculum

While Scrum is an increasingly popular software development practice, there still exist questions of how one can incorporate the Scrum process into the undergraduate curriculum. This paper provides...

New features of C++11

C++ is a programming language commonly taught in the undergraduate Computer Science curriculum. Recently the long awaited new standard for C++ known as C++11 was adopted replacing the C++98/C++03 standard. C++11 source code is backward compatible with the first standard source code. Many of the changes were strongly influenced by the TR1 and the Boost library. The new language features include scoped enumerations, the auto and decltype keywords, lambdas, variadic templates, raw strings, a range based for loop, and initializer lists. Many changes involve the Standard C++ Library. Of particular note are templates for tuples, smart pointers (shared_ptr, unique_ptr and weak_ptr), arrays, forward lists, unordered sets and maps, regular expressions, and sophisticated random number generation support.

A Case Study of Teaching Parallel and Distributed Computing Topics on a Computer Cluster

This paper presents the establishment of cluster computing lab at a minority serving institution that aims to provide computing resources to support undergraduate computer science curriculum. The computing resources of the cluster are managed by a job distribution environment that allows the users to upload, compile, and run their jobs. The job distribution software distributes the submitted jobs to the computing nodes of the cluster. The authors will present a case study of using this platform to teach parallel and distributed computing topics in the operating system course. The evaluation of the teaching effectiveness is presented thereafter.

Fiction as an Introduction to Computer Science Research

The undergraduate computer science curriculum is generally focused on skills and tools; most students are not exposed to much research in the field, and do not learn how to navigate the research literature. We describe how fiction reviews (and specifically science fiction) are used as a gateway to research reviews. Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related to their chosen science fiction book, movie, or TV show.

Experimenting an implementation-focused approach to undergraduate compiler course

Compiler construction is one of the most challenging courses to teach in undergraduate computer science curriculum. In a traditional compiler course, many theoretical topics are covered and a compiler project is assigned. Our previous experience shows that this approach not only is challenging but also can be ineffective sometimes. To cover the extensive amount of material within a semester is a struggle. To expect students to produce a working compiler is also impractical since most undergraduates have little experience in developing large software. Our preliminary experiment using an implementation-focused approach produces many positive outcomes. In this approach, the lectures focused on the case study of the design and implementation of a Pascal compiler using a language independent framework. For the project, students successfully used the given language independent framework to design and develop a working compiler for a modified subset C language.

Design and Implementation of an Interpreter Using Software Engineering Concepts

In this paper, an interpreter design and implementation for a small subset of C Language using software engineering concepts are presented. This paper reinforces an argument for the application of software engineering concepts in the area of interpreter design but it also focuses on the relevance of the paper to undergraduate computer science curricula. The design and development of the interpreter is also important to software engineering. Some of its components form the basis for different engineering tools. This paper also demonstrates that some of the standard software engineering concepts such as object-oriented design, design patterns, UML diagrams, etc., can provide a useful track of the evolution of an interpreter, as well as enhancing confidence in its correctness

Enriching undergraduate computer science curriculum with steganography examples

Steganography is the art and science of concealing communication. The goal of steganography is to hide the very existence of information exchange by embedding messages into unsuspicious digital med...

C++11! New features

C++ is a programming language commonly taught in the undergraduate Computer Science curriculum. Recently the long awaited new standard for C++ known as C++11 was adopted replacing the C++98/C++03 standard. C++11 source code is backward compatible with the first standard source code. Many of the changes were strongly influenced by the TR1 and the Boost library.

Integrating Mobile Robotics and Vision With Undergraduate Computer Science

This paper describes the integration of robotics education into an undergraduate Computer Science curriculum. The proposed approach delivers mobile robotics as well as covering the closely related field of Computer Vision and is directly linked to the research conducted at the authors' institution. The paper describes the most relevant details of the module content and assessment strategy, paying particular attention to the practical sessions using Rovio mobile robots. The specific choices are discussed that were made with regard to the mobile platform, software libraries, and lab environment. The paper also presents a detailed qualitative and quantitative analysis of student results, including the correlation between student engagement and performance, and discusses the outcomes of this experience.