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Reversing is the notion of thinking or working in reverse. Computer science textbooks and tutors recognize it primarily in the form of recursion. However, recursion is only one form of reversing. Reversing appears in the computer science curriculum in many other forms, at various intellectual levels, in a variety of fundamental courses. As such, it is too important a notion to be left unexplored. In this paper we discuss reversing in the context of two theoretical frameworks--as a fundamental idea in computer science, in the context of Schwill's model of fundamental ideas, and as a powerful problem solving means, in the context of Schoenfeld's cognitive model of problem solving. We recognize seven different forms of reversing, and demonstrate the essential role of each in algorithm or proof design. Our focus in the paper is conceptual, yet we briefly note our initial experimental experience, with student difficulties in both the invocation and utilization of reversing. We then discuss cognitive aspects of the teaching of reversing and offer ways to assimilate its awareness and elaborate its fundamental role in computer science. (Contains 1 note.)

This paper discusses the role of reduction in computer science and describes a study on undergraduate students' perception of the concept of reduction. Specifically, based on an analysis of students answers to questions addressing different computer science topics, we present several findings regarding the ways in which undergraduate students conceive of and apply reduction. These findings can be interpreted within the framework of the tension that exists between the following two factors: The need to think in terms of high levels of abstraction, on one hand, and the fact that reduction introduces a new approach to be used in problem-solving situations, on the other. In addition the paper suggests several teaching applications.

Computer science education should not be based on short-term developments but on content that is observable in multiple domains of computer science, may be taught at every intellectual level, will be relevant in the longer term, and is related to everyday language and/or thinking. Recently, a catalogue of "central concepts" for computer science education such as "algorithm", "computer", and "data" has been empirically determined. This paper now concentrates on "central processes" relevant for computer science education. Experts of computer science rated 44 general-education processes regarding four criteria. By using a cluster analysis approach the following central processes for computer science education have been identified: "problem solving and problem posing", "analyzing", "generalizing", "finding relationships", "classifying", and "investigating". (Contains 1 table, 6 figures, and 2 notes.)

Abstraction is a major concept in computer science and serves as a powerful tool in software development. Pattern-oriented instruction (POI) is a pedagogical approach that incorporates patterns in an introductory computer science course in order to structure the learning of algorithmic problem solving. This paper examines abstraction processes in the course of solving an algorithmic problem and highlights three distinct, although interrelated, facets of abstraction: pattern recognition, black-boxing, and structure identification. A study that examined the influence of the POI approach on students' abstraction skills is described; students who learned according to the POI approach were compared with students who learned in a traditional manner with regard to analogical reasoning and problem decomposition and solution, as well as verbal expression. The three facets of abstraction were used to analyze students' abstraction skills and their competency in algorithmic problem solving. (Contains 4 figures and 5 tables.)

Non-determinism is one of the most important, yet abstract, recurring concepts of Computer Science. It plays an important role in Computer Science areas such as formal language theory, computability theory, distributed computing, and operating systems. We conducted a series of studies on the perception of non-determinism. In the current research, we studied and analyzed undergraduate Computer Science students' solutions to assignments in a course on automata and formal languages. Our findings shed some light on students' perceptions of non-determinism, their tendency to use non-determinism, and the characteristics of their non-deterministic solutions. This paper describes the current research and its results, and suggests several teaching applications. (Contains 14 figures and 1 table.)

This paper describes an empirical study that investigated the knowledge that Computer Science students have about the extent of their own previous learning. The study compared self-generated estimates of performance with actual performance on a data structures quiz taken by undergraduate students in courses requiring data structures as a prerequisite. The study was contextualized and grounded within a research paradigm in Psychology called "calibration of knowledge" that suggests that self-knowledge across a range of disciplines is highly unreliable. Such self-knowledge is important because of its role in "meta-cognition", particularly in cognitive self-regulation and monitoring, as well as in the credence that instructors give to student self-reports. Our results indicated that Computer Science student self-estimates are highly correlated with performance, more so for estimates provided after the performance than before. This high level of calibration, however, was likely the result of a number of conditions that do not always hold: that the students already had domain expertise, that the quiz had unambiguous and verifiable answers, and that students expected their estimates to be validated. When these conditions are not met, it becomes more important for students to have direct feedback about their performance so as to uncover those areas where their intuitions might mislead them. Students also had weak knowledge about their standing relative to their peers, particularly those in the lower performance quartiles, exhibiting the well known "better-than-average" heuristic. There was, additionally, no correlation between calibration ability and degree of liking or difficulty with the data structures material, suggesting that instructors and researchers should not treat liking or difficulty as reliable indicators of the learning that has occurred. (Contains 1 note, 4 tables and 4 figures.)

This paper proposes an evaluation framework for assessing students' comprehension of concurrent programs. The need for such a framework is illustrated by a review of various Computer Science Education forums. This review suggests that there is little pedagogical research in the area of concurrent software, particularly with respect to assessing students' knowledge. In particular, the proposed framework attempts to categorize the types of information students seek and the types of information they obtain, as they learn about concurrent software. Thus the framework could be used to guide teaching methods in this area, to hone the representations used to portray concurrent software to students and subsequently, to assess students' performance. (Contains 4 tables.)

Background and Context: Though computer science education is marked by a pronounced racial participation gap, there is little research about effective ways that teachers can be supported in creating racially-just and equitable computer science learning opportunities for students. Objectives: This paper examines how teachers engage in learning about race and equity in computer science education during professional development. Method: Drawing from data collected from 94 participants attending a summer week-long Exploring Computer Science workshop, this mixed-methods study revealed how key curricular and instructional features of professional development foster race-conscious discussions and shifts in beliefs and agency around broadening participation in computing. Findings: Given the importance of developing teacher capacity to increase opportunities for students of color in computing classrooms, the findings of this study highlight how intentionally integrating race education in the center of professional development for teachers can support teachers' equity-based beliefs and practices. Implications: Professional development for teachers that seeks to broaden participation in computing is supported by long-term professional learning that explicitly center issues of race and equity in the curriculum. [ABSTRACT FROM AUTHOR]

Reduction is one of the key techniques used for problem-solving in computer science. In particular, in the theory of computation and complexity (TCC), mapping and polynomial reductions are used for analysis of decidability and computational complexity of problems, including the core concept of NP-completeness. Reduction is a highly abstract technique that involves revealing close non-trivial connections between problems that often seem to have nothing in common. As a result, proper understanding and application of reduction is a serious challenge for students and a source of numerous misconceptions. The main contribution of this paper is detection of such misconceptions, analysis of their roots, and proposing a way to address them in an undergraduate TCC course. Our observations suggest that the main source of the misconceptions is the false intuitive rule “the bigger is a set/problem, the harder it is to solve”. Accordingly, we developed a series of exercises for proactive prevention of these misconceptions. [ABSTRACT FROM PUBLISHER]

To support teachers to implement Computer Science curricula into classrooms from the very first year of school, teachers, schools and organisations seek quality curriculum resources to support implementation and teacher professional development. Until now, many Computer Science resources and outreach initiatives have targeted K-12 school-age children, with the intention to engage children and increase interest, rather than to formally teach concepts and skills. What is the educational quality of existing Computer Science resources and to what extent are they suitable for classroom learning and teaching? In this paper, an assessment framework is presented to evaluate the quality of online Computer Science resources. Further, a semi-systematic review of available online Computer Science resources was conducted to evaluate resources available for classroom learning and teaching and to identify gaps in resource availability, using the Australian curriculum as a case study analysis. The findings reveal a predominance of quality resources, however, a number of critical gaps were identified. This paper provides recommendations and guidance for the development of new and supplementary resources and future research. [ABSTRACT FROM PUBLISHER]

Computational thinking (CT) is considered as a valuable literacy for all students, and its inclusion in compulsory schooling could increase the numbers of underrepresented students who pursue computing-related careers. Computer Science Unplugged (CSU) had success in making computer science (CS) accessible to K–12 students in outreach settings. Such "unplugged" approaches have the potential to do the same in formal education. This review considers how research findings on unplugged pedagogies might advance CS/CT education priorities, while highlighting areas of unknown and tension. We conducted a search in academic databases using terms +unplugged "computer science" and +unplugged CT, and related terms. We synthesized our review with existing ones to inform the priorities of CS-for-all and CT development. We surfaced CSU's limitations to broaden access for underrepresented students and suggested a remedy. We proposed 10 research questions that fill key gaps to support efforts that provide just access to quality CS/CT education. [ABSTRACT FROM AUTHOR]

Computer science education should not be based on short-term developments but on content that is observable in multiple domains of computer science, may be taught at every intellectual level, will be relevant in the longer term, and is related to everyday language and/or thinking. Recently, a catalogue of central concepts for computer science education such as algorithm, computer, and data has been empirically determined. This paper now concentrates on central processes relevant for computer science education. Experts of computer science rated 44 general-education processes regarding four criteria. By using a cluster analysis approach the following central processes for computer science education have been identified: problem solving and problem posing, analyzing, generalizing, finding relationships, classifying, and investigating. [ABSTRACT FROM AUTHOR]

Researchers have been looking into the complexity of computer science (CS) education and tried to apply rigorous and relevant educational research methods to understand and facilitate the learning experience of students. The purpose of this study was to explore college students' conceptions of learning CS to shed light on student learning activities, feelings, contexts, and beliefs about their learning. Draw-a-picture technique was used as an emerging research technique in CS education. Using a modified coding checklist, we analyzed the drawings into 9 categories with 50 sub-categories. College students most frequently expressed computer programming as the learning activity by illustrating syntax and semantics of programming languages in the drawings. Problem-solving constructs like decomposition and abstraction were also recorded along with other tools like diagramming techniques. Gender and prior computer science experience were analyzed as moderators. The results provide a baseline for future research on the topic and preliminary evidence in using the method to discern student learning conceptions in CS. [ABSTRACT FROM AUTHOR]

Background and Context: EarSketch was developed as a program to foster persistence in computer science with diverse student populations. Objective: To test the effectiveness of EarSketch in promoting intentions to persist, particularly among female students and under-represented minority students. Method: Meta-analyses, structural equation modeling, multi-level modeling, and qualitative analyses were performed to examine how participation in EarSketch and other factors affect students' intentions to persist in computing. Findings: Students significantly increased their intentions to persist in computing, g =.40[.25,54], but examination within just the five quasi-experimental studies did not result in a significant difference for students in EarSketch compared to students not in EarSketch, g =.08[−.07,.23]. Student attitudes towards computing and the perceived authenticity of the EarSketch environment significantly predicted intentions to persist in computing. Implications: Participation in computer science education can increase students' intentions to persist in programming, and EarSketch is one such program that can aid in these intentions. [ABSTRACT FROM AUTHOR]

While there has been a remarkable interest to make computer science a core K-12 academic subject in the United States, there is a shortage of K-12 computer science teachers to successfully implement computer sciences courses in schools. In order to enhance computer science teacher capacity, training programs have been offered through teacher professional development. In this study, the main goal was to systematically review the studies regarding computer science professional development to understand the scope, context, and effectiveness of these programs in the past decade (2004–2014). Based on 21 journal articles and conference proceedings, this study explored: (1) Type of professional development organization and source of funding, (2) professional development structure and participants, (3) goal of professional development and type of evaluation used, (4) specific computer science concepts and training tools used, (5) and their effectiveness to improve teacher practice and student learning. [ABSTRACT FROM PUBLISHER]

Previous studies have shown that students with low knowledge understand and learn better from more cohesive texts, whereas high-knowledge students have been shown to learn better from texts of lower cohesion. This study examines whether high-knowledge readers in computer science benefit from a text of low cohesion. Undergraduate students (n = 65) read one of four versions of a text concerning Local Network Topologies, orthogonally varying local and global cohesion. Participants' comprehension was examined through free-recall measure, text-based, bridging-inference, elaborative-inference, problem-solving questions and a sorting task. The results indicated that high-knowledge readers benefited from the low-cohesion text. The interaction of text cohesion and knowledge was reliable for the sorting activity, for elaborative-inference and for problem-solving questions. Although high-knowledge readers performed better in text-based and in bridging-inference questions with the low-cohesion text, the interaction of text cohesion and knowledge was not reliable. The results suggest a more complex view of when and for whom textual cohesion affects comprehension and consequently learning in computer science. [ABSTRACT FROM AUTHOR]

Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI. [ABSTRACT FROM AUTHOR]

A national web-based survey was administered to 700 undergraduate computer science (CS) programs in the United States as part of a stratified random sample of 797 undergraduate CS programs. The 251 program responses (36% response rate) regarding social and professional issues are presented. This article describes the demographics of the respondents, presents results concerning whether programs teach social and professional issues, how social and professional issues are integrated, perceptions of CS faculty regarding the importance of social and professional issues, pedagogies used to teach social and professional issues, and what specific social and professional topics have been included in the CS curriculum. Additionally, we (a) provide suggestions for CS programs regarding the integration of social and professional issues into the CS curriculum, (b) suggest ways to encourage more social and professional coverage in CS programs, pedagogy, and (c) recommend what social and professional topics should be included in future CS curriculum reports. [ABSTRACT FROM AUTHOR]

Educators in the computing fields are often familiar with the characterization of computing as a combination of theoretical, scientific, and engineering traditions. That distinction is often used to guide the work and disciplinary self-identity of computing professionals. But the distinction is, by no means, an easy one. The three traditions of computing are based on different principles, they have different aims, they employ different methods, and their products are very different. Educators in the field of computing should be aware of the fundamental differences between the traditions of computing so that they can offer their students a truthful and balanced view about computing branches. In this article the three traditions of computing are presented and some of their underlying assumptions, principles, application areas, restrictions, and weaknesses are portrayed. Also, some of the landmark arguments in the debates about the identity of computing disciplines are discussed. [ABSTRACT FROM AUTHOR]