Search

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.)

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.)

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.)

Presents a summary of nine research reports from a National Educational Computing Conference research workshop whose purpose was to bring together researchers who have been conducting gender-related research in computer science education. Three subgroups discussed the issues of exposure, attraction, and retention of women to computer science. (MDH)