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ACM Transactions on Computing Education

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Updated: 16 min 36 sec ago

Learning to Get Literal: Investigating Reference-Point Difficulties in Novice Programming

Tue, 05/28/2019 - 20:00
Craig S. Miller, Amber Settle

We investigate conditions in which novices make some reference errors when programming. We asked students from introductory programming courses to perform a simple code-writing task that required constructing references to objects and their attributes. By experimentally manipulating the nature of the attributes in the tasks, from identifying attributes (e.g., title or label) to descriptive attributes (e.g., calories or texture), the study revealed the relative frequencies with which students mistakenly omit the name of an identifying attribute while attempting to reference its value. We explain how these reference-point shifts are consistent with the use of metonymy, a form of figurative expression in human communication.

Incorporating Computing Professionals’ Know-how: Differences between Assessment by Students, Academics, and Professional Experts

Mon, 05/20/2019 - 20:00
Ana Sánchez, César Domínguez, Jose Miguel Blanco, Arturo Jaime

It is important for both computer science academics and students to clearly comprehend the differences between academic and professional perspectives in terms of assessing a deliverable. It is especially interesting to determine whether the aspects deemed important to evaluate by a computer science expert are the same as those established by academics and students. Such potential discrepancies are indicative of the unexpected challenges students may encounter once they graduate and begin working. In this article, we propose a learning activity in which computer science students made a video about their future profession after hearing an expert in the field who discussed about the characteristics and difficulties of his or her work.

Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review

Mon, 05/20/2019 - 20:00
Matija Novak, Mike Joy, Dragutin Kermek

Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness.

Computer Science Pedagogical Content Knowledge: Characterizing Teacher Performance

Mon, 05/20/2019 - 20:00
Aman Yadav, Marc Berges

Computer science education efforts are expanding across the globe to equip students with the necessary computing skills for today’s digital world. However, preparing students to become literate in computing activities requires the training of tens of thousands of teachers in computer science. The discrepancy between student needs and teacher preparation in computer science has raised questions of quality teachers, particularly for teachers who do not possess adequate content or pedagogical knowledge to teach computer science efficiently. To address this issue, we designed an instrument to measure knowledge needed to teach computer science (i.e., computer science pedagogical content knowledge). Results exhibited that our instrument measured aspects of teachers’ computer science pedagogical content knowledge; however, teachers’ prior background in teaching did not influence their performance.