Pelchen, T, Mathieson, L & Lister, R 2020, 'On the evidence for a learning hierarchy in data structures exams', ACE 2020 - Proceedings of the 22nd Australasian Computing Education Conference, Held in conjunction with Australasian Computer Science Week, pp. 122-131.View/Download from: Publisher's site
© 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7686-0/20/02...$15.00 Several previous research studies have found a relationship between the ability of novices to trace and explain code, and the ability to write code. Harrington and Cheng refer to that relationship as the Learning Hierarchy. However, almost all of those studies examined students at the end of their first semester of learning to program (i.e. CS1). This paper is only the third paper to describe a study of explain in plain English questions on students at the end of an introductory data structures course. The preceding two papers reached contradictory conclusions. Corney et al. presented results consistent with the Learning Hierarchy identified in the CS1 studies. However, Harrington and Cheng presented results for data structures students suggesting that the hierarchy reversed by the time students had progressed to the level of learning about data structures; that is, tracing and explaining were skills that followed writing. In our study of data structures students, we present results that are consistent with the Learning Hierarchy derived from the CS1 students. We believe that the reversal identified by Harrington and Cheng can occur, but only as a consequence of a mismatch in the relative difficulty of tracing, explaining and writing questions.
Pelchen, T & Lister, R 1970, 'On the Frequency of Words Used in Answers to Explain in Plain English Questions by Novice Programmers', Proceedings of the Twenty-First Australasian Computing Education Conference, Australasian Computing Education Conference, ACM, Sydney NSW Australia, pp. 11-20.View/Download from: Publisher's site
Most previous research studies using Explain in Plain English questions have focussed on categorising the answers of novice programmers according to the SOLO taxonomy, and/or the relationship between explaining code and writing code. In this paper, we study the words used in the explanations of novice programmers. Our data is from twelve Explain in plain English questions presented to over three hundred students in an exam at the end of the students' first semester of programming. For each question, we compare the frequency of certain words used in correct answers, between students who scored a perfect twelve on all the Explain in plain English questions and students with lower scores. We report a number of statistically significant differences in word frequency between the students who answered all questions correctly and students who did not. The students who answered all twelve questions correctly tended to be more precise, more comprehensive, and more likely to choose words not explicitly in the code, but instead words that are an abstraction beyond the code.