Maguire, Phil and Maguire, Rebecca and Kelly, Robert (2018) Using automatic machine assessment to teach computer programming. Computer Science Education, 27 (3-4). pp. 197-214. ISSN 0899-3408

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Abstract

We report on an intervention in which informal programming labs were switched to a weekly machine-evaluated test for a second year Data Structures and Algorithms module. Using the online HackerRank system, we investigated whether greater constructive alignment between course content and the exam would result in lower failure rates. After controlling for known associates, a hierarchical regression model revealed that HackerRank performance was the best predictor of exam performance, accounting for 18% of the variance in scores. Extent of practice and confidence in programming ability emerged as additional significant predictors. Although students expressed negativity towards the automated system, the overall failure rate was halved, and the number of students gaining first class honours tripled. We infer that automatic machine assessment better prepares students for situations where they have to write code by themselves by eliminating reliance on external sources of help and motivating the development of self-sufficiency.

Item Type:	Article
Keywords:	Programming instruction; programming confidence; automatic correction; skill development; constructive alignment; automatic feedback;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Psychology
Item ID:	12365
Identification Number:	https://doi.org/10.1080/08993408.2018.1435113
Depositing User:	Phil Maguire
Date Deposited:	04 Feb 2020 16:57
Journal or Publication Title:	Computer Science Education
Publisher:	Taylor & Francis
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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