Paper Info
Title
Superficial Code-guise: Investigating the Impact of Surface Feature Changes on Students' Programming Question Scores
Abstract
ABSTRACTAssessing student performance on programming questions is important for introductory computer science courses, both for student learning and for ensuring students demonstrate competence. Part of being a competent programmer includes the ability to transfer learning from solved to analogous problems. Additionally, particularly in computer-based and online assessment, mitigating cheating efforts is another important consideration. One way to mitigate cheating is by randomly selecting from large pools of equivalent questions. In order to produce large pools of questions quickly, we used a permutation strategy to rapidly make new question variants by altering existing questions' surface features. In this work, we present the results of our first set of surface feature permuted questions in an introductory Python course. We find surface feature permutations to be an effective way to produce questions of a similar difficulty to other new questions for students while mitigating potential cheating. However, we also see permutations expose potential student knowledge fragility and transfer concerns, as performance on permutations of homework questions is not strictly better than performance on questions that are entirely new on assessments
Year
DOI
Venue
2021
10.1145/3408877.3432413
Computer Science Education
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Max Fowler100.68
Craig B. Zilles293294.74