  Educating students about academic integrity expectations has been suggested
as one of the ways to reduce malpractice in take-home programming assignments.
We test this hypothesis using data collected from an artificial intelligence
course with 105 participants (N=105) at a university in India. The AI course
had two programming assignments. Plagiarism through collusion was quantified
using the Measure of Software Similarity (MOSS) tool. Students were educated
about what constitutes academic dishonesty and were required to take an honor
pledge before the start of the second take-home programming assignment. The two
programming assignments were novel and did not have solutions available on the
internet. We expected the mean percentage of similar lines of code to be
significantly less in the second programming assignment. However, our results
show no significant difference in the mean percentage of similar lines of code
across the two programming assignments. We also study how the Big-five
personality traits affect the propensity for plagiarism in the two take-home
assignments. Our results across both assignments show that the extraversion
trait of the Big Five personality exhibits a positive association, and the
conscientiousness trait exhibits a negative association with plagiarism
tendencies. Our result suggests that the policy of educating students about
academic integrity will have a limited impact as long as students perceive an
opportunity for plagiarism to be present. We explain our results using the
Fraud triangle model.
