Influence of Personality Traits on Plagiarism Through Collusion in Programming Assignments
- URL: http://arxiv.org/abs/2407.15014v1
- Date: Sat, 29 Jun 2024 10:26:48 GMT
- Title: Influence of Personality Traits on Plagiarism Through Collusion in Programming Assignments
- Authors: Parthasarathy PD, Ishaan Kapoor, Swaroop Joshi, Sujith Thomas,
- Abstract summary: We study how the Big-five personality traits affect the propensity for plagiarism in two take-home programming assignments.
Our results 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Assessing the Prevalence of AI-assisted Cheating in Programming Courses: A Pilot Study [0.0]
Tools that can generate computer code in response to inputs written in natural language pose an existential threat to Computer Science education.<n>We conducted a pilot study in a large Computer Science class to assess the feasibility of estimating AI plagiarism through anonymous surveys and interviews.
arXiv Detail & Related papers (2025-07-08T22:40:44Z) - The Failure of Plagiarism Detection in Competitive Programming [0.0]
Plagiarism in programming courses remains a persistent challenge.<n>This paper examines why traditional code plagiarism detection methods frequently fail in competitive programming contexts.<n>We find that widely-used automated similarity checkers can be thwarted by simple code transformations or novel AI-generated code.
arXiv Detail & Related papers (2025-05-13T05:43:49Z) - Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments [15.295438618760164]
We describe code interviews: a more authentic assessment method for take-home programming assignments.
Code interviews pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights.
We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.
arXiv Detail & Related papers (2024-10-01T19:01:41Z) - Implicit Personalization in Language Models: A Systematic Study [94.29756463158853]
Implicit Personalization (IP) is a phenomenon of language models inferring a user's background from the implicit cues in the input prompts.
This work systematically studies IP through a rigorous mathematical formulation, a multi-perspective moral reasoning framework, and a set of case studies.
arXiv Detail & Related papers (2024-05-23T17:18:46Z) - Plagiarism and AI Assistance Misuse in Web Programming: Unfair Benefits
and Characteristics [0.0]
Plagiarized submissions are similar to the independent ones except in trivial aspects such as color and identifier names.
Students believe AI assistance could be useful given proper acknowledgment of the use, although they are not convinced with readability and correctness of the solutions.
arXiv Detail & Related papers (2023-10-31T00:51:14Z) - Does Starting Deep Learning Homework Earlier Improve Grades? [63.20583929886827]
Students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment.
Existing literature on the impact of time spent on homework is not clear-cut and comes mostly from K-12 education.
We develop a hierarchical Bayesian model to help make principled conclusions about the impact on student success.
arXiv Detail & Related papers (2023-09-30T09:34:30Z) - Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science.
Standard approaches require instructors to manually grade student-implemented interactive programs.
Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
arXiv Detail & Related papers (2022-11-16T10:00:23Z) - Label Matching Semi-Supervised Object Detection [85.99282969977541]
Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training.
Label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training.
We propose a simple yet effective LabelMatch framework from two different yet complementary perspectives.
arXiv Detail & Related papers (2022-06-14T05:59:41Z) - Plagiarism deterrence for introductory programming [11.612194979331179]
A class-wide statistical characterization can be clearly shared with students via an intuitive new p-value.
A pairwise, compression-based similarity detection algorithm captures relationships between assignments more accurately.
An unbiased scoring system aids students and the instructor in understanding true independence of effort.
arXiv Detail & Related papers (2022-06-06T18:47:25Z) - Measuring Plagiarism in Introductory Programming Course Assignments [0.0]
This paper discusses the methods of plagiarism and its detection in introductory programming course assignments written in C++.
A general framework is developed that uses the three token-based similarity methods as features and predicts if the solution is plagiarized.
We achieved an F1 score of 0.955 and 0.971 on original and synthetic datasets.
arXiv Detail & Related papers (2022-04-29T17:06:26Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - Enforcing Consistency in Weakly Supervised Semantic Parsing [68.2211621631765]
We explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs.
We find that a more consistent formalism leads to improved model performance even without consistency-based training.
arXiv Detail & Related papers (2021-07-13T03:48:04Z) - Effects of Human vs. Automatic Feedback on Students' Understanding of AI
Concepts and Programming Style [0.0]
The use of automatic grading tools has become nearly ubiquitous in large undergraduate programming courses.
There is a relative lack of data directly comparing student outcomes when receiving computer-generated feedback and human-written feedback.
This paper addresses this gap by splitting one 90-student class into two feedback groups and analyzing differences in the two cohorts' performance.
arXiv Detail & Related papers (2020-11-20T21:40:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.