Collaboration Versus Cheating

We outline how we detected programming plagiarism in an introductory online course for a master's of science in computer science program, how we achieved a statistically significant reduction in programming plagiarism by combining a clear explanation of university and class policy on academic honesty reinforced with a short but formal assessment, and how we evaluated plagiarism rates before SIGand after implementing our policy and assessment.Comment: 7 pages, 1 figure, 5 tables, SIGCSE 201

Are competition and extrinsic motivation reliable predictors of academic cheating?

Previous studies suggest that extrinsic motivation and competition are reliable predictors of academic cheating. The aim of the present questionnaire study was to separate the effects of motivation- and competition-related variables on academic cheating by Hungarian high school students (N = 620, M = 264, F = 356). Structural equation modeling showed that intrinsic motivation has a negative effect, and amotivation has a positive indirect effect on self-reported academic cheating. In contrast, extrinsic motivation had no significant effect. Indirect positive influence on cheating, based on some characteristics of hypercompetition, was also found, whereas attitudes toward self-developmental competition had a mediated negative influence. Neither constructive nor destructive competitive classroom climate had a significant impact on academic dishonesty. Acceptance of cheating and guilt has significant and direct effect on self-reported cheating. In comparison with them, the effects of motivational and competition-related variables are relatively small, even negligible. These results suggest that extrinsic motivation and competition are not amongst the most reliable predictors of academic cheating behavior

A cross-country evaluation of cheating in academia: is it related to ‘real world’ business ethics?

Today’s economics and business students are expected to be our future’s business people and potentially our tomorrow’s economic leaders and politicians. Thus, their beliefs and practices are likely to affect the definition of acceptable economics and business ethics. The empirical evaluation of the cheating phenomenon in academia has been almost exclusively focused on the US context, and the non-US studies involve, in general, a narrow scope of countries. In the present paper we perform a wide cross-country study on the determinants of economics and business undergraduate cheating which involves 21 countries from the American (4), European (14), Africa (2) and Oceania (1) Continents and 7213 students. We found that the average magnitude of copying among the economics and business undergraduates is quite high (62%) but with a significant cross-country heterogeneity. The probability of cheating is significantly lower in students enrolled in schools located in the Nordic or the US plus British Isles blocks when compared with their South Europe counterparts; quite surprisingly that probability is also lower for the African block. Distinctly, students enrolled in schools from the Western and especially from the Eastern Europe observe statistically significant higher propensities for perpetrating academic fraud. Our findings further suggest that average cheating propensity in academia is significantly correlated with ‘real world’ business corruption.cheating; corruption; university; economics; business; countries

Games for a new climate: experiencing the complexity of future risks

This repository item contains a single issue of the Pardee Center Task Force Reports, a publication series that began publishing in 2009 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.This report is a product of the Pardee Center Task Force on Games for a New Climate, which met at Pardee House at Boston University in March 2012. The 12-member Task Force was convened on behalf of the Pardee Center by Visiting Research Fellow Pablo Suarez in collaboration with the Red Cross/Red Crescent Climate Centre to “explore the potential of participatory, game-based processes for accelerating learning, fostering dialogue, and promoting action through real-world decisions affecting the longer-range future, with an emphasis on humanitarian and development work, particularly involving climate risk management.” Compiled and edited by Janot Mendler de Suarez, Pablo Suarez and Carina Bachofen, the report includes contributions from all of the Task Force members and provides a detailed exploration of the current and potential ways in which games can be used to help a variety of stakeholders – including subsistence farmers, humanitarian workers, scientists, policymakers, and donors – to both understand and experience the difficulty and risks involved related to decision-making in a complex and uncertain future. The dozen Task Force experts who contributed to the report represent academic institutions, humanitarian organization, other non-governmental organizations, and game design firms with backgrounds ranging from climate modeling and anthropology to community-level disaster management and national and global policymaking as well as game design.Red Cross/Red Crescent Climate Centr

Cut-and-Paste Plagiarism: Teaching Student Researchers Boundaries

Librarians affiliated with educational institutions, as members of an academic community, participate in teaching students and future scholars how they share in the responsibility of upholding ethical standards of scholarship and values of academic honesty. Academic honesty, in its variant forms,was part of issues in education long before the introduction of computers. Two forms, plagiarism and copyright infringement, were chronic problems in the print realm and present additional dimensions in today’s electronic environment. The causes of copyright infringement and plagiarism are extensive and complex. Divergent positions are represented in the literature on how to deal with these issues. They are not only legal issues, but moral and ethical issues as well

Plagiarism in philosophy: prevention better than cure

[Introduction] Plagiarism more common than thought in student essays’ would make a good headline. Recent research suggests that students admit to much more plagiarism and other forms of cheating than teachers generally suspect, and it is widely believed that the problem is increasing as a result of the internet. The solution is to use a range of techniques to get the thought back into student essay writing, and to take more active steps to spot when this has not happened

Artificial Intelligence Implications for Academic Cheating: Expanding the Dimensions of Responsible Human-AI Collaboration with ChatGPT

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty and staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future

The detection of cheating in multiple choice examinations

Cheating in examinations is acknowledged by an increasing number of organizations to be widespread. We examine two different approaches to assess their effectiveness at detecting anomalous results, suggestive of collusion, using data taken from a number of multiple-choice examinations organized by the UK Radio Communication Foundation. Analysis of student pair overlaps of correct answers is shown to give results consistent with more orthodox statistical correlations for which confidence limits as opposed to the less familiar "Bonferroni method" can be used. A simulation approach is also developed which confirms the interpretation of the empirical approach.Comment: 16 pages, 13 figure

Catching Cheating Teachers: The Results of an Unusual Experiment in Implementing Theory

This paper reports on the results of a prospective implementation of methods for detecting teacher cheating. In Spring 2002, over 100 Chicago Public Schools elementary classrooms were selected for retesting based on the cheating detection algorithm. Classrooms prospectively identified as likely cheaters experienced large test score declines. In contrast, classes that had large test score gains on the original test, but were prospectively identified as being unlikely to have cheated, maintained their original gains. Randomly selected classrooms also maintained their gains. The cheating detection tools were thus demonstrated to be effective in distinguishing between classrooms that achieved large test-score gains as a consequence of cheating versus those whose gains were the result of outstanding teaching. In addition, the data generated by the implementation experiment highlight numerous ways in which the original cheating detection methods can be improved in the future.

How to Price Shared Optimizations in the Cloud

Data-management-as-a-service systems are increasingly being used in collaborative settings, where multiple users access common datasets. Cloud providers have the choice to implement various optimizations, such as indexing or materialized views, to accelerate queries over these datasets. Each optimization carries a cost and may benefit multiple users. This creates a major challenge: how to select which optimizations to perform and how to share their cost among users. The problem is especially challenging when users are selfish and will only report their true values for different optimizations if doing so maximizes their utility. In this paper, we present a new approach for selecting and pricing shared optimizations by using Mechanism Design. We first show how to apply the Shapley Value Mechanism to the simple case of selecting and pricing additive optimizations, assuming an offline game where all users access the service for the same time-period. Second, we extend the approach to online scenarios where users come and go. Finally, we consider the case of substitutive optimizations. We show analytically that our mechanisms induce truth- fulness and recover the optimization costs. We also show experimentally that our mechanisms yield higher utility than the state-of-the-art approach based on regret accumulation.Comment: VLDB201