Matching Items (5)

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Understanding the Motivation to Cheat Under High and Low Control

Description

Under what conditions are people more likely to cheat? In this study, we looked to examine the effect of personal control in connection with the motivation to cheat. Specifically, we are interested in which individuals were more likely to engage

Under what conditions are people more likely to cheat? In this study, we looked to examine the effect of personal control in connection with the motivation to cheat. Specifically, we are interested in which individuals were more likely to engage in, or accept, illegal activity when a cheating cue, signaling either a high or low probability of other people to cheat, is present. Results indicate that individuals who perceive they have low (vs. high) personal control are more likely to cheat when they believe others are not cheating (a low cheating cue), but they cheat directionally less when they believe many other people are cheating (high cheating cue). Moreover, when the cheating cue is high, both low and high control individuals believe the risk of being watched and the risk of being caught is significantly greater than when a low cheating cue is present.

Contributors

Agent

Created

Date Created
2014-05

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Responses to Cheating in Need-Based Transfers: An Agent Based Model

Description

Gift-giving economies are economic models that freely give resources rather than barter for them or purchase them from market. Need-based transfers fit into this economic model by freely giving resources on the basis of need, provided the giver can spare

Gift-giving economies are economic models that freely give resources rather than barter for them or purchase them from market. Need-based transfers fit into this economic model by freely giving resources on the basis of need, provided the giver can spare the resources. The Maasai are an East African pastoral tribe that practices need-based transfers through a tradition they call osotua. If they have a partner with an established osotua relationship, then they will give any amount of cattle that partner request, provided they can spare the cattle. Cheating each other is unheard of in this tradition, but for this simulation I am introducing cheating into this economic model through feigning need. If a cheater is not in need, they will act like they are in need. If they are in need, then the cheater will request more cattle than what they need to survive. I am testing two different responses to cheating: walking-away and punishing. In the walk-away condition, the victim ends their osotua partnership and establishes a new one. In the punishment condition, a portion of the cheater's stolen cattle is destroyed.

Contributors

Agent

Created

Date Created
2016-12

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Academic Integrity among University Journalism Students An Action Research Project to Study the Impact of Online Educational Modules

Description

Academic integrity among college students continues to be a problem at colleges and universities. This is particularly important for journalism students where ethical issues in the profession are critical, especially in an era of “fake news” and distrust in the

Academic integrity among college students continues to be a problem at colleges and universities. This is particularly important for journalism students where ethical issues in the profession are critical, especially in an era of “fake news” and distrust in the media. While most journalism students study professional ethics, they do not necessarily make the connection between their future careers and their academic career. In fact, at Western Washington University (Western) a recent exploration into academic dishonesty revealed that violations were increasing, and that journalism was one of the top three majors where violations occurred (based on percent of majors). To address this problem of practice, an online academic integrity resource – specific to journalism – was developed to see whether it could increase students’ knowledge as it relates to academic integrity and decrease violations. The mixed methods action research (MMAR) study took place during summer and fall quarter at Western Washington University, a state university located in Bellingham, Washington. Participants included students who were pre-majors, majors, and minors in the three tracks of journalism: news-editorial, public relations, and visual journalism. They were given multiple opportunities to self-enroll in the Resource for Ethical Academic Development (READ) Canvas course for academic integrity. Self-efficacy theory and social learning theory provided a framework for the study. Data was collected through pre- and post-innovation surveys as well as qualitative interviews. Quantitative results suggest that there is work yet to do in order to educate students about academic integrity and potential consequences of behavior. Qualitative results suggest that one avenue may be through an online resource that provides concise and comprehensive information, models behavior relevant to the student’s own discipline, and is easily accessible. It also suggests that a culture change from a systemic emphasis on grades to a focus on growth and individual learning may be beneficial.

Contributors

Agent

Created

Date Created
2021

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Kitsune: Structurally-Aware and Adaptable Plagiarism Detection

Description

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.

Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.

Contributors

Agent

Created

Date Created
2020

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Improving proctoring by using non-verbal cues during remotely administrated exams

Description

This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head pose and affective state for detection of cheating incidences in

This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head pose and affective state for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s non-verbal cues indicated that time delay, the variation of a student’s head pose relative to the computer screen and confusion had significantly statistical relation to cheating behaviors. Additionally, time delay, head pose relative to the computer screen, confusion, and the interaction term of confusion and time delay were predictors in a support vector machine of cheating prediction with an average accuracy of 70.7%. The current algorithm could automatically flag suspicious student behavior for proctors in large scale online courses during remotely administered exams.

Contributors

Agent

Created

Date Created
2015