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Record W2600608324 · doi:10.18260/1-2--640

Promoting Academic Integrity Through An Online Module

2020· article· en· W2600608324 on OpenAlexaboutno aff
Murali Krishnamurthi, Jason Rhode

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
FundersDivision of Undergraduate EducationNorthern Illinois University
KeywordsAcademic dishonestyCheatingAcademic integrityCommitReputationDishonestyPsychologyPejorativeMisconductInstitutionPollingPublic relationsInternet privacySocial psychologyComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract PROMOTING ACADEMIC INTEGRITY THROUGH AN ONLINE MODULE Introduction Academic integrity is not a new concern and faculty members address it in their courses often, but the rise in academic dishonesty cases indicates an alarming national trend. According to Lathrop and Foss2, a 1998 survey by the publisher of Who’s Who Among American High School Students indicates 83% of students polled admitted that “almost everybody does it” (“it” meaning cheating). Academic dishonesty has far reaching consequences beyond the classroom. Whitley and Keith-Spiegel3 cite a number of reasons as to why educators should be concerned about academic dishonesty. The reasons include “reputation of the institution” and “public confidence in higher education” as evidenced by numerous corporate scandals in recent years. The social consequences of academic dishonesty are far more damaging as Cizek1 points out students who cheat and plagiarize are more likely to do the same in work or in their family situations due to the “habit forming nature of cheating and plagiarism.” Academic dishonesty is not limited just to cheating and plagiarism, but also includes falsification and fabrication of information, contributing to the violation of course policies and procedures, and sabotaging the work of others. It is not just the failing students who commit academic dishonesty. In some instances top students also cheat for a variety of reasons including pressure to keep their GPAs up, lack of time to do their school work due to work and family commitments, poor writing skills, and most importantly the attitude that they are somehow above rules and regulations because they are “good students.” In engineering disciplines, some students genuinely may not know what constitutes plagiarism and what its consequences are. Some have never had a formal exposure to the definition of plagiarism, citation styles, techniques for paraphrasing, and strategies for avoiding plagiarism. International students are especially susceptible to false accusations of plagiarism as viewpoints on intellectual property can sometimes vary across cultures. Faculty members have a difficult time educating students on academic integrity and also keeping up with academic dishonesty incidents. The use of various hand-held and online technologies has added to the difficulty of dealing with academic dishonesty. Faculty not only have to learn and keep up with the latest technology tools, but also have to be more vigilant on how students use technology tools to violate course policies. Almost every academic institution has policies on academic dishonesty on the web, and some also have educational materials, tutorials, and online modules on academic integrity. The educational materials online on academic integrity fall under three categories: (1) policy information on academic dishonesty (numerous universities), (2) simple tutorials on academic integrity with examples and quizzes (York University, Penn State University, Indiana University, Virginia Tech, University of Southern California, Radford University) and (3) multimedia tutorials with audio, stills, and interaction (Rutgers University, University of Guelph).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.142
GPT teacher head0.376
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2020
Admission routes1
Has abstractyes

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