e-journal

Testing a model to predict online cheating—Much ado about nothing

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Much has been written about student and faculty opinions on academic integrity in testing. Currently,
concerns appear to focus more narrowly on online testing, generally based on anecdotal assumptions that
online students are more likely to engage in academic dishonesty in testing than students in traditional oncampus courses. To address such assumptions, a statistical model to predict examination scores was recently
used to predict academic dishonesty in testing. Using measures of human capital variables (for example,
grade point average, class rank) to predict examination scores, the model provides for a comparison of R2
statistics. This model proposes that the more human capital variables explain variation in examination scores,
the more likely the examination scores reflect students’ abilities and the less likely academic dishonesty was
involved in testing. The only study to employ this model did provide some support for the assertion that
lack of test monitoring in online courses may result in a greater degree of academic dishonesty. In this study,
however, a further test of the predictive model resulted in contradictory findings. The disparate findings
between prior research and the current study may have been due to the use of additional control variables
and techniques designed to limit academic dishonesty in online testing.

Keywords: Online cheating, predicting academic dishonesty

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Informasi Detail

Judul Seri

Active Learning in Higher Education

No. Panggil

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Penerbit

New York : SAGE Publications Ltd., 2014

Deskripsi Fisik

Active Learning in Higher Education 2014, Vol. 15(1) 65– 75

Bahasa

English

ISBN/ISSN

DOI: 10.1177/1469787

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

2014, Vol. 15(1)

Subjek

PENDIDIKAN

Info Detail Spesifik

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Pernyataan Tanggungjawab

agus

Versi lain/terkait

Lampiran Berkas

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