Detecting probable cheating during online assessments based on time delay and head pose

Chia Yuan Chuang, Scotty Craig, John Femiani

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This study investigated the ability of test takers’ behaviors during online assessments to detect probable cheating incidents. Specifically, this study focused on the role of time delay and head pose for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s behavior indicated that not only time delay but also the variation of a student’s head pose relative to computer screen had significant statistical relation to cheating behaviors. Additionally, time delay and head-pose variation relative to a computer screen were predictors in a logit model of cheating prediction with an average accuracy of 75.6%. The current algorithm could automatically flag suspicious student behavior for proctors in large-scale online courses during remotely administered exams.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalHigher Education Research and Development
DOIs
StateAccepted/In press - Mar 23 2017

Fingerprint

incident
incidence
student
time
ability

Keywords

  • cheating
  • E-learning
  • head pose
  • time delay

ASJC Scopus subject areas

  • Education

Cite this

Detecting probable cheating during online assessments based on time delay and head pose. / Chuang, Chia Yuan; Craig, Scotty; Femiani, John.

In: Higher Education Research and Development, 23.03.2017, p. 1-15.

Research output: Contribution to journalArticle

@article{8df6eaba75f8460485727def2b21b940,
title = "Detecting probable cheating during online assessments based on time delay and head pose",
abstract = "This study investigated the ability of test takers’ behaviors during online assessments to detect probable cheating incidents. Specifically, this study focused on the role of time delay and head pose for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s behavior indicated that not only time delay but also the variation of a student’s head pose relative to computer screen had significant statistical relation to cheating behaviors. Additionally, time delay and head-pose variation relative to a computer screen were predictors in a logit model of cheating prediction with an average accuracy of 75.6{\%}. The current algorithm could automatically flag suspicious student behavior for proctors in large-scale online courses during remotely administered exams.",
keywords = "cheating, E-learning, head pose, time delay",
author = "Chuang, {Chia Yuan} and Scotty Craig and John Femiani",
year = "2017",
month = "3",
day = "23",
doi = "10.1080/07294360.2017.1303456",
language = "English (US)",
pages = "1--15",
journal = "Higher Education Research and Development",
issn = "0729-4360",
publisher = "Routledge",

}

TY - JOUR

T1 - Detecting probable cheating during online assessments based on time delay and head pose

AU - Chuang, Chia Yuan

AU - Craig, Scotty

AU - Femiani, John

PY - 2017/3/23

Y1 - 2017/3/23

N2 - This study investigated the ability of test takers’ behaviors during online assessments to detect probable cheating incidents. Specifically, this study focused on the role of time delay and head pose for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s behavior indicated that not only time delay but also the variation of a student’s head pose relative to computer screen had significant statistical relation to cheating behaviors. Additionally, time delay and head-pose variation relative to a computer screen were predictors in a logit model of cheating prediction with an average accuracy of 75.6%. The current algorithm could automatically flag suspicious student behavior for proctors in large-scale online courses during remotely administered exams.

AB - This study investigated the ability of test takers’ behaviors during online assessments to detect probable cheating incidents. Specifically, this study focused on the role of time delay and head pose for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s behavior indicated that not only time delay but also the variation of a student’s head pose relative to computer screen had significant statistical relation to cheating behaviors. Additionally, time delay and head-pose variation relative to a computer screen were predictors in a logit model of cheating prediction with an average accuracy of 75.6%. The current algorithm could automatically flag suspicious student behavior for proctors in large-scale online courses during remotely administered exams.

KW - cheating

KW - E-learning

KW - head pose

KW - time delay

UR - http://www.scopus.com/inward/record.url?scp=85015849056&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015849056&partnerID=8YFLogxK

U2 - 10.1080/07294360.2017.1303456

DO - 10.1080/07294360.2017.1303456

M3 - Article

AN - SCOPUS:85015849056

SP - 1

EP - 15

JO - Higher Education Research and Development

JF - Higher Education Research and Development

SN - 0729-4360

ER -