The Application Of Intelligent Context-Aware Systems To The Detection Of Online Student Cheating - ICAS 2013

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The Application Of Intelligent
Context-Aware Systems To The
Detection Of Online Student
Cheating
Thomas Lancaster
Birmingham City University
ICAS 2013

Does Cheating Matter?

Students Are Cheating!
There are lots of statistics to support this
statement
e.g. 60.8% of college students admitted cheating
30,000 respondents
cheating on assignments and tests
and
16.5% of these college students didn’t regret cheating
http://oedb.org/library/features/8-astonishing-stats-on-academic-cheating

Cheating Leads To…
Poor reputation of the university
Students are not able to complete the job they were
assigned to
Poor reputation of the Computing industry
Disadvantageous to honest students who are putting
the time and effort in
Their results may look worse than their cheating peers

We Need To Be Addressing
Cheating
As a community of educational professionals,
ensuring academic integrity needs to be at the
forefront of everything we do
There are opportunities for us to Intelligent
Context-Aware Systems to both prevent and
detect student cheating

Why I’m Interested In Further
Addressing Cheating…
Thomas Lancaster
Senior Lecturer in Computing at
Birmingham City University, UK
Program Leader BSc Computer
Science
PhD “Effective and Efficient
Plagiarism Detection”
Originator of research into
“contract cheating” (with Robert
Clarke)

The Traditional Type Of
Student Cheating

The Most Discussed
Examples…
Student Collusion
Students work together to complete a solution
to a programming task
Student Plagiarism
Student submits a report using sections of text
taken from online web sites

Plagiarism Is Detectable
TurnItIn widely used within education to find text in common
with web sites and a massive database.

Collusion Is Detectable
JPlag (http://www.jplag.de)
used to find clusters
(groups) of students who
have submitted program
source code solutions which
are structural and
syntactically similar

Computing Academics
Have Plagiarism Solved?
Many algorithms to look for plagiarism and
collusion have been developed and tested
There are a lot of software solutions out there
for educational use
Systems like TurnItIn “win out” due to the benefits of a
massive database of previous work

Anti-Plagiarism Systems
Make Limited Use Of Context
Copy of student work (in electronic format)
Work submitted by other students in current
cohort
Work submitted by other students in previous
cohorts/at other universities
Database of web sites/journal papers/lecture
notes

Three More Problems That
Could Perhaps Utilise Context

The Most Blatant Cheating
Is Easy To Detect
but…
Here are three more challenges for discussion
(1) Essay Spinning
(2) Social Media Cheating Groups
(3) Contract Cheating Using Auction Sites
(the printed paper discusses these in more detail and proposes further challenges)

(1) Essay Spinning

Essay Spinning - English
http://translate.google.com

Essay Spinning - Danish

Essay Spinning - Slovenian

Essay Spinning – English
(again)

English to English Is Quite
Different
Within the Computing field, this means that
students are receiving degrees when they
cannot sufficiently program. Hence, when
these students take a job within the
Computing industry, they will be unable to
deliver source code to the standard that their
employer expects. Although such students are
not likely to last long in these jobs, this lack of
ability reflects badly on the university. This
means that other students from that
institution will struggle to get jobs. Further,
with the interconnected world facilitated by
social media, other companies are likely to
also know about the bad quality of the
students obtaining similar degrees.
In the computer field means that students get
degrees, they can not properly program.
Therefore, when these students take a job in
the computer industry will be able to provide
the source code to the standard that their
employer expects. Though these students are
not likely to last long in this business, this
failure reflects poorly on the university. This
means that other students from these
institutions are struggling to get a job.
Continue with the interconnected world of
social media facilitated another company, you
probably know about the low quality of the
students have similar degrees.

This Is A Mild Example…
Poor quality English can be improved using tutorial
support (or may be acceptable for international
speakers)
Can we use context to detect the multi-lingual
version of this translation?
Suggestion – to extend the anti-plagiarism
databases to automatically compare against
translation – using the known languages of the
student as context

(2) Social Media Cheating
Groups

Private Information Sharing
“The superintendent for the West Islip School District said 12
students used Facebook to cheat on their biology homework and
exam.
‘You can quickly, you know, snap a picture of your homework and post it’
He also said the Facebook page was taken down.”
http://newyork.cbslocal.com/2013/05/24/west-islip-students-accused-of-using-facebook-to-cheat

Contextual Solutions
Require students to provide access to all their
social media accounts – then use traditional
anti-plagiarism monitoring and algorithms?
Use time and location cues to provide evidence
of social media cheating?

(3) Contract Cheating Using
Auction Sites

Buying Original Work
“This assignment is to create a Java program that inputs loan
amount, annual interest rate and loan term and outputs the
loan payment information. Attached is the output screen that
it needs to look like when complete. The official assignment
requirements will be forwarded to the winner of the contract
which includes all the details and formats to be used. ”
http://www.freelancer.com/projects/Java/Hire-someone-Java-homework.html

Contract Cheating
Contract cheating occurs when a student has
original work produced for them, which they
then submit for academic credit
This will not be detected by TurnItIn or JPlag

Using Contextual Intelligence?
Need to automatically scan auction web
sites, such as Freelancer and gather information
(evidence)
The contextual challenge is attribution (which student?
which university?)
Can stylometrics (writing style analysis) be used
to identify when work is written out of context?

Recommendations

Need To Innovate
Getting ahead of students in the cheating game is
an “arms race”
If the students put the same amount of effort into their
work, they would all end up with first class degrees!
Context is an underutilised component of cheating
detection
We need to improve our use of intelligent systems to do
this
This paper provides an early contribution to that discussion

For More Information
Thomas Lancaster:
http://thomaslancaster.co.uk
thomas.lancaster@bcu.ac.uk
Slides Available At:
http://slideshare.net/ThomasLancaster
Video Discussion Available At:
http://youtube.com/DrThomasLancaster

Discussion
What are the biggest problems in student cheating?
How can Intelligent Context-Aware Systems aid
academics in preventing and detecting those types of
cheating?

The Application Of Intelligent Context-Aware Systems To The Detection Of Online Student Cheating - ICAS 2013

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The Application Of Intelligent Context-Aware Systems To The Detection Of Online Student Cheating - ICAS 2013