Criminology and contract cheating · Criminology and contract cheating: Prevalence, detection, and...
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Criminology and contract cheating: Prevalence, detection, and prevention Joe Clare Presentation at the 2 nd Annual TEQSA Conference Melbourne, Australia December 1, 2017 1 Acknowledgment of co-authors across papers Dr Guy Curtis, Associate Dean Sonia Walker, & Dr Julia Hobson – Murdoch Dr Michael Baird – Curtin
Transcript of Criminology and contract cheating · Criminology and contract cheating: Prevalence, detection, and...
Criminology and contract cheating:
Prevalence, detection, and prevention
Joe Clare
Presentation at the 2nd Annual TEQSA Conference
Melbourne, Australia
December 1, 2017
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Acknowledgment of co-authors across papers
Dr Guy Curtis, Associate Dean Sonia Walker, & Dr Julia Hobson – Murdoch
Dr Michael Baird – Curtin
What is this talk about?
• Exploring contract cheating from a criminological perspective
o Summarising three 2017 papers to suggest that opportunity theory has a
contribution to make
• Crime is non-random, opportunity-based explanations are the best
way to account for this, and opportunity can be adjusted to prevent
crime
• If opportunity theory is relevant, what would we expect?
o Prevalence
o Repeats (targets and perpetrators)
o Prevention
• What we found
• What does this mean2
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Crime is non-random
It clusters at very specific spaces
4Source. Eck (2015). Who should prevent crime at places? The advantages of regulating place managers and challenges to police services. Policing
10% of addresses account for 80% of crime
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It involves very specific offenders
10% of the population account for
66% of crime(prevalence)
Most active 10% of
offenders account for
41% of crime(frequency)
Source. Martinez, Lee, Eck, & SooHyun (2017). Ravenous wolves revisited: a systematic review of offending concentration. Crime Science
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It involves very specific victims
Source. SooHyun, Martinez, Lee, & Eck (2017). How concentrated is crime among victims? A systematic review from 1977 to 2014. Crime Science
10% of the population experience
74% of victimisation
(prevalence)
Most victimised 10%
experienced 35% of
victimisation(frequency)
7Source: Sidebottom, A. et al. (2011). Theft in price-volatile markets: on the relationship between copper price and copper theft. Journal of Research in Crime and Delinquency, 48(3), 396-418.
It involves very specific targets
Think of it loosely like an 80:20 rule
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• The 80:20 Rule – a small number of things are responsible for a large proportion of outcomes
o The rule-of-thumb is important to target interventions
o The percentages change (80:20) depending on the problem
o Small numbers of offenders are responsible for many crimes
o Small numbers of victims suffer a large amount of victimisation
o Small numbers of places are the locations of many crimes
• In pure terms
o Repeat-location problems (time-space interaction)
o Repeat-offender problems
o Repeat-victim problems (individual/target)
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We can explain the non-randomness of
crime
• The problem analysis triangle helps us understand how and why crime occurs at a specific time in a specific place involving specific people/targets
http://www.popcenter.org/
The problem analysis triangle
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• The problem analysis triangle helps us understand how and why crime occurs at a specific time in a specific place involving specific people/targets
http://www.popcenter.org/
The problem analysis triangle
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Crime is an opportunity-based rational choice
• Decisions to offend are constrained by time, cognitive
ability and information
o Bounded rationality
o Within the context, to that person, it made sense, at the time
• “Perceptions” of the situation and of risks and rewards is
more important that actual circumstances
• Decisions vary by the different stages of the offense and
among different offenders
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Crime is an opportunity-based rational choice
• Individuals who are not normally “criminals” may choose to
offend based on the perceived risks and rewards
• If offenders choose to commit crimes based on a number of
factors, then those factors can be altered to discourage
them from choosing to offend
• Crime – in many circumstances –
is not inevitable
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We can prevent non-random, rational
events by altering the opportunity
Using these explanations to develop targeted prevention
• Effective problem solving requires understanding
o How offenders and targets come together in places (time and space)
o How offenders, targets, and places are not effectively controlled
▪ Handlers, guardians, and managers
• “Think thief” to understand why offending in that opportunity structure was ‘rational’
• Analysis in this way identifies weaknesses in the problem analysis triangle
• This will point to targeted interventions designed to address the specific problem
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We can use this approach to reduce opportunity for crime
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• Manipulate the opportunity structure by…
• Increasing the effort
• Increasing the risk
• Reducing the rewards
• Reducing the provocations
• Removing the excuses
• These are the 5 mechanisms that underpin the25 techniques of Situational Crime Prevention
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18Source: https://t3.ftcdn.net/jpg/01/26/46/68/240_F_126466885_sSsDz5lzxlkXvs7Cv5CsdUELx2LeMVH5.jpg
Relating this to contact cheating
• So what would we expect for people paying a third-party to
do assessments for them?
1. Relatively few students will be doing it
2. A large number of those who are doing it are probably
repeat offenders
3. Not all assessment items are going to be suitable targets
4. Suitable targets will be repeatedly victimised
5. It should be possible to alter currently suitable opportunities
for contract cheating to make them less suitable
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1. Relatively few students will be doing it
• Combined results from 5 self-report
surveys of contract cheating
• N = 1,378 respondents
• 2.1% of students reported engaging
in contract cheating
• Bretag et al., 2017, estimated 2.2%
of respondents had obtained an
assignment to submit as their own
work
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2. Likely to be repeat offending
• Combined results from 5 self-report
surveys of contract cheating
• N = 1,378 respondents
• 63% of contract cheaters reported
doing so more than one
• Contract cheating was moderated by
opportunity
o Students who had studied longer were
more likely to have cheated
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3. Not all assessment items will be suitable targets
• Looking for unusual difference scores
between supervised and unsupervised
assessment items
o 3,798 unit results from 1,459 students
• Unusual pattern (UP) 1
o Unsupervised ≥ 70% and Supervised ≤ 50%
• UP 2
o (Unsupervised − Supervised) ≥ 25 percentage points
• UP 3
o Unsupervised ≥ 80% and
(Unsupervised − Supervised) ≥ 40 percentage points
• UP 4
o Unsupervised ≥ 60% and Supervised ≤ 30%
• UP 5
o (Unsupervised − Supervised) ≥ 95% CI
4.6% incidence
8.1% incidence
0.7% incidence
0.7% incidence
5.0% incidence
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3. Not all assessment items will be suitable targets
• Looking for unusual difference scores
between supervised and unsupervised
assessment items
o 3,798 unit results from 1,459 students
• Unusual pattern (UP) 1
o Unsupervised ≥ 70% and Supervised ≤ 50%
• UP 2
o (Unsupervised − Supervised) ≥ 25 percentage points
• UP 3
o Unsupervised ≥ 80% and
(Unsupervised − Supervised) ≥ 40 percentage points
• UP 4
o Unsupervised ≥ 60% and Supervised ≤ 30%
• UP 5
o (Unsupervised − Supervised) ≥ 95% CI
4.6% incidence
8.1% incidence
0.7% incidence
0.7% incidence
5.0% incidence
Just because the difference patterns are
unusual, doesn’t mean the students are
cheatingo Type 1 errors – false positives - Terrible at
exams? Potentially identified for educational
support
o Type 2 errors – missing those who do just-
enough on exams - Looking across Units
prevents one-offs
These difference patterns are a proxy for
a non-random ‘problem’
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3. Not all assessment items will be suitable targets
0 1 2 3 4 5
CRM_ACRM_BCRM_CCRM_DCRM_ECRM_FCRM_GCRM_HLAW_ALAW_BLAW_CLAW_DLAW_ELAW_FLAW_GLAW_HLAW_ILAW_JLAW_KLAW_L
LAW_MLAW_NLAW_OLAW_PLLB_ALLB_BLLB_CLLB_DLLB_ELLB_FLLB_GLLB_HLLB_ILLB_JLLB_K
Pattern significantly greater at the unit-level• Criminology units
demonstrated
significantly more
frequent unusual
patterns often
• Law units showed
some unusual patterns
• One LLB unit showed
unusual patterns
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31 students with multiple violations: 2.1% of population…
• Previous prevalence estimates
2.1%/2.2%
• Repeats common within this
sample across rule types
• As a proportion of units in the sample:
o Students 3, 8, 9, 14, 16, 22, 25, & 31 had UPs for 100% of units
o Students 2, 17, 23 and 27 had UPs for 3 out of 4 units
Student # UP1 UP2 UP3 UP4 UP5
# Units in
sample
1 5
2 4
3 2
4 4
5 8
6 3
7 6
8 2
9 2
10 4
11 4
12 3
13 3
14 2
15 4
16 2
17 4
18 3
19 7
20 5
21 5
22 2
23 4
24 3
25 2
26 4
27 6
28 5
29 7
30 4
31 3
% unusual
unitsLegend
= no Ups
= 1 UP
= 2 UPs
= 3 UPs
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31 students with multiple violations: 2.1% of population…
• Previous prevalence estimates
2.1%/2.2%
• Repeats common within this
sample across rule types
• As a proportion of units in the sample:
o Students 3, 8, 9, 14, 16, 22, 25, & 31 had UPs for 100% of units
o Students 2, 17, 23 and 26 had UPs for 3 out of 4 units
Student # UP1 UP2 UP3 UP4 UP5
# Units in
sample
1 5
2 4
3 2
4 4
5 8
6 3
7 6
8 2
9 2
10 4
11 4
12 3
13 3
14 2
15 4
16 2
17 4
18 3
19 7
20 5
21 5
22 2
23 4
24 3
25 2
26 4
27 6
28 5
29 7
30 4
31 3
% unusual
unitsLegend
= no Ups
= 1 UP
= 2 UPs
= 3 UPs
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5. If opportunity matters, altering opportunity should prevent contract cheating
Also Case Study 4 in:
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5. If opportunity matters, altering opportunity should prevent contract cheating
• Case study from B.Commerce
student capstone unit
• Varied assessment items
o Business simulation
o Case study
o Weekly eTests
o Presentation
• Anonymous feedback (2015)
revealed contract cheating
problem relating to the parameters
for the business simulation
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5. If opportunity matters, altering opportunity should prevent contract cheating
• Risk, reward, effort, excuses, and
provocations manipulated to adjust
the opportunity structure
• Types of changes included
o Anonymous feedback to allow
reporting
o Team ‘shake-ups’ to break up groups
o Increased variability between classes
o Increased education about academic
misconduct
• 1-year post changes
o Academic misconduct decreased
from 183 to 27 (85% decline)
o Did not hinder genuine students’
ability to succeed
What does all of this mean?
• Consistent estimates – 2.1%/2.2% students contract cheating
• Repeat offending common, moderated by opportunity
• Variability in assessment items for targeting – not all targets
‘suitable’
• Just because the difference patterns are unusual, doesn’t mean the
students are cheating
o Type 1 errors – false positives - Terrible at exams? Potentially identified for
educational support
o Type 2 errors – missing those who do just-enough on exams - Looking across Units
prevents one-offs
o These difference patterns are a proxy for a non-random ‘problem’
• When contract cheating is detected, manipulating the opportunity
structure can prevent the problem
o Not dependent on apprehension30
