Digital Proctor is a company that provides a solution for authenticating online student identities. Their solution uses typing pattern analysis to uniquely identify students and verify their identity as they complete coursework. It can detect potential cases of outsourcing assignments or entire courses by checking for inconsistencies in a student's typing patterns across assignments. The solution also flags unusual cutting/pasting activity and potential collusion. It provides reporting to faculty and administrators on suspicious activity with data to help them investigate cases further. The solution aims to promote academic integrity while being non-invasive to students and protecting their privacy.

1. 2011
Digital Proctor
A Detailed Look Behind the Scenes
Digital Proctor Inc.
1/1/2011

2. Welcome.
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3. Client Objectives
Detailed market and client analysis has yielded the following objectives as indicated by institutions,
without limitation, when implementing a solution for student identity verification purposes:
• Institutions would want a solution for authenticating the identity of students who register for
and participate in online courses and programs
• Institutions would want a solution to enhance their current single sign on architecture through
true multi-factor authentication
• Institutions would want a solution that is committed to protecting the privacy of students and
the security of their personal data.
• Institutions would want a solution that students would not perceive as intrusive or privacy
insensitive
• Institutions would want a solution that is able to serve a global student base
• Institutions would want a solution that is cost-effective and would allow for unlimited use with
respect to each student
• Institutions would want a solution that requires minimal if any time for students to set up or
upgrade
• Institutions would want a solution that does not exceed the system requirements of their LMS
• Institutions would want a solution that allows for potential authentications at any time of the
day or night
• Institutions would want a solution that is robust to accommodates students with slow speed
internet connections
• Institutions would want a solution that accommodates students using multiple computers from
different locations
• Institutions would want a solution that features a robust and intuitive reporting infrastructure
with varying levels of privileged access
• Institutions would want a solution that integrates with the learning management system and
potentially other University systems
• Institutions would want a solution that minimizes the need for technical support.
• Institutions would want a solution that would accommodate a minimum need for training or
orientation.
• Institutions would want a solution that maintains the highest security standards for
administrative, technical, and physical safeguards to protect the security, confidentiality, and
integrity of the University’s confidential information
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4. Key Challenges
Institutions are currently looking out for a solution, which should meet the following challenges faced by
them:
Stronger mechanisms for student identity verification
Systematic and centralized approach to academic integrity
Reduction of exposure to risk
Digital Proctor has appraised itself of the above challenges and is offering a solution delineated in the
following pages. The solution addresses the above challenges and brings about enhanced institutional
experience with regards to student identity verification.
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5. Digital Proctor Solution
Overview
Digital Proctor has developed a powerful set of technologies designed to prevent and detect cheating in
online education.
Digital Proctor provides an unprecedented view into the online learning environment. We implement a
transparent authentication solution that analyzes student behavior invisibly in the background. As a
byproduct of students’ normal interactions with their assignments, we are able to create an individual
identity profile for each student using multiple data points, including our groundbreaking typing
recognition system. Our software verifies student identities, reports atypical cut/copy/paste usage,
detects collusion, and gives faculty members a set of intelligent questions that they can ask students to
confirm or discount any suspicious activity.
We are able to identify students by the unique way that they type on their keyboard while interacting
within the learning management system. Throughout all course activity for a particular student, we
check his/her typing patterns for consistency and can identify if a particular assignment has been
outsourced. That is, if a student has enlisted a friend or paid someone to complete an assignment for
them. Our reporting interface is further capable of detecting if an entire course has been outsourced.
With our software, the identification (authentication) of students is intrinsically bound with the
completion of assignments. The students interact normally within the learning management system,
and as a byproduct of this interaction, we can uniquely identify them.
If the typing pattern is inconsistent and it appears that the student has in fact outsourced an
assignment, the adminstrators, faculty, and staff members have access to a comprehensive reporting
interface which incorporates a robust set of data points around the suspicious assignment or course.
From this data, the faculty member can ask intelligent questions and investigate the suspicious activity.
This affords the faculty member a light touch, non accusatory way to open a dialogue with the student
and confirm or deny the suspicious activity.
In addition to preventing outsourcing through student identity verification, our product also provides
faculty with insight into unusual cut/copy/pasting activity that a student is executing within the learning
management system. Our reporting interface also highlights cases of blatant collusion, that is, when
students are working on particular assignments together (when they should not be).
Our most recent feature is a commenting system that allows administrators, faculty, and staff to make
notes on particular students within the reporting interface.
Digital Proctor takes the privacy of our clients and their students extremely seriously. It is important to
understand that we do not collect what a student types, and are not classified as keylogging software.
In fact, the order of the keys that a student types are scrambled and unable to be reconstructed before
they are sent to our server for analysis. Further, all data sent to and from our servers is protected by
256-bit encryption and our reporting interface resides on an HTTPS server.
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6. The primary objective of our product is to stop the most blatant forms of cheating (ie outsourcing
assignments, pasting answers from the web, working together on assignments) with the least amount of
invasion. Students already give off unique identifiers as they complete coursework. Digital Proctor
analyzes these identifiers, checks them for consistency, and packages them in an intelligible and, if
needed, an actionable format for the faculty members and administration.
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7. Product Description
Digital Proctor provides a complete solution for student identity verification, that is, credible verification
that a student who registers for a course is the same student who completes the course and receives
credit. The design of the student authentication solution affords an additional layer of functionality.
That is, an electronic proctoring tool, specifically capable of capturing instances of collusion and atypical
cut/copy/paste activity.
Because the solution is fully hosted, students and faculty are not required to download or install
anything and the solution transfers from computer to computer, accounting for the mobility of the
modern student.
The solution begins when a student logs in, running transparently and non-confrontationally in the
background. Throughout a student’s activity, the solution collects several data points including: typing
pattern samples, location information, browser characteristics, software environment, date and time,
and then maps all of this information to a particular assignment or activity for the duration of the
student’s course(s).
This data is then packaged and sent to our server for analysis. If the typing pattern analysis for a student
yields consistent results, the student is successfully authenticated and receives a passing check mark. If
the typing pattern for a student is inconsistent, a probability calculation considers the likelihood that the
student outsourced an assignment or course, and then a student receives either a passing check mark or
is flagged with an “x” indicating that suspicious activity has been detected.
This data culminates in an intuitive reporting user interface that is continuously accessible to
administrators and faculty. If a student is flagged for suspicious activity, the administrator or faculty
member can access the assignment or course history of the student, find out what specifically we
flagged as suspicious, and then investigate the situation appropriately. Equipped with multiple data
points and information around the suspicious activity, the faculty member or administrator can make an
intelligent inquiry to the student and properly investigate the assignment(s) or course(s). A best
practices guideline outlines the recommended course of action, but of course, the institution will
ultimately decide this.
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8. Electronic Proctoring Functionality
In addition to the student identity verification, some institutions have expressed an interest in additional
electronic proctoring tools to further strengthen academic integrity. The two primary tools packaged into our
product in addition to student identity verification are the cut/copy/paste detection tool and the collusion
discovery tool.
The electronic proctoring solutions are capable of detecting instances of suspected collusion and
atypical cut/copy/paste activity (CCPA).
Detecting suspected collusion is accomplished through a creative combination and filtering process of
the existing data points that we collect for our student authentication solution.
CCPA is an added functionality that allows us to capture the amount of this activity for any given
assignment, and then determines what exactly was pasted so that the faculty member or administrator
can determine whether or not the paste was a legitimate action.
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9. Software Updates
Updates occur seamlessly for the end-user, because all updates occur on the server side. End-users are
not required to implement an update.
A later version of our software would deploy seamlessly for the end-user with no implications.
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10. Administrator, Faculty, and Staff Training
Students do not need to be trained how to use our solution, as we are only interested in what students
are already doing naturally. Administrators, faculty, and staff members receive training sessions, made
available at a frequency determined by the institution, on how to navigate and interpret the data in the
reporting user interface. Depending on the institutions preference, these training sessions can be given
on-site or remotely via webinars. Typically, each instituion receieves two access periods to review a
recorded webinar, which is followed up by a live webinar in which a representative from Digital Proctor
answers any remaining questions.
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11. True Multi-factor Authentication
Digital Proctor provides true multi factor authentication in strict accordance with the Federal Financial
Institutions Examination Council’s (FFIEC) conclusion that, “By definition true multifactor authentication
requires the use of solutions from two or more of the three categories of factors. Using multiple
solutions from the same category at different points in the process may be part of a layered security or
other compensating control approach, but it would not constitute multifactor authentication." The
categories of factors including:
• Something the user knows (e.g., password, PIN);
• Something the user has (e.g., ATM card, smart card); and
• Something the user is (e.g., biometric characteristic, such as a unique typing pattern).
Digital Proctor leverages each category to provide true multi factor authentication. Specifically:
Something the user knows
Utilizes the secure login/password combinations currently issued by the client through its SSO
architecture.
Something the user has – Including:
A particular browser environment identifiable by cookie files, height and width characteristics, and other
metadata such as the particular version of the browser.
A particular location where assignments are completed.
A particular software environment.
A particular schedule when assignments are completed identifiable by date and time of activity
The client can opt in to all or none of these particular data collection points.
Something the user is
Analyzes students’ unique typing patterns, an established behavioral biometric, as they interact within
the learning environment. Checks each student’s typing pattern for consistency, ensuring all
assignments are completed by the same student.
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12. Data Collection and Student Privacy
All data to and from our server is protected by 256 bit encryption.
All analyses of students is conducted blindly, without using their names. For our typing pattern analysis,
we do not collect the order of keystrokes that students enter into the learning management system. In
order to obtain a biometric watermark of students' typing patterns, we only need timing measurements.
This allows us to scramble the order of keys into an unreconstructable order before they are even sent
to our server for analysis. In this manner, the solution is legally not classified as a keylogger according to
DLA Piper’s professional opinion.
Additional data points that are optionally collected include: IP address, browser characteristics, software
environment, time of activity, and data that is cut, copied, and/or pasted into the learning management
system.
The institution owns the data collected on students, but there are restrictions due to FERPA. For more
information about FERPA, please see section 6.3 in the Digital Proctor Software License and Hosting
Agreement.
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13. Multiple Computers
Because the solution is fully hosted, it is transferable from computer to computer without any need for
a download or installation.
A student using a different keyboard will exhibit a slightly different typing pattern from time to time;
however, we automatically detect if a student is using a different keyboard and take this into account to
limit false positives resulting from different keyboard use. But even more importantly, while a student
may exhibit a slightly different typing pattern from one keyboard to the next, the difference between
these samples is still far less than the typing pattern of another student. Therefore, we can verify
student identity across multiple computers.
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14. Reporting
Administrators, faculty, and staff can access reports at any time through our reporting user interface.
Access privileges are currently designed to give faculty members access to their specific courses only
and administrators access to all courses. Currently, students do not have access to the reporting user
interface.
Primary indicators of suspicious behavior:
The detection of more than one distinct typing pattern under a single student account
The same typing distinct typing pattern across more than one account
Secondary indicators of suspicious behavior:
Different location detected for “higher stakes” assignments
Different time of activity for “higher stakes” assignments
Different browser characteristics for “higher stakes” assignments
Different software environment for “higher stakes” assignments
Reporting capabilities include:
• Failure to match one student authenticating at time of registration with attempt during the
semester
• Failure to match one student authenticating at multiple points during semester across
multiple courses
• Failure to match one student authenticating in two different semesters
• Matches between two or more “different” students in a given semester or across semesters
• Failure to match one student authenticating at multiple points in a given semester in a
single course as well as across multiple courses
• Detecting one student posing as one or more other students (exhibiting the same profile for
authentication)
• High level statistical reporting for administrators
• Identify only the top x% of suspicious students
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15. Approach to Providing the Scope of Services
Implementation Methodology
The solution has three main components:
The first component is installed on or alongside the learning management system. This collects typing
data and other unique characteristics of students' activities while they are registering and completing
assignments at an institution. It is important to emphasize that the privacy of students is assured: 1)
Typing data is randomized before being sent to us so we cannot see what a student types, only how they
type it; 2) all data is sent over secure SSL (which is the same security used by banking websites); and 3)
typing data is signed using a 256-bit encryption scheme (this is the highest level of security of the
options the US government recommends using) to assure it genuinely comes from the right student.
Another key aspect of this component is that it is very light weight. It runs seamlessly in the background
on a user's computer. Not one student at the schools we have serviced has complained about this
software. Also, the total amount of data sent from a user's computer is only a few kilobytes per
minute. Historically, the installation process for this component has taken about fifteen minutes of
system administrator’s time, plus another thirty minutes of us providing background information and
testing.
The second component is the server on our end that receives the data sent from students. This server is
amazingly stable. Last semester (Spring 2011), there were no crashes or unscheduled downtime. There
was only one fifteen minute period of scheduled maintenance, and that occurred at 1am on a weekend,
when activity was at its lowest. It is important to note that, in the extremely unlikely event that our
collection servers go down, students would still be able to complete assignments as normal. There is no
negative impact except the assignments students complete during that time would not be verified.
The third component is our web user interface. This analyzes, organizes, and displays a wealth of
information about student activity. Faculty members are able to see activity from their courses, and
administrators can see all the activity at their institution. There are pages to narrow in by a course, by
an assignment, by a student, and more. Each page is intuitive but also contains embedded help
dialogues.
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16. False Positives
Solutions that involve biometrics are susceptible to type I (false positives) and type II errors (false
negatives).
First, we want to clarify terminology to ensure an accurate response. Digital proctor adopts the
following standard definitions of type I and type II errors and their implications:
In biometrics, the null hypothesis is that the input does identify someone in the searched list of people.
For this solution specifically, the null hypothesis is that the input (authentication) of a student matches
the previous input of the same student. Again, the null hypothesis is that the student authenticating is
the same student who registered for the course.
Type I error (false positive) – The error of rejecting the null hypothesis when it should not have been. In
the context of student authentication, a type I error occurs when the biometric system fails to
authenticate the student when it should have authenticated the student. That is, a false positive would
indicate the honest student is not completing their own work, when in fact they were completing their
own work.
Type II error (false negative) – The error of failing to reject the null hypothesis when it is in fact not true.
In the context of student authentication, a type II error occurs when the biometric system authenticates
the student when it should have failed to authenticate a student. That is, a false negative would
indicate that a dishonest student, who is outsourcing their assignment(s), was completing their own
work, when in fact they were not completing their own work.
Inherent to biometric systems is the correlation between type I and type II errors. Our statistical system
is designed to keep the number of false positives to an absolute minimum, even at the cost of allowing a
small number of false negatives dishonest students to go unnoticed. Digital Proctor’s philosophy is that
it is far worse to falsely accuse an honest student than to let a dishonest student go through undetected.
In combination with the biometric component of our solution, we have implemented a human
intelligence based component that serves to further reduce the number of false positives that our
system might reveal. This component looks at the “stakes” of an assignment after we have detected a
different typing pattern and calculates the likelihood, using a number of different methods, that the
student would have outsourced that particular assignment.
Further, the best practices guidelines that we encourage institutions to follow encourages investigating
instances of suspicious activity using a non accusatory, data oriented line of questioning as opposed to a
quick pass or fail judgment. A dishonest student who is confronted over suspicious activity using our
recommended method will most likely cease all future suspicious activity and/or admit to some form of
deviance. Knowing that someone is looking over their shoulder, will be a compelling force to keep
students honest. If an honest student is confronted, they should easily be able to account for any
suspicious activity and deny any claims to the contrary without hesitation.
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17. Special/Unique Qualifications
Digital Proctor embodies the ideal synergy of technical talent and client relations.
The technical team is led by Andrew Mills, who in addition to his striking technical background and
accomplishments, is a clear communicator and works excellent in team environments.
Client relationships are managed by Shaun Sims, who works ceaselessly to make sure client expectations
are promptly met and exceeded. Shaun leverages his carefully cultivated network of leaders in the
space to stay ahead of the current issues facing higher education and sets internal policies that keep
Digital Proctor in line with industry best practices.
The size and organizational structure of Digital Proctor allows us to respond quickly to customer
requests without delay. Digital Proctor has access to one of the country’s most accomplished talent
pools in Austin, Texas, including relationships with premium employers and sources of capital to help us
grow securely and source customer requests as needed.
Digital Proctor has been recognized for the following awards:
DFJ-Cisco Global Business Plan Competition Finalist
1st Place Milken-Penn GSE Competition '10
1st Place UT Idea to Product '10
2nd Place Texas Moot Corp '10
McGinnis Venture Competition Semi-Finalist '10
Selection DLA Venture Pipeline
Digital Proctor is also represented by one of the world’s largest international and most respected law
firms, DLA Piper.
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