You log into your learning management system and begin grading final exams for your online course. You notice that Jane Smith and John Brown have remarkably similar essay responses. You recall from the introduction discussion that Jane and John went to the same high school, have remained friends during college, and even live in the same co-ed dormitory. How can you ensure that their work is the result of their own individual efforts? If you suspect these two students have violated academic integrity in your online course, how do you prevent this in the future?

Under the Higher Education Opportunity Act, institutions must ensure students who register for online courses are the same students who participate in and receive credit for completing the course. 34 CFR 602.17(g)(1) requires the institution to verify the identity of the student. The institution meets this requirement by assigning a unique login and password so the student can enter the online course. Within the course, instructors can verify identity by requiring proctored exams. Both institutions and courses can also make use of technology and practices to verify student identity and authenticity of work. With the rise of educational technology “solutions,” such as plagiarism detectors, biometrics and learning analytics, how can instructors satisfy the law while still respecting student data privacy?

Academic dishonesty (AD) is not a new phenomenon in education. However, cheating in online courses is viewed as easier to accomplish and more prevalent than face-to-face courses (Peled, Y., Eshet, Y., Barczyk, C., & Grinautski, K. 2018). This may be due to a perception of “distance” between the instructor and students, the ease of access and availability of online information and, to some extent, the lack of student understanding of academic dishonesty. Other factors, such as external expectations, fear of failure or lack of personal integrity may also contribute to dishonesty in online courses. Contrary to the perspective that it is easier to cheat in an online course, Peled et al found that “e-learners exhibited less propensity to engage in AD if compared to their counterparts in face-to-face courses” (p. 56). This is possibly due to the typical characteristics of distance learners: strong intrinsic motivation, self-determination and self-regulation, and expectation of real-world inspired problems and solutions. Online students, regardless of their characteristics or motivation, are still susceptible to academic dishonesty.

The two most common approaches to academic misconduct are prevention and enforcement. Prevention aims to stop misconduct before it occurs. As “[t]he instructors will remain the first line of defense against cheating and it will be up to them to reinforce values, foster a culture of integrity and lead by example” (Amigud et al, p. 206), instructors should:

1. use extensive calendaring to promote task planning and time management;
2. monitor ongoing stream of work instead of exams;
3. randomize exam questions to individualize an exam for each student;
4. discuss academic integrity to create awareness and commitment;
5. allow asynchronous learning to decouple student progress;
6. track student submissions to identify potential inconsistencies; and
7. provide prompt feedback to facilitate a student’s assessment of progress.

Lee-Post, A., & Hapke, H. (2017, p. 137).

Wagner, Enders, Pirie, & Thomas (2016) researched how synchronous video meetings can address academic dishonesty. Their study found regular real-time video meetings between an instructor and students helped students maintain pacing within the content, prevented impersonation, and created personal connections between instructor and students, which in turn deters student desire to cheat. The study noted the intent of the video meetings was to check the identity of the student and authenticity of the student’s work. During video meetings, students reflect on their work, answer questions on the topic or connect it to a real world problem, indicating their mastery of the topic and evidencing their ownership of the work. In addition to providing assurances of student identity and authenticity of work, the video meetings also establish a connection between the instructor and students breaking down the perceived “distances” in online learning referenced by Peled (2018).

Use of learner analytics is becoming more common to prevent academic dishonesty. Information about the student, such as time of log in, amount of time spent in the learning management system (LMS), learning progress and performance can be utilized to ascertain academic misconduct. But is this a wise practice? Watson, Wilson, Drew, & Thompson (2017) argue “continuous … surveillance inhibits the participant from taking control over their learning. Continuous online assessment as surveillance therefore raises issues of trust and radically alters the premises of higher education” (p. 1042). This concern is echoed by Morris & Stommel (2017) in A Guide for Resisting Edtech: the Case against Turnitin. “There’s something terribly parasitic about a service that plays on our insecurity about students and our fears of cheating. And it’s not just leaching student intellectual property, and reinforcing teachers’ mistrust of students, it’s actually handicapping teachers from exercising their pedagogical agency.”

Thinking back to the introductory issue, how can online instructors ensure that student work is the result of their own individual efforts? Teacher, know thy student. Make the effort to establish a real connection with the student. Make your learning environment a safe place where students can express themselves, and are comfortable confiding in you or other students. With an open and caring environment, students can better resist the temptation to cheat. As real connections form, instructors can better recognize when student work may not be original. This is a teaching moment, not a moment to judge and convict. Discuss the issue. Explain the ramifications (look at what happened to Milli Vanilli). Work out a solution together with the student.

How can instructors prevent academic dishonesty? Teacher, look to the real world. Once the student leaves your institution and enters the workforce, they will not be expected to have rote recall of facts or figures. They will not be isolated from people or resources. Employers want workers who can analyze a problem, find an answer and develop a plan. This can be done by allowing open book-open note tests, multiple attempt quizzing, collaborations and group work. The actual assessment can be done through student reflections, discussion and collaborative work within the course. Mastery of a subject is not always dependent on the final exam. Instead, it is evidenced in how the student worked through the material in a real and meaningful way.

“The issue of academic integrity should be viewed holistically because the overall effectiveness of any academic integrity depends on more than just the technology, but requires sound policy, administrative, and pedagogical practices” (Amigud et al, p. 206).

References

Amigud, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldan, A. (2017). Using Learning Analytics for Preserving Academic Integrity. International Review of Research in Open & Distance Learning, 18(5), 192–210. https://doi.org/10.19173/irrodl.v18i5.3103

Higher Education Opportunity Act, 110 P.L. 315, 122 Stat. 3078, 110 P.L. 315, 2008 Enacted H.R. 4137, 110 Enacted H.R. 4137

Lee-Post, A., & Hapke, H. (2017). Online Learning Integrity Approaches: Current Practices and Future Solutions. Online Learning, 21(1), 135–145. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ1141915&site=eds-live

Morris, S., & Strommel, J. (2017). A Guide for Resisting Edtech: the Case against Turnitin. Hybrid Pedagogy, June 5, 2017. Retrieved from https://hybridpedagogy.org/resisting-edtech/

Peled, Y., Eshet, Y., Barczyk, C., & Grinautski, K. (2018). Predictors of Academic Dishonesty among undergraduate students in online and face-to-face courses. Computers & Education. https://doi.org/10.1016/j.compedu.2018.05.012

Teclehaimanot, B., You, J., Franz, D., Xiao, M., & Hochberg, S. (2018). Ensuring Academic Integrity in Online Courses. Quarterly Review of Distance Education, 19(1), 47–52. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=eft&AN=131537048&site=eds-live

Wagner, E., Enders, J., Pirie, M., & Thomas, D. (2016) Supporting academic integrity in a fully-online degree completion through use of synchronous video conferences. Journal of Information Systems Education, 27(3), 159-174. https://search.proquest.com/docview/1928986196?accountid=14470

Watson, C., Wilson, A., Drew, V., & Thompson, T. L. (2017). Small data, online learning and assessment practices in higher education: a case study of failure? Assessment & Evaluation in Higher Education, 42(7), 1030–1045. https://doi.org/10.1080/02602938.2016.1223834