Trust-Related Privacy Factors in E-Learning Environments

Historically, e-learning opportunities have focused on the design and delivery of course content without significant consideration given to privacy concerns (El-Khatib, Korba, Xu, & Yee, 2003). Given the ever-increasing volume of student information that resides online, this historical lack of attention to privacy is unsustainable. As learners and instructors become more aware of the risks relating to the disclosure of student information, providers of distance education courses will need more guidance in addressing these privacy risks. Today’s web-based software and other tools provide the opportunity for innovation and enhanced learning environments that involve student-driven interactions (Diaz, Golas, & Gautch, 2010), but with these new tools comes a heightened privacy concern that institutions and instructors who design and implement courses online need to address.Many factors influence student trust in an online learning environment, and privacy issues are among them (Wang, 2014). Anwar and Greer indicated that earning student trust in online learning environments is key to the success of online learning and that privacy is equally important (2012). According to Anwar and Greer (2012), “Privacy and trust are equally desirable in a learning environment. Privacy promotes safe learning, while trust promotes collaboration and healthy competition, and thereby, knowledge dissemination” (p. 62). With these propositions as a starting point, this review of the e-learning-focused privacy literature aimed to synthesize existing online educational privacy guidance available to instructors in a distance learning setting, both generally and as it related to learner trust online.Online tracking of individuals is becoming more common and more pervasive. Most commercial websites download some form of tracking software onto users’ computers, from cookies that store user names and passwords to perhaps hundreds of files or programs, most of which typically originate from companies that track and sell web user data (Angwin, 2010). The presence of tracking programs is not always apparent to an Internet user. These programs often come from hidden files within downloads or display ads. Certainly, consumers might appreciate the personalized experience that is made possible through a third party comprehensively tracking Internet behavior, but relevant, targeted ads are just one result of online data collection. This type of data that is “mined” from a user’s online activity is a significant source of revenue for web-based companies, and it stands to gain in net worth as more interested parties discover the value of collected information and are willing to pay for it.It follows that from a business standpoint, it is valuable to know as much as possible about a consumer and what he or she typically is seeking. As technology progresses, the process of researching consumer behavior is becoming increasingly accurate and efficient in every way, and the level of sophistication of tracking technologies continues to rise. To the extent that an online learner is situated similarly to an online consumer of noneducational goods or services, the value of an online student’s mined data can be just as high. Therefore, concerns with privacy and student data security are real.Government agencies worldwide have adopted privacy laws and policies aimed at protecting personal information (El-Khatib et al., 2003). In the United States, a public interest in educational privacy is reflected by several laws that aim to protect student privacy, including Family Educational Rights and Privacy Act of 1974 (FERPA) and the Higher Education Opportunity Act (2008), as well as the Children’s Online Privacy Protection Act (1998) as it relates to collecting personal information from persons under the age of 13, and of course broader privacy regulation including the Privacy Act of 1974; the proposed Consumer Privacy Protection Act (2015); recent state legislation in Illinois (the Illinois Bio-metric Information Privacy Act); Texas (the Capture or Use of Biometric Identifier), aimed specifically at the use of biometrics in online settings; Washington’s biometric identifier law which became effective on July 23, 2017; and California’s Student Online Personal Information Protection Act, which went into effect in January of 2016 and addresses personal information on websites, applications and online services that focus on K–12 students. A comprehensive survey of laws and their application to the e-learning environment is outside the scope of this review but would prove informative to future research and literature addressing privacy. The main goal of this review is to identify existing literature that addresses FERPA and other privacy concerns in e-learning environments and to then consider a framework for future exploration through literature of the challenges of online educational privacy issues, including those issues that specifically relate to learner trust.FERPA (20 U.S.C. § 1232g; 34 CFR Part 99) (1974), a Federal law that protects the privacy of student education records, is at the forefront of student privacy concerns and applies to all schools that receive funds under an applicable program of the U.S. Department of Education. Going forward, the interpretation of FERPA and its applicability to online course design and implementation will necessarily inform the use of online tools in support of distance education as a starting point for establishing privacy best practices (Diaz et al., 2010). For example, when adopting a course management system platform as well as collaborative technologies such as social media, blogs and wikis, and screencasting tools, an instructor is asking course participants to share with service providers a variety of information, some of which learners will consider private (Kim, 2013).Generally, institution-licensed course management system platforms such as Blackboard have a privacy policy that invokes FERPA and discloses that the company collects personally identifiable information from or about students. Black-board’s Privacy Policy stated that it considers student data to be “strictly confidential and in general does not use it for any purpose other than improving and providing our Services to the school or on the school’s behalf.” (sec. 7, para. 1). The policy also purports to comply with the U.S.-EU Safe Harbor Framework and the U.S.-Swiss Safe Harbor Framework as set forth by the U.S. Department of Commerce regarding the collection, use, and retention of personal information from European Union member countries and Switzerland. In comparison, free and open source course management system platforms that do not offer an educational license generally say nothing about FERPA and do not purport to comply with data protection laws from jurisdictions outside the United States. In other words, social media and other free software and applications potentially open the academic environment to the public (Rodriguez, 2011).Despite this increased likelihood of exposure of student data through online distance education courses, there is an apparent gap in literature that speaks to this topic. This review examined literature that provided a starting point to the student privacy discourse.Asllani (2012) discussed FERPA requirements in the context of online education but focused on student information, including financial data, records, advising opportunities, and grades as opposed to course content. Almost 10 years ago, Alexander, Jones and Brown (1998) also addressed privacy concerns of faculty and students regarding the potential compromise of private data, but this survey was not aimed at online education; rather, it addressed the educational information that institutions store digitally. Similarly, Culnan and Carlin (2008) discussed educational privacy concerns related to data breach and theft of information such as social security numbers and alumni data, as opposed to data related to online coursework.Daries et al. (2014) focused on FERPA regulations and distance education in the context of massive open online courses (MOOCs). However, Daries et al. (2014) discussed the intentional release of de-identified student data in order to promote research on the composition of MOOC-enrolled students and to add to the body of literature about MOOCs. FERPA, in this situation, according to the authors, was a hindrance to social science research because of privacy concerns. Interestingly, Daries et al. (2014) also noted, “not all institutions consider MOOC learners to be subject to FERPA” (p. 58). This might come into play as social scientists continue to work toward future data releases.With similar goals for the use of student data, Goyal and Vohra (2012) supported the mining of students’ educational data, or educational datamining, in various settings. They proposed that datamining in higher education could help to improve student performance, learning outcomes, course choices, and retention through the collection, analysis, interpretation, and presentation of educational datamining (Goyal & Vohra, 2012). Goyal and Vohra (2012) described the two primary student data collection methods in an e-learning environment as (a) statistics—or the review of files and databases to determine information such as where students enter an exit the online environment, the most popular web pages, the number of resource downloads, pages actually browsed by a student, and the time the student spent on a page; and (b) visualization—looking at patterns of student users’ online behavior and data such as summative assessment scores, tracked attendance and group activity. Goyal and Vohra (2012) did not explicitly discuss the privacy implications of educational datamining, but they did address data security of warehousing the mined student data. Of course, many of the privacy concerns that FERPA attempts to address, and that scholars possibly implicate with their proposed mining and use of student data, might also be the basis of issues with learner trust in an online education environment. But a review of the literature in this sub-area of online educational privacy concerns leads to limited consideration and discourse related to a framework for examining issues of privacy-related trust in distance learning environments.There is a significant body of literature that has addressed the importance of building trust in distance learning environments (e.g., Brookfield, 2015; Carchiolo, Correnti, Longheu, Malgeri, & Mangioni, 2008; Liu, Chen, & Sun, 2011; Wang, 2014). A search for literature that has taken into account the trust-related privacy implications of e-learning environments resulted in a much smaller dataset. Wang (2014) was one of the few scholars who framed a discussion of participant trust in online education communities against a privacy backdrop; she proposed a “social-technical framework of trust-inducing factors” (p. 347) in distance education, which discussed privacy and security issues based on approaches found in the literature of ElKhatib et al. (2003) and Anwar and Greer (2012). Wang (2014) classified 12 features of trusted learning environments into four groups: (a) credibility, (b) design, (c) instructor sociocommunicative style, and (d) privacy and security. Wang (2014) suggested that important aspects of a framework of trust-inducing factors for online educators are: posting a clear and sufficient privacy policy, using security measures in design and access to distance learning environments, and complying with accepted U.S. and E.U. security standards.El-Khatib et al. (2003) discussed privacy principles in Canada as they related to various learning technology standards, specifically addressing “trust mechanisms” between learning platform users and service providers. They proposed that the service providers need to trust that a learner is actually the individual authorized to take a particular course, and that the learners need to trust the service that is provided. More accurately, according to ElKhatib et al. (2003), “the learner must believe the service provider will only use his/her private information, such as a name, address, credit card details, preferences, and learning behavior in a manner expressed in the policy provided for the e-learning system user” (p. 14). El-Khatib et al. (2003), presented the infrastructure for the framework of the digital authentication of provider trust, and provided a technical overview of trust management systems. This direction approached learner trust from the technology instead of the privacy regulation perspective and did not provide practical guidance at the instructor level.Anwar and Greer (2012) also addressed trust in e-learning environments, focusing on trust relationships among co-learners, through identity management models for e-learning forums. Through a privacy-enhancing identity management model, Anwar and Greer (2012) proposed a pseudonymous “partial identity” for online collaborators to determine an effective reputation management system that would allow learners to present themselves anonymously. As with the El-Khatib et al. (2003) modeling, the Anwar and Greer (2012) insights, although they presented an interesting perspective on co-learner trust, did not provide instructor-level guidance.Finally, Diaz (2010) suggested that to foster trust when using a publically available facilitating technology or service provider, an instructor should include a statement in the course syllabus that confirms that the student consents to the use of specific collaborative tools that are open to the public and that the contributions to the tool might be part of the student’s educational record that will be disclosed. Tang, Hu, and Smith (2008) ostensibly supported Diaz (2010) when they indicated that privacy protection measures, such as privacy policies, can enhance levels of consumer trust and are likely effective in terms of consumers online; however, they also pointed out that the costs of increased privacy measures might create inefficiencies that have negative effects on social welfare. This concept translates well to online learning environments, although the authors did not specifically address education in their consumer-based analyses.It is worth considering that the increased cost and time commitment of heightened privacy measures—from drafting new privacy policies to researching platforms, to monitoring third-party data collection and use—might be unattractive to institutions, faculty, and students. This might be an explanation for the lack of a push to add educational privacy standards and measures more quickly, and it also might explain the relatively small body of literature supporting research and standards related to e-learning privacy issues. Whatever the reason, there is a dearth of literature providing helpful insights into FERPA, trust-related privacy concerns, and other privacy and data security risks in e-learning environments.Technology does not stand still. The dynamic evolution of distance education continues to incorporate the latest technological advances on its quest to reach more and more learners across the globe. There was a time when writing was a new technology; today, educators can collaborate with learners through real time idea-sharing written discourse, audio-visual discussion threads, and videoconferencing; track learners’ movements, patterns and activity within an online learning platform; and facilitate online tests that are proctored via biometric identity authentication. Educational relationships online take data sharing to a whole new level, on a variety of platforms and devices. But what, exactly, are participants in the average learning environment sharing—and do they know? As applied to distance education, privacy laws are nascent and hardly able to keep up with the evolving technologies that instructors and students employ. Additional research is needed to support a framework within which to address privacy under FERPA and other privacy regulations—and to contribute to an environment of trust—in online distance courses and communities.

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