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International Review of Research in Open and Distributed Learning
Volume 24, Number 4
November – 2023
On the Ethical Issues Posed by the Exploitation of
Users’ Data in MOOC Platforms: Capturing Learners’
Perspectives
Matthieu Tenzing Cisel
Institut Des Humanités Numériques, CY Cergy Paris Université
Abstract
Due notably to the emergence of massive open online courses (MOOCs), stakeholders in online
education have amassed extensive databases on learners throughout the past decade. Administrators of
online course platforms, for instance, possess a broad spectrum of information about their users. This
information spans from users’ areas of interest to their learning habits, all of which is deduced from
diverse analytics. Such circumstances have sparked intense discussions over the ethical implications and
potential risks that databases present. In this article, we delve into an analysis of a survey distributed
across three MOOCs with the intention to gain a deeper understanding of learners’ viewpoints on the
use of their data. We first explore the perception of features and mechanisms of recommendation
systems. Subsequently, we examine the issue of data transmission to third parties, particularly potential
recruiters interested in applicants’ performance records on course platforms. Our findings reveal that
younger generations demonstrate less resistance towards the exploitation of their data.
Keywords: learning analytics, massive open online course, MOOC, ethics, recommender systems, data
privacy
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On the Ethical Issues Posed by the Exploitation of Users’ Data in
MOOC Platforms: Capturing Learners’ Perspectives
Over the past decade, virtual learning environments have been increasingly used in educational settings
and, along with them, dashboards whose aim is notably to monitor users’ activity. This evolution has
triggered vivid debates worldwide over the ethical questions raised by the use of learning analytics (LA),
whose potential applications have been widely documented (Ifenthaler, 2017; Ifenthaler & Tracey,
2016). Pardo and Siemens (2014) listed the privacy issues that needed to be considered during the design
of LA-based research projects if learners were to “make rational, informed choices regarding consent to
having their data collected, analysed, and used” (p. 438). Prinsloo and Slade (2015, 2017, 2018)
highlighted the importance of students’ self-management of analytics.
To address these issues, Jisc, a charitable organization that champions the use of digital technologies in
the UK, developed a Code of Practice for Learning Analytics (Jisc, 2015; Sclater, 2016), which discusses
a taxonomy of issues and necessary governance structures. An Australian study by West et al. (2016)
collected views from key academic stakeholders, from professors to institutional leaders, to develop a
decision-making framework aimed at fostering transparent institutional policies and broader ethical
literacy.
The rise of massive open online courses (MOOCs) in the early 2010s led to a sharp increase in
publications on the use of LA on an unprecedented scale. Early studies dealt with hundreds of thousands
of learners (Breslow, 2013; Ho et al., 2014), and there continues to be a stream of research focusing, in
some cases, on the behaviour of millions of registrants worldwide (Kizilcec et al., 2017; Wintermute et
al., 2021), raising numerous ethical issues (Marshall, 2014).
As MOOC designers, we took the opportunity to investigate some of these issues through a survey
consisting of closed questions, which was broadcast in three different online courses that started in 2021.
We employed a self-selection sampling strategy. The large audiences of these courses enabled us to
gather a significant number of responses, and respondents were likely prone to consider the issue of
data privacy because all of their actions were being logged by the hosting platform. We investigated two
emblematic data-related use cases in MOOCs: recommender systems and the transmission of data to
third parties. The first represents a typical service aimed at improving user experience, while the latter
involves the monetization of user data. In the next paragraphs, we expose some of the reasons why these
problems are significant in the context of MOOCs.
The need for user oversight of LA has become increasingly pressing with the rise of artificial intelligence
in online learning (Hsu et al., 2022), and the increasing use of adaptive systems with personalization
(Hwang et al., 2022). As underscored in our previous publication focused on MOOC catalogues, the
diversity of the offer has increased sharply during the 2010s (Cisel, 2019), increasing the need for
dedicated recommender systems. In some online course platforms such as DataCamp (Baumer et al.,
2020), users are even provided with a list of course and resource suggestions after an automated
assessment in order to improve their score over time.
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A few publications have delved into the issue of recommender systems in the context of MOOCs: “With
MOOCs[’] proliferation, learners will be exposed to various challenges and the traditional problem in
TEL [technology enhanced learning], finding the best learning resources, is more than ever up to date”
(Bousbahi & Chorfi, 2015, p. 1813).
As the audience of MOOC platforms grows, associated recommendation algorithms may influence at
large scales the topics that users choose to pursue. This could lead to gradual homogenization of learning
projects (Tough, 1971) across the landscape of online education, and one should not underestimate the
potential long-term impacts of these recommender systems. While these long-term dynamics should not
be overlooked, learners may be more immediately concerned with what happens to the data fed into
these algorithms, particularly with regard to potential data leaks. Due to privacy concerns, researchers
working with MOOC data have developed frameworks to ensure the protection of users’ anonymity (Hutt
et al., 2022). It is worth exploring learners’ perspectives on the exploitation of information even for
services like recommender systems, which were designed to improve the user experience. This becomes
even more important if this information is monetized through interactions with third parties (Ferguson,
2019).
Selwyn (2019, p.11) highlighted the risk that profit-led projects posed to public education in the
“burgeoning data economy.” He advocates that learners should have a greater oversight on their LA, a
topic that gained increasing importance with the implementation of the General Data Protection
Regulation (Presthus & Sørum, 2019). With regard to MOOCs, employers may express skepticism about
the value of MOOC certificates (Radford et al., 2014), particularly because not all classes are career
relevant (Kizilcec & Schneider, 2015). Employers might therefore show more interest in other types of
data generated by these courses (Allal-Chérif et al., 2021).
If we adopt a strict definition of MOOCs—online classes with free registration and open access to most
course material (Daniel, 2012)—concerns about the monetization of learners’ data are heightened by the
complexity of MOOCs’ business models (Porter, 2015). During the early stages of the MOOC movement,
authors demonstrated that these courses were costly to design and broadcast (Hollands & Tirthali, 2014)
and that income from selling certificates rarely covered design costs (Burd et al., 2015). While it was
argued that these classes did not necessarily need to be economically sustainable as they were used in
part as advertisement tools by academic institutions (Kolowich, 2013), it remains likely that various
stakeholders, especially hosting platforms like Coursera, will seek to diversify their income sources
(Burd et al., 2015). It is therefore relevant to explore learners’ reactions to the monetization of their data,
particularly if it involves sharing it with third parties like recruiters, and to understand how different
groups of learners may respond to varying business models. This latter issue drove us to incorporate
sociodemographic variables in our analysis.
Sociologists have repeatedly proven the importance of age when it came to individuals’ perspectives on
information privacy (Regan et al., 2013). Miltgen and Peyrat-Guillard (2014, p. 107), for instance,
underscored the influence of age in an influential empirical research study: “In addition to culture, age
dictates how people relate to IT, a phenomenon that influences their privacy concerns.” A generational
shift in the concern over data privacy could trigger long-term changes in the acceptability of a diverse
range of uses of LA; while the respondents’ ages were collected in most of the aforementioned
quantitative studies, this information has not been exploited to understand the data. These diverse
considerations led us to investigate a set of research questions, three of which are presented in this
article:
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RQ1: What are learners’ perspectives on the exploitation of MOOC data to feed recommender
systems, and how much do their opinions depend upon the goal of the recommendation?
RQ2: What are learners’ perspectives on the transmission of MOOC data to third parties
(recruiters, etc.)?
RQ3: To what extent does the age of learners affect their perspective on the use of their data?
Based on the findings of Miltgen and Peyrat-Guillard (2014), we hypothesized that younger users were
more open to the use of their data.
Privacy Concerns and the Calculus Framework
The scholarly exploration across the field of management information systems (MIS) and other related
disciplines inspired Dinev et al. (2013) to develop a comprehensive research model that can prove useful
to the interpretation of privacy concerns regarding LA. We relied on this author’s work, including
literature reviews, as a theoretical framework to interpret our data. Central to this model is the “calculus
framework” of privacy, which encapsulates the dynamic interplay between risk and control elements (as
per the studies of Culnan & Armstrong, 1999; Dinev & Hart, 2006). This calculus perspective on privacy
has been acknowledged for its significant utility in dissecting the pressing privacy issues faced by the
contemporary consumer (Culnan & Bies, 2003).
Underpinning this perspective is the concept that privacy is not an absolute term. It implies that there
is a certain fluidity to it, shaped by a cost–benefit analysis or, in other words, a “calculus of behavior”
(Laufer & Wolfe, 1977, p.22). Such a notion draws a nuanced portrait of privacy, showcasing it as a
concept that extends beyond the simplistic binary of private or public. To strengthen this core concept
of privacy, Dinev et al. (2013) has interwoven it with modern theories of control and risk. These theories
strive to elucidate the process by which individuals formulate their perceptions of risk and control
regarding their personal information.
The extent of privacy concerns that an individual harbours has been demonstrated to influence their
decisions about revealing personal information. Personal data and the concept of privacy risk play vital
roles in this process. Additionally, the calculus framework of overall privacy (as highlighted by Dinev &
Hart, 2004, 2006) further emphasizes the risk–control interplay.
Interestingly, both risk and control are seen to function as privacy-driven beliefs linked to the possible
repercussions of data disclosure, raising questions about the perceived risks of a disclosure of LA data.
Therefore, based on this extensive literary exploration, Dinev and Hart pinpoint the two primary factors
that contribute to the perception of privacy: the control perceived over one’s information and the
perceived risk associated with it.
This calculus perspective on privacy, hailed for its utility in analysing modern consumer privacy
concerns (Culnan & Bies, 2003), reiterates the understanding that privacy is not a fixed construct. As
advocated by Klopfer & Rubenstein (1977), it is a fluid concept, subject to a cost–benefit analysis and,
thus, forms a part of the “calculus of behavior” (Laufer & Wolfe, 1977).
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These identified components—perceived control and perceived risk—are then integrated into Dinev et
al.’s (2013) model. The author proposes that the risk an individual perceives is influenced by a
combination of factors. These include the expected outcomes from sharing their information, the
sensitivity of the information, the importance placed on transparency about the information, and the
legal expectations tied to regulations.
Delving deeper into the model, the privacy calculus concept suggests that individuals weigh costs against
benefits in their privacy decisions. This trade-off scenario is assumed to be a vital aspect when an
individual is considering sharing personal information with service providers or businesses. This is
because, according to the calculus perspective of privacy, consumers carry out a cost–benefit analysis
when asked to provide personal data (Culnan, 1993; Dinev & Hart, 2006; Milne & Gordon, 1993).
This decision-making behaviour is driven by an individual’s aim to achieve the most positive net
outcome, as highlighted by Stone and Stone (1990). Overall, Dinev et al. (2013) extends the calculus
perspective on privacy by incorporating it with an understanding of how contextual (information
sensitivity), organizational (transparency importance), and legal (regulatory expectations) influences
shape an individual’s perceived risk. They further propose that this blend of influences collectively
determines an individual’s decision on information disclosure, whether it be LA for a recommender
system or grades in a MOOC for a potential recruiter.
Methods
In this section, we initially present different authors’ approaches to the ethics of data use in online
education, and then position our work within this expanding literature. We then delve into the data
collection tool—MOOCs—and discuss Likert scale–based items. We finally describe the respondents’
characteristics.
Information Ethics: An Empirical Approach
Although many ethical issues raised by the increasing use of LA have been mapped through literature
reviews (Cerratto Pargman & McGrath, 2021; Tzimas & Demetriadis, 2021) and specific articles, the
majority of contributions are conceptual, especially from the perspective of information ethics (Rubel &
Jones, 2016). In a literature review, Hakimi et al. (2021) listed only 14 empirical papers or case studies
out of the 77 publications about the ethical concerns raised by LA that they considered, therefore
highlighting the need for more data collection on the topic. The articles published by Schumacher and
Ifenthaler (2018) and Jones et al. (2020) well illustrate a qualitative approach to exploring learners’
perspectives. Both articles feature qualitative studies on students’ expectations about how their analytics
would be used.
Our current work belongs to the category of survey-based assessment of learners’ perspectives on their
analytics, with the aim of understanding how their attitudes vary depending on the proposed purpose of
these analytics (Arnold & Sclater, 2017; Viberg et al., 2022; West et al., 2020; Whitelock‐Wainwright et
al., 2019; Whitelock‐Wainwright et al., 2020; Whitelock-Wainwright et al., 2021). Whitelock-
Wainwright and colleagues proposed and validated the Student Expectations of Learning Analytics
Questionnaire (SELAQ), a comprehensive tool that encompasses common issues and applications of LA
in higher education. SELAQ considers three dimensions: expectations of service features,
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communalities, and ethical and privacy concerns. “Will the university ask for my consent before my
educational data are outsourced for analysis by third‐party companies?” is a typical question drawn from
the latter category. A significant proportion of items are relevant only in the context of a university. As
the survey is targeted towards students, a significant proportion of items are relevant only in a university
context; therefore, this research instrument is not applicable to our context. Unlike these authors, our
study focuses on a narrower range of possible uses but explores them in greater depth.
Using MOOCs to Circulate Surveys
As argued by Zimmermann et al. (2016), MOOCs are great tools to collect data, notably through surveys.
Due to their ability to quickly reach thousands, if not tens of thousands of potential respondents, these
courses have been used extensively over the years to carry out survey-based research projects. In some
of our previously published research, thanks to collaboration with dozens of MOOC instructors, we
gathered more than 8,000 responses, enabling us to obtain statistically robust results (Cisel, 2018).
Stephens-Martinez et al. (2014) used MOOCs to gather thousands of responses for a survey aimed at
evaluating the mock-up of a dashboard. Learners were required to assess the utility of a set of indicators
meant to monitor course activity.
The use of an online course to broadcast a survey on LA is made all the more relevant by the fact that
respondents are themselves directly concerned with the question. The mere registration and navigation
to the page where the survey is embedded (we located the form within the course itself) generates logs
that can thereafter be analysed. Many respondents typically engage in proposed graded activities, which
makes questions about the processing of their grades and analytics relevant to them. Self-selection bias
is inherent in online surveys (Bethlehem, 2010), but it did not impede comparisons within items or
across age categories, as self-selection affected all groups equally.
Data Collection Tool
The MOOC platform France Université Numérique (FUN) was established by the French Ministry of
Higher Education in 2014 and has gathered millions of registrations over the years (Cisel, 2019;
Wintermute et al., 2021); it requires all instructors, at the beginning of each course, to incorporate a
survey of 26 items focusing on users’ sociodemographic data and motivations to follow the class. This
survey also allows instructors to include a few of their own questions.
We incorporated three items based on the use of LA in the form of 4-point Likert items in three MOOCs
launched in 2022: Data and Critical Thinking, An Introduction to Data Science, and Blended Learning
in Higher Education. The use of Likert items is a departure from the binary yes/no approaches that were
adopted by Arnold & Sclater (2017), for instance. The authors designed items that limited respondents
to a binary “yes” or “no” response—for example: “Would you be happy for data on your learning activities
to be used if it kept you from dropping out or helped you get personalized interventions?” Likert items
introduce more nuance in the responses (Joshi et al., 2015).
Likert Items
The first item laid emphasis on the type of data used by recommendation systems: “If you use a learning
platform enough (like FUN MOOC or Coursera), it has a certain amount of data about you: your
interests, skills, etc. Technically, this data can be transmitted to a number of stakeholders outside the
platform (however, this is not the case for FUN). Out of the following data, what do you think about the
fact that they could be transmitted to third parties (potential recruiters, etc.)?”. The items listed were:
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courses completed, key strengths, grades, areas of interest, learning habits, registrations, and dropped
courses.
The second item was on the features of recommender systems: “Imagine you are browsing a MOOC
catalogue, like FUN MOOC, and following classes. Within the platform, recommendation algorithms
suggest personalized elements (courses, activities, learning partners, etc.) to improve your learning
experience. What is your position on the fact that your data are mobilized to provide you with the
following recommendations?” Five possibilities were then listed: grades, key strengths, courses
completed, areas of interest, and learning habits.
The third item was about the data used by recommender systems: “The more you agree to share data
about yourself, the more it is possible to improve the performance of recommendation algorithms (for
courses, educational content, etc.). What is your position on sharing the following personal data in order
to improve a recommender system?” Seven different types of data were then presented, ranging from
courses completed to learning habits.
At the beginning of each question, we reminded the learners that the MOOC platform that hosted the
courses did not use their data in any of the ways listed in the items. We wanted the respondents to
understand that they were dealing with theoretical situations that did not apply directly to them in their
MOOC.
Sample
The three courses were open for an entire year, from November 2021 to November 2022, allowing for
continuous collection of surveys as users kept registering. Out of the 3,018 responses received
throughout the year, 404 were incomplete, resulting in a total of 2,614 valid responses. Most of them
came from the Data and Critical Thinking MOOC (62.5%) (Table 1). The sex ratio was close to 1:1, and
69.8% of respondents lived in France. For further analyses, we segmented age into 10-year intervals,
beginning at 16. Learners over 66 years of age were grouped into a single category, representing
approximately 10% of respondents. They were, however, excluded for further analyses since most were
retired as per French law, and items on transmission of data to third parties like recruiters therefore
proved irrelevant.
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Table 1
Descriptive Statistics for Relevant Variables Among Respondents (N = 2,614)
Variable
Level
% of respondents
Age (years)
16–25
11.8
26–35
18.6
36–45
19.5
46–55
22.3
56–65
18.0
66+
9.9
Gender
Male
51.1
Female
48.2
Other
0.6
Country of
residence
France
69.8
Other
30.2
MOOC (origin of
responses)
Data and Critical Thinking
62.5
Data Science and Its Stakes
6.6
Blended Learning in Higher
Education
30.8
Data Analysis
Various strategies exist for designing Likert items. One key dichotomy lies in the choice between even
and odd numbers of levels. Researchers commonly favour a 5-point or 7-point scale (Dawes, 2008) when
determining their agreement or disagreement with a given option. This approach, however, has been
criticized for enabling undecided respondents to default to the median option (Dawes, 2008). An even
number of points urges respondents to “pick a side”; hence, it was our chosen method. All three items
regarding learners’ perspectives on the use of their data were presented in 4-point Likert items.
Statistical analysis of Likert data has followed two distinct approaches over the years. The first involves
converting the different scale levels into numbers. For example, the levels somewhat favourable and
favourable might be represented by the numbers 3 and 4, respectively. This method facilitates the use
of comprehensive models like linear regression, enabling comparison of the relative influence of
independent variables such as gender and age. Conversely, some researchers prefer to treat the Likert
item as categorical data. The approach of treating numeric data as if it were categorical has faced
criticism for violating the fundamental assumptions of parametric models. Since our main independent
variable was age, reducing the necessity for comprehensive models, we opted for the traditional Chi-
square approach to Likert data (Croasmun & Ostrom, 2011). Age was categorized to facilitate this
analysis. To visualize our results, we used the Likert function from the HH package in R 4.0.
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Results
Below, we examine learners’ views on recommender systems, concentrating on their attitudes towards
various system features and the kind of data used to power them. Next, we present their views on data
sharing from online course platforms with third parties, such as recruiters. In both scenarios, we
demonstrate the correlation between the learners’ age group and their perspectives.
Recommender Systems
Even if there were statistically significant differences across the four items of the survey
(X2 [9, N = 2,614] = 846.03, p < 2.210−16), respondents overall had favourable opinions about the use of
a recommendation system for the different use cases that we listed. Exercise and course
recommendation scored the highest (49.0% and 60.1% agreed, respectively) (Figure 1).
While respondents seemingly had a positive view of the existence of these algorithms in different
situations, they “frowned upon” the fact that these algorithms used different types of data to make
recommendations (Figure 2). Their position differed significantly depending on the type of data (X2 [9,
N = 2,614] = 130.8, p < 2.210−16).
Figure 1
Posture Towards Different Features of the Recommender System (N = 2,614)
The use, in recommender systems, of the IDs of the videos that were viewed was the least controversial
(55.6% agreed, and among them 40.7% completely agreed), while at the other extremity of the spectrum,
the exploitation of learners’ activity in course forums by recommender systems was rejected by the
majority of respondents (50.6% disagreed overall, among which 37.6% completely disagreed) (Figure
2). Recommendation algorithms’ mobilization of learning habits also appeared to generate stronger
rejection than data such as registration data.
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Figure 2
Posture Towards the Use of Different Types of Learner Data to Enable the Use of Recommendation
Systems (N = 2,614)
Respondents’ age was strongly correlated with their perspective on the use of different data in the
recommender systems, as illustrated by algorithms’ use of data about learning habits (X2 [12,
N = 2,614] = 129.7, p < 2.10−16). Younger learners overall appeared less reluctant to have their data used.
For instance, 17.2% of learners in the 16–25 age category completely opposed the use of data on learning
habits for any type of recommendation, while this proportion reached 36.4% of older users in the 56–
65 age category (Figure 3). This pattern appeared to be consistent across all types of data used by
recommendation systems, and it also held when it came to the transmission of learners’ data to third
parties, as we will see in the next section.
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Figure 3
Participants’ Age and Posture on the Use of Learning Habits Data in Recommendation Algorithms
(N = 2,614)
Transmission of Data to Third Parties and Generational Divides
Respondents overall opposed the possible transmission of their data to third parties, with a statistically
significant difference across items (X2 [9, N = 2,614] = 217.5, p < 2.210−16) (Figure 4). This opposition
was weaker for the list of courses that they had completed (only 33% completely disagreed) but appeared
strong when it came to learning habits (49% completely disagreed) or to the different classes that they
had registered to (55% completely disagreed).
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Figure 4
Learners’ Stance on Data Transmission to Potential Recruiters (N = 2,614)
Learners’ posture on data transmission to third parties depended upon their age. We found statistically
significant relationships between age and all types of data, with stronger effects for data such as grades
(X2 [12, N = 2,614] = 279.2, p < 2.10−16 ; Figure 5) and learning habits (X2 [12, N = 2,614] = 252.8,
p < 2.10−16; Figure 6).
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Figure 5
Relationship Between Age and Posture on Grade Transmission Data to Potential Recruiters
(N = 2,614)
Younger generations (16–25 years) were much more open to the transmission of their grades (35%
completely agreed; only 15% completely disagreed). Most respondents over 35 years old opposed the
transmission of their grades; the ratio of respondents who completely disagreed to completely agreed
reached 54% to 15% for those aged 56–65 (Figure 5). A similar trend was observed with regard to the
transmission of data on learning habits. Among the youngest learners (Figure 6), 28% completely agreed
while 26 % completely disagreed. For individuals aged 56–65, the former and the latter categories
accounted for 12% and 61% of responses, respectively.
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Figure 6
Relationship Between Age and Posture on Transmission of Learning Habits Data to Third Parties
(Potential Recruiters, etc.; N = 2,614)
Discussion
We here discuss the questions of recommender systems and of transmission of data to third parties,
which represent two typical uses of learners’ data, on the one hand to improve the user experience and
on the other hand to generate revenues. We then highlight the existence of a potential generational
divide on data privacy, especially with regard to the transmission of data.
Recommender Systems
Like Arnold and Sclater (2017), we observed significant contrasts with regard to how respondents would
react to a set of proposed use cases. The authors noticed that students were on average reluctant to the
idea of their data being used to enable performance comparisons within the classroom, while,
unsurprisingly, a minority would raise objections to these data being used to improve grades. Our study
mirrored these findings. Participants generally exhibited favourable attitudes towards the different
functionalities proposed, with one exception—exercise recommendation. A notable portion of the
respondents expressed moderate disagreement with this case of data use, highlighting privacy concerns
similar to those noted by Culnan and Armstrong (1999).
Drawing parallels with the concept of privacy calculus by Dinev and Hart (2006), we argue that students
perform a cost–benefit analysis when determining the acceptability of data use. They weigh the potential
benefits—such as improved grades—against the perceived privacy risks—such as the discomfort
associated with performance comparisons if learning data were to be used outside of the context of
recommender systems.
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To address these variances in attitudes, MOOC platforms could consider offering a customizable user
experience with regard to recommender systems. This approach aligns with Dinev’s integrated model,
in which perceived control over personal information is a key factor in privacy perceptions. As such,
these platforms could allow learners to toggle specific features of the system. Some developers, such as
Bousbahi and Chorfi (2015), have already taken steps in this direction by giving users the ability to select
which data are fed into the recommendation algorithms. This approach affords a degree of
personalization and imbues learners with a sense of control over their data, which, according to Culnan
and Bies (2003), can effectively mitigate privacy concerns.
Consider a platform like Facebook as an example. Users on this social networking site are provided with
the flexibility to determine their own privacy parameters. They can set rules on a variety of aspects such
as who is permitted to view their profiles and personal details, the individuals who can locate them
through the platform’s search function, the methods through which others can contact them, and even
the kind of personal narratives that get published on their profiles. Adjustable privacy settings seem to
significantly influence users’ comfort level regarding privacy. It has been posited that when individuals
perceive that they have an adequate degree of control over their personal information, their concerns
surrounding privacy typically diminish (Culnan & Armstrong, 1999).
Broadening user control over what is fed to the algorithms, as suggested by Bousbahi and Chorfi (2015),
can significantly reduce resistance to adopting recommender systems in online learning platforms such
as Coursera or FUN MOOC. When learners are given more autonomy over their data, they are likely to
trust the system more, leading to higher engagement and more effective use of the recommender
features. While it may be improbable for users to have a say in the decisions made by online learning
stakeholders with regard to the use of these algorithms, providing them with a degree of control could
prevent feelings of distrust. If users feel that their privacy is being violated, they may not necessarily
stop using the platform, but they might choose to disregard the recommendations made by the system,
rendering it less effective. Therefore, the sense of control over their data could not only ease privacy
concerns but also boost the utility and relevance of the recommender systems.
Transmission of Data to Third Parties
Sharing data with third parties, such as potential recruiters, was once regarded as a promising business
model for MOOCs and is now a practice that has been adopted by course platforms like Udacity (Allal-
Chérif et al., 2021). Unsurprisingly, younger learners showed less reluctance than senior workers with
regard to the transmission of their LA. This result is consistent with the generational divide that Miltgen
and Peyrat-Guillard (2014) observed regarding data privacy in their qualitative study. Respondents
entering the job market, in contrast to those with a well-established professional trajectory, might view
data sharing as an opportunity to increase their visibility. However, third parties such as recruiters might
be interested in data of which the transmission may not necessarily benefit learners.
Learning habits and details on uncompleted courses, whether due to lack of engagement or difficulties,
could potentially offer employers deeper insights into an individual’s profile. However, the transmission
of this type of data would mean much weaker control for learners over the display of their activity on the
course platform; it would represent a significant shift from the current situation, where individuals
mostly show what classes they have engaged in (Allal-Chérif et al., 2021) or what certificates they have
obtained (Radford et al., 2014), but not their grades, transmission of which could be informative for
employers but harmful for potential applicants. Respondents’ may be conscious of this misalignment,
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which could explain why they appeared much more reluctant to share data on their learning habits than
on their key strengths, for instance.
Arguably, in contrast with online platforms that focus exclusively on upskilling the workforce, generalist
platforms like Coursera and FUN MOOC cover a diversity of topics that are arguably unrelated to
learners’ professional activity (poetry, etc.; Cisel, 2019). This means that these platforms potentially
have detailed information on users’ hobbies, as long as we consider that registering to a course signals
someone’s area of interest. Our results suggest that learners may be reluctant to the transmission of this
type of data.
This is likely one of the unique aspects of generalist MOOC platforms: a learner might have different
objectives depending on the type of course they enrol in (Kizilcec & Schneider, 2015). This is in contrast
with career-oriented platforms—DataCamp, for example, which is focused on data science (Baumer et
al., 2020). As Miltgen and Peyrat-Guillard (2014, p. 113) showed through focus groups, for similar types
of data, individuals have a distinct stance on privacy depending on the context in which they could be
used. Expectations regarding the management of their LA could vary significantly across different types
of courses (e.g., career-oriented or hobby-oriented), adding complexity to the task of platforms striving
to customize data management according to user preferences.
A Generational Divide in Terms of Data Privacy?
Concerning the correlation between learners’ age and acceptance of the use of their data, our findings
align with prior studies on users’ viewpoints regarding data privacy (Miltgen & Peyrat-Guillard, 2014;
Regan et al., 2013). While this generalized explanation may suffice for recommender systems, it seems
less comprehensive when considering the question of data transmission to third parties, as it leaves
essential queries unaddressed: How much do respondents’ views hinge on their generational affiliation?
How much do they rely on their age? Currently, it is challenging to determine whether the existing
response structure will remain consistent over time.
The perceived value of visibility to potential recruiters—as understood in the Dinev et al.’s (2013)
calculus framework—could largely hinge on the professional career stage that learners are in. For
example, younger respondents who presently view data transmission favourably due to their quest for
career opportunities may exhibit more reluctance once they have established themselves professionally.
However, we might be witnessing a more lenient attitude towards data privacy from individuals born in
the 1990s and 2000s, as they have been granting access to their data to various entities via their mobile
devices or social networking accounts since their adolescence.
This debate echoes conversations around the cultural-historical activity theory (Roth & Lee, 2007),
which asserts that an individual’s cultural backdrop and the era in which they were born should be
factored into the examination of their psychological traits and opinions. Distinguishing between these
two competing explanations is crucial for predicting the evolution of citizens’ perceptions of data privacy
and its potential impact on future political decisions.
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Conclusion
Limitations
This case study is not exempt from conventional scrutiny concerning the practical applicability of our
results to the technological design and enhancement of the systems under consideration. Additionally,
due to the specificity of our queries, our insights cannot be effortlessly generalized beyond the MOOC
context. However, given that these courses now constitute a significant portion of the online education
environment, we assert that despite the challenges related to the external validity of our findings,
dedicated studies on ethical usage of online learners’ analytics bear inherent value. A principal
constraint of this research lies in our inability to comprehensively understand how learners perceive
information privacy and retention at large, leaving us unable to determine if their views are unique to
the educational context or not.
Perspectives
As MOOCs often host classes related to learners’ hobbies, users may view data use on these platforms
differently when compared with platforms that focus primarily on professional skills. Notably, platforms
like DataCamp (Baumer et al., 2020) offer both courses and job opportunities, suggesting that learners’
perceptions of recommender systems and data sharing with third parties like recruiters might differ
when they view these platforms as both skill-development tools and career-advancement avenues.
Finally, contrasting respondents’ viewpoints about their data use across different platforms (e.g.,
Facebook, Netflix, etc.), with the aim to determine if they adopt a more permissive attitude concerning
their data usage in the context of MOOCs, would be enlightening. How would individuals who showed
minimal hesitation about the use of their learning data react if other aspects of their lives were exposed?
Would the generational divide be as pronounced on social media platforms?
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