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Understanding the Perception and Awareness of Education
Technologies’ Privacy and Security Issues
Rakibul Hasan
Arizona State University
Tempe, Arizona, USA
rakibul.hasan@asu.edu
ABSTRACT
The rapid growth of education technologies (EdTech), accompanied
by large-scale data collection with obscure policies for use and
sharing, poses numerous privacy and security risks to users. While
policymakers and the research community struggle to catch up
with EdTech’s fast development, adequate risk awareness among
the users and other stakeholders can go a long way in ensuring
proper use and initiating movements against invasive technologies.
Past studies surveyed users to understand their risk perceptions,
but they predominantly focused on students, who may have little
understanding of potential privacy risks (e.g., young students) or
control over EdTech’s deployment. Moreover, while past studies
repeatedly identied a general concern about surveillance and data
collection, studies examining users’ perceptions of and experiences
with specic EdTech apps remain scarce.
Toward a broader and more nuanced understanding of risk per-
ception, experience, and awareness involving a larger population,
this paper reports ndings based on large-scale online datasets:
9M Twitter posts and 0.5M Reddit posts related to EdTech. We
implemented a novel pipeline to automatically identify posts re-
lated to EdTech’s privacy and security issues; overall, we detected
a low level of awareness about these issues among the examined
population. Through manual annotation of more than 7,000 tweets
and qualitative analysis of 186 Reddit posts, we surfaced concerns
people expressed about specic technologies.
Moreover, we identied several EdTech stakeholder groups (e.g.,
educators) from online proles to examine their perspectives. Most
educators cared for students’ privacy—arming past studies; but im-
portantly, we also found educators to be concerned about their own
privacy. In contrast, some educators regarded students’ concerns
for privacy as unwarranted and advocated for EdTech’s increased
use. We discovered power asymmetry and tensions between stake-
holders (e.g., instructors and administrators) regarding EdTech’s
deployment and use, which may exacerbate the risks for students.
Finally, we surfaced threats to the privacy the people around EdTech
users (e.g., family members), as many apps conduct multi-device
tracking and home network scanning. We reect on these ndings
and make recommendations for future research in this fast-growing
domain.
This work is licensed under the Creative Commons Attribu-
tion 4.0 International License. To view a copy of this license
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Proceedings on Privacy Enhancing Technologies 2023(4), 269–286
©2023 Copyright held by the owner/author(s).
https://doi.org/10.56553/popets-2023- 0110
KEYWORDS
education technology, privacy, surveillance, user perception
1 INTRODUCTION
Education technologies (EdTech) are being integrated into every
step of educational processes [
129
]. The Covid-19 pandemic im-
posed an abrupt move to virtual settings, and EdTech applications
(apps) were often deployed bypassing proper vetting processes [
26
,
44
,
60
], and without adequate IT infrastructures and training re-
sources for educators and IT personnel [
11
,
20
,
67
,
89
,
118
] to prop-
erly congure, maintain, and use those tools. EdTech’s rapidly grow-
ing market (estimated to exceed $404B by 2025 [63]) hints at their
continued and expanded use in the post-pandemic world [
44
,
75
,
92
].
EdTech apps, like most other digital tools, mine massive amounts
of user data [
1
,
46
], and analyze them for proling, evaluation, and
comparison [
58
,
129
]. Moreover, educational institutes are increas-
ingly ooading data collection, storage, and maintenance respon-
sibilities to for-prot entities [
44
,
45
] that may not be subject to
data protection laws like FERPA (Family Educational Rights and
Privacy Act) [
11
,
44
]. Consequently, the marketplace for EdTech
data is growing [
87
,
105
,
129
], and researchers and privacy activists
are expressing increasing concerns regarding the security, privacy,
and safety risks from EdTech [79, 82, 87, 129].
Are the stakeholders—students, educators, parents, school o-
cials, and developers—aware of and concerned about these risks?
Public awareness and resistance to surveillance technologies can
prevent their normalization. For example, parental opposition to
inBloom—a multi-state program to collect data from educational
institutes and share them with private companies—prevented it
from materializing [40].
Past research investigated risk perception and awareness by
EdTech users [
10
,
19
,
68
,
70
–
72
,
83
,
115
,
120
,
124
,
126
]; most of
them, however, focused only on students; a few included other
stakeholders [
70
,
71
,
83
,
115
]. Furthermore, ndings varied between
populations—ranging from students being unaware of EdTech’s
use [
19
], to being aware but unconcerned about data collection [
124
],
to demanding transparency and greater control over their data [
116
,
126
]. Such inconsistencies might have resulted from framing ef-
fects, cultural variations, and biases [
19
,
49
,
54
,
124
]. Finally, while
prior research repeatedly discovered general anxiety around data
collection, they rarely spotted specic privacy concerns from cer-
tain tools or experiences of privacy violations, presumably, because
surveying large populations or iterating over a comprehensive list
of potential risks could be prohibitively expensive.
This paper aims toward a broader and simultaneously nuanced
understanding of people’s perceptions of and experiences using
269
Proceedings on Privacy Enhancing Technologies 2023(4) Rakibul Hasan
EdTech. We complement prior survey-based research by analyz-
ing large-scale data from two popular platforms: Twitter and Red-
dit. These platforms oer diverse perspectives on current socio-
technical issues and have been used in numerous studies on privacy
and security (e.g., [
7
,
64
,
65
,
78
,
98
,
101
]). From Twitter and rele-
vant subreddits (e.g., /r/teachers,/r/students, and /r/edtechhelp), we
collected 11M tweets and 0.5M Reddit posts that contained EdTech-
related keywords; these posts were made between January 2008
and February 2022. We analyzed these data to identify posts about
the privacy and security of EdTech, as well as mapped the post
authors to EdTech stakeholders (e.g., educators) to understand risk
perception and awareness of dierent groups.
Trend analyses revealed that tweets about EdTech in general
and tweets specic to EdTech’s privacy/security issues peaked after
the Covid-19 lockdown, but the latter had a much smaller scale
than the former. This result suggests that the examined population
may lack risk awareness despite news on data breach [
35
,
95
,
131
]
and abuse [
24
,
42
,
59
,
103
] are on the rise and frequently posted
on Twitter. Although small in relative terms, we identied more
than 7,000 tweets discussing EdTech’s privacy and security issues.
Manual annotation of the tweets revealed that people expressed
generic concerns about surveillance and proling as well as specic
risks they perceived or experienced while using certain technology
(e.g., sharing video during online classes). Critically, more tweets
expressed specic concerns than generic concerns; the former also
reached a larger audience (through retweets and likes) than the
latter, especially, after the Covid-19 lockdown. We broke down the
above ndings across stakeholder groups: we found that, among
other results, academics engaged only moderately in discussing
EdTech’s potential risks and creating public awareness.
Reddit posts, compared to tweets, are more descriptive, and their
qualitative analyses oered nuanced understandings of the issues
at hand. The key discoveries include educators’ concerns regarding
self-privacy being risked by EdTech, and tensions among educators
and school administrators regarding EdTech’s deployment and use.
Additionally, we surfaced novel threats: not only students but also
their family members may experience privacy violations as EdTech
enter personal spaces (e.g. home) and conduct multi-device tracking
through stealthy behaviors; such threats may impact marginalized
populations disproportionately due to their greater reliance on
school-issued or shared devices [
55
]. Finally, we found that some
educators—who maintain that students’ aversion to using EdTech
due to privacy risks contradicts their use of social media and that
EdTech’s benets outweigh their risks—advocated for EdTech’s
increased deployment and use.
In sum, this paper provides a broader view of EdTech’s pri-
vacy/security issues as perceived and experienced by a large popula-
tion, over a 12-years timeline. Instead of solicitation, we relied on
voluntarily provided data that are less likely to be biased; we expect
the ndings to portray an accurate picture of people’s concerns and
awareness. Based on these ndings, we oer guidelines for future
research in this rapidly emerging space.
2 LITERATURE REVIEW
2.1 Data mining by EdTech
As students interact with technologies for educational purposes,
their activities are logged, mined, and analyzed for patterns. For
example, web-based learning management systems collect every
click, page transition, and contextual information (e.g., date and
time) [
58
,
89
]. Remote tutoring and proctoring tools continuously
collect audio and video data; and mobile-based apps monitor stu-
dents’ activities in and out of school campuses and in contexts
unrelated to educational activities [77, 89, 129].
2.2 Privacy and security risks from EdTech
EdTech’s massive-scale data collection is accompanied by an in-
creasing number of data breaches at educational institutes [
79
],
secondary uses of students’ data [
82
,
87
,
129
], and an expansion
of data marketplaces [
4
,
43
,
107
]. Harms from data breaches are
explicit: leaking identity and other sensitive attributes of students
have led to tax fraud, unlawful extortion of money, and even the
death of a student [
79
]. Behavioral data mining may appear less
dangerous, but proling and identifying information can be inferred
from such data [
37
,
58
]. Moreover, constant monitoring of students’
activities may have gradual, but more insidious eects of destroy-
ing independent and creative thinking capabilities [
129
]. Integrat-
ing applications from multiple vendors and third-party developers
through APIs (Application Programming Interface) [
14
,
41
,
51
] not
only subject more people to the existing risks (by scaling up data
volume and parties that can access the collected data) but also
create new risks since triangulating cross-platform data facilitates
re-identication (even if the original data were deidentied) and
proling [130]).
2.3 User awareness and risk perception
Researchers have investigated the level of awareness and concerns
about EdTech’s security and privacy issues by surveying students;
unfortunately, some ndings were inconsistent across samples.
Jones et al. reported that students lack awareness of both data
collection for learning analytics and their purposes [
72
]. Phu et al.
found students to be aware of their interactions with e-learning por-
tals being recorded but carefree [
124
]. Contrarily, many researchers
reported that students felt threatened by EdTech and wanted more
control over their data and a consenting mechanism before data
collection and analyses [
116
]. Students’ risk perception also dif-
fered based on the amount and the type of data being collected
as well as the purposes for data collection [
10
,
120
,
126
]. Very few
studies investigated other stakeholders such as educators [
70
,
71
]
and developers [
115
]; both groups generally were supportive of
better privacy for students but fell short of properly understanding
or carrying out their responsibilities to achieve that goal [
71
]. A
study based on large-scale, unsolicited opinions on EdTech from
the public could ll the gaps in the literature and resolve conicts
in ndings.
2.4 Preventive measures
Researchers have proposed guidelines for trustworthy and privacy-
respecting implementations of EdTech [
39
,
102
]. Unfortunately,
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Understanding EdTech’s Privacy and Security Issues Proceedings on Privacy Enhancing Technologies 2023(4)
contributions from the security and privacy research community
to realize those guidelines and subsequently audit them have been
scarce, except for a few studies [
13
,
18
,
52
,
53
,
58
,
85
]. Policymakers’
eorts to protect consumers’ rights and interests have also been se-
verely lacking [
11
,
44
]. A comprehensive and nuanced understand-
ing of the level of public awareness, perception, and experiences
may help propel research eorts to better inform the users and the
public, establish policies to regulate data collection and usage, and
innovate technical means to mitigate the risks.
2.5 Social sensing for privacy and security
research.
Online platforms (e.g., Twitter and Reddit) have become major
venues for discussing socio-technical issues. Twitter provides an
open platform for researchers, academics, and practitioners to dis-
cuss various aspects of research and teaching [
57
], and Reddit
oers role-specic forums for dierent EdTech stakeholders. These
platforms were instrumental in acquiring threats intelligence and
understanding public attitudes toward security/privacy issues at
scale (e.g., [
64
,
78
,
98
]). Furthermore, non-security experts also par-
ticipate online in such discussions [
114
], and critically, large-scale
public discussions have led to desirable outcomes such as enhanc-
ing product security [
62
] and banning invasive remote proctoring
apps at educational institutes [23].
3 METHODS OF TWITTER DATASET
COLLECTION AND ANALYSIS
In this paper, we take a quantitative approach and analyze large-
scale data from Twitter and Reddit (Section 5) to identify privacy
and security concerns regarding EdTech. This approach has been
adopted in numerous prior research investigating such concerns in
dierent domains (e.g., [
56
,
64
,
78
,
90
,
93
,
98
,
125
]). While interview-
based studies allow one to identify rich and nuanced content, collect-
ing data from a large sample is not usually possible in those settings.
More importantly, we view interview-based and observational stud-
ies are complementary to each other, rather than competitors: the
latter can be used to conrm and extend the ndings from the
former employing a larger sample. Since many prior studies in-
terviewed or surveyed EdTech users and stakeholders to identify
concerns, we focus on whether those ndings apply to a larger
sample including diverse stakeholder groups.
This section details Twitter data collection and analysis methods;
we combined methodological steps from past research (e.g., [
56
,
90
,
93
,
125
]) with our own to create a novel iterative process of data
analyses. Results from automated analyses were manually reviewed
and validated by the authors as well as independent annotators to
establish reliability.
We used Tweepy [
27
] to query Twitter’s public API (Applica-
tion Programming Interface) for academic research [
123
] which
allows searching for tweets posted anytime since Twitter’s incep-
tion. We compiled a list of hashtags, account names, and keywords
(described below) that were used as search terms. The collected
tweets included id, text, number of retweets and likes, and authors’
prole information. We collected original tweets, as well as quotes
and replies, but omitted retweets as they do not add information.
After collection, tweet content and prole descriptions were pre-
processed for topic clustering and sentiment analyses. We describe
these steps below.
3.1 Collecting search terms
Both Twitter handles and hashtags associated with EdTech apps
were used to search tweets. First, we listed apps from several au-
thoritative sources: the most popular 100 EdTech apps on Android
and iOS marketplaces, apps recommended by Common Sense Edu-
cation,
1
top 100 apps ranked by SimilarWeb [
6
] that has been used
in numerous prior research (e.g., [
36
,
119
]), and other popular on-
line sources (see Appendix A.1). After removing duplicates, apps
without an associated Twitter prole (e.g., Kiddopia), and apps that
were not directly used for educational purposes (e.g., Here Comes the
Bus, which is used for school bus scheduling), we were left with 45
apps and corresponding Twitter handles (full list in Appendix A.2).
Hashtags were collected from sources listed in Appendix A.1, as
well as from recommendations made by Twitter when we searched
with already collected tags and Twitter handles; we stopped search-
ing after reaching convergence. In total, we collected 54 unique
hashtags (e.g., #edtech, #eddata, and #remoteteaching), see the full
list in Appendix A.2. To further expand the set of search terms, the
collected hashtags and handles were concatenated with the follow-
ing keywords: surveillance, surveil, track, risk, vulnerable, threat,
security, privacy, student, secure, insecure, private, dataveillance,
invasion, and invasive, totaling 1,365 search terms.
Next, we collected tweets that contained any of the hashtags,
handles, or search phrases. Note that we collected tweets containing
the handles, rather than posted by the associated Twitter account;
the former includes tweets from consumers or the public and ex-
presses opinions about the apps or their developers, while the latter
includes tweets that are posted by companies and usually intended
for advertisements. In total, we collected 11,315,305 unique tweets
posted from 33,27,801 unique accounts. Only English tweets (N =
9,745,446) were analyzed in this paper.
3.2 Clustering Twitter users
To understand who discusses EdTech on Twitter, we mapped users
to stakeholder groups (e.g., educators) by clustering prole descrip-
tions, as detailed below.
Pre-processing and language detection of user proles. First,
texts describing proles were pre-processed to remove numbers,
special characters, and stopwords [
33
]. The remaining words were
lemmatized to obtain their base or dictionary form [
109
]. To deter-
mine the prole language, the pre-processed texts were fed into
three popular language detection models: Langid [
30
], Google’s
Compact Language Detector [
29
], and a fasttext pre-trained model [
73
].
The language was determined based on majority voting. We iden-
tied 1,698,096 proles in English, 142,066 in Spanish, 91,536 in
French, and 42,593 in German; 1,235,386 proles remained undeter-
mined and the rest were in other languages. Only English proles
were used in subsequent analyses. We could not determine the
language of 1,235,386 (
≈
11%) proles, either because the prole
descriptions were empty or contained only emojis or URLs; these
1
Common Sense Education is part of Common Sense Media [
3
] which is a leading
organization in reviewing and rating technologies aimed at children
271
Proceedings on Privacy Enhancing Technologies 2023(4) Rakibul Hasan
proles were discarded from further analyses. We note that empty
prole is not uncommon on Twitter; prior research has found that
more than 20% of the proles can have empty descriptions [
96
,
111
].
Clustering pre-processed proles. The pre-processed proles
were embedded in a 100-dimensional vector space where each pro-
le was represented by a point and ‘similar’ proles can be grouped
to form clusters. For the embedding, we trained fasttext [
16
] follow-
ing the skip-gram model, which places texts that are semantically
(rather than syntactically) similar next to each other, and thus sim-
ilar proles get embedded in nearby points [
17
]. We adapted Sia
et al.’s implementation of a vector clustering algorithm to cluster
the proles [113].
Since clustering is an unsupervised algorithm, the appropriate
number of clusters needs to be determined experimentally. We
used the coherence score, one of the most popular metrics [
106
], to
determine the number of clusters. First, the clustering algorithm
was executed multiple times with dierent cluster numbers (as a
parameter): 10, 20, 30, 40, 50, 80, 100, 120, and 150. Each time, the
individual cluster’s coherence score was computed by averaging
the pairwise cosine similarity among the 10 most important words
in that cluster (i.e., words representing the cluster center [
113
]).
We settled on 40 clusters since this structure achieved the highest
average coherence score of 0.69 (range: 0–1) across the specied
number of clusters [
106
]. Following cluster identication, each
prole was assigned to the cluster with which it had the highest
cosine similarity.
3.3 Identifying tweets expressing concerns
The sheer volume of online data, with only a tiny fraction of it
being related to privacy and security (e.g., .12% [
90
] to .5% [
93
] for
app reviews) makes manual analyses infeasible. Thus, following
prior works [
56
,
90
,
93
,
125
], we combined automated and manual
steps to identify and categorize relevant tweets. Figure 1 depicts
the analysis pipeline.
First, tweets were identied using keyword search (see below)
and then clustered using topic modeling. Next, we performed topic
modeling to distinguish tweets related to EdTech from other topics.
Finally, we conducted sentiment analysis and discarded tweets that
conveyed positive or neutral sentiment as they were unlikely to
express concerns. The remaining tweets were manually annotated.
The following sections detail these steps.
Keyword search. Following prior work [
90
,
93
], we identied
tweets that contained keywords related to privacy and security.
The following keywords were compiled from literature review [
56
],
news articles, and our observation of tweets: privacy,security,stu-
dent,surveillance,surveil,dataveillance,vulnerable,vulnerability,
infosec,abuse data,misuse data,aggregation,secondary use,intrude,
intrusion,appropriation,appropriate, and specic purpose.
Tweet topic clustering. Tweets were pre-processed by tokenizing
(using an open-source library [
117
]), replacing emoticons with cor-
responding words [
32
], converting contractions to full forms [
31
],
removing special characters and stopwords, and lemmatizing the
remaining words. Next, tweets were converted into vectors using
fasttext [
16
] and then clustered [
113
]. As before, we experimented
Collected
tweets
Identify tweets
containing
keywords
related to
EdTech’s privacy
and security
Cluster tweets
topics and
manual
assessment
review identified
clusters
Remove tweets
with topics
unrelated to
security/privacy
after manual
review
Manual coding
of 500 randomly
selected tweets
to create
themes
Sentiment
analysis of the
remaining
tweets
Taxonomy of
security and
privacy
concerns in
tweets
Manual labeling
of the negative
tweets using the
previously-
created themes
Compare with labels assigned by
three independent annotators
Compare with results from three
independent annotators
Figure 1: Steps to create a taxonomy of privacy and security
concerns about EdTech from the tweet dataset, and evaluate
it with randomly sampled data labeled by independent an-
notators.
with dierent cluster numbers: 5 8 10 15 20 25 30, and identied 10
clusters based on the coherence score (0.71).
Manual assessment of the clusters. To evaluate the cluster qual-
ity, we reviewed 10 representative words in each cluster. As Table 1
shows, clusters 3, 8, and 10 were dicult to interpret from the
words alone without more context. Hence, we manually reviewed
a random sample of 50 tweets from each cluster, a total of 500 (all
tweets in clusters 7, 9, and 10 were included as they contain less
than 50 tweets).
This review step served two additional purposes: it helped iden-
tify clusters irrelevant to our research and served as a basis for
creating a taxonomy of concerns that we later used to label all
tweets (Section 3.4). Tweets in clusters 5, 7, 8, and 9 were removed
from further analyses; almost all tweets in cluster 5 were related to
security/privacy risks in Bring Your Own Device paradigm (#BYOD)
in employment contexts, tweets in cluster 7 were related to two in-
cidents involving Facebook [
38
,
91
], and clusters 8 and 9 contained
tweets on random topics.
Sentiment analysis. We trained a supervised machine learning
model for sentiment analysis using two datasets: the open dataset [
88
]
containing 6.3 million tweets labeled as positive,negative, or neutral
by the AWS Comprehend API [
8
], and the SemEval dataset [
104
]
containing 50,000 tweets labeled similarly by crowd workers. The
combined dataset was divided into training (80%) and evaluation
(20%) splits. We trained a fasttext (multi-class) classication model
on the training split. To improve the classication accuracy, we used
bi-grams from pre-processed tweet texts (e.g., to correctly interpret
phrases like “not happy”). The model yielded 91.3% classication ac-
curacy on the test split which is higher than a state-of-the-art model
trained only on the SemEval dataset [
25
] (we experimented with
a popular lexical-based model named Vader [
66
], but it performed
worse in the test split.)
The trained model was used to infer the sentiment of tweets
in our dataset. Only tweets predicted as negative were used in
subsequent analyses as positive and neutral tweets were unlikely
to express concerns or risks.
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Understanding EdTech’s Privacy and Security Issues Proceedings on Privacy Enhancing Technologies 2023(4)
Cluster #Tweets Keywords
1 12,735
edtech, cybersecurity, dr, skillsgap, nlproc, ingramtechsol, systemsthinke, stcenturyskill, thecybersecurity-
place, cybint
2 1,205
datum, protect, sotu, schoolprivacyzone, paigekowalski, datadrivesresult, rachelbarrer, takectrl, replukemesser,
benjaminbherold
3 6,447 username, share, talk, story, daily, event, awesome, tune, david, advisor
4 702
school, lifeleader, schoolchildren, safeschool, studentvoice, chooseprivacy, schooldistrict, coetail, powerschool,
indyschool
5 37,108
security, byod, mobile, device, enterprisesecurity, byodsecurity, ercemobileit, securityrisk, dellsecurity, syman-
tec
6 104,699 privacy, student, news, washington, priva, myname, privac, stud, studentright, priv
7 48 facebook, internship, aw, austrian, lose, harvard, expose, highlight, messenger, cancel
8 699 conclude, cpdp, myriad, mineeld, txcto, asugsv, sotn, bettertogether, nuisance, rundown
9 3 neighborhood, georgetown, auburn, nazi, chattanooga, tulsa, knife, sixth, uconn, franklin
10 12 gauge, pseudonym, tyranny, culprit, sarcasm, ludicrous, nefarious, pedophile, disingenuous, fwiw
Table 1: The number of tweets and most representative 10 words in each cluster.
3.4 Manual annotation of tweets expressing
concerns.
This section details our process of categorizing the tweets identied
in the previous step.
Creating categories. During our review of 500 tweets (Section 3.3),
we also categorized their content type. For example, some tweets
expressed general privacy concerns and fear of data abuse, while
others mentioned specic concerns (e.g., visual data privacy during
a virtual meeting) or risks from a specic technology. Many tweets
discussed privacy/security incidents (such as a data breach) and
(the lack of) laws or policies to regulate EdTech. Table 2 lists these
categories.
Applying the categories. In this step, we applied the categories
to all tweets expressing concerns and simultaneously validated this
taxonomy of concerns. But, instead of crowdsourcing, we decided to
label the full dataset ourselves based on the following observations.
While labeling the rst 500 tweets, we noted that many tweets
could not be fully interpreted from the text alone as they may con-
tain only URLs, news headlines, or replies to another tweet, and
required visiting Twitter to gather contextual information. More-
over, many tweets referred to specic EdTech providers by Twitter
handle contained technical terms (e.g., bias in AI-based remote proc-
toring apps), or referred to specic people (e.g., a privacy researcher
or activist) that are likely to be unfamiliar to crowd workers. Fur-
thermore, replies to other tweets required us to read the whole
tweet thread to properly categorize them. Finally, many tweets
were duplicates of others (e.g., a news article shared by many);
we had to scan all tweets multiple times to spot such duplicates.
Thus, crowdsourcing the labeling task in our case could produce
unreliable results.
Note that the categories were created and applied based on the
presence of certain entities (e.g., Twitter handles, links to news
articles, and technical phrases referring to privacy or security vul-
nerabilities), and thus unlikely to be inuenced by subjective biases.
Four tweets did not t any of the categories; hence, we created a
new category to label those: tech support (Table 2). All other tweets
Figure 2: The trend in English tweets related to EdTech.
were annotated by the previously created categories, demonstrat-
ing the comprehensiveness of the taxonomy. To establish further
reliability on the results, we compared our annotations with anno-
tations created by independent annotators on random tweet subsets
(Section 4.5).
4 FINDINGS
We collected 11,315,305 tweets from 3,327,801 users; 9,745,446 (86%)
were in English (from 1,698,096 users). Figure 2 plots the tweet
trend: EdTech-related discussions peaked around mid-2020 once
educational institutes went remote. The following sections report
ndings based only on English tweets and the users who posted
them.
4.1 User groups discussing EdTech on Twitter
As Section 3.2 explained, we identied 40 prole clusters, manually
reviewed them, and merged similar clusters, resulting in seven
larger groups that resembled EdTech stakeholders (Table 3). We
note the semantic similarities among the words representing each
group; the non-words were hashtags or Twitter handles for relevant
entities (e.g., universities or research organizations, we conrmed
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Label Explanation
Privacy generic Tweets expressing concerns around EdTech’s surveillance, data collection, and misuse.
Privacy specic Tweets referring to privacy/security concerns or experiences specic to a context or a technology.
Privacy incidents Tweets discussing privacy-violating incidents such as data breaches.
Policy Tweets discussing the applicability or (in)adequacy of laws and policies to protect EdTech consumers.
Article
Tweets sharing a news or journal article related to EdTech. The most common article topics included security
and privacy challenges posed by EdTech and the current status of laws and policies for consumers’ protection.
Other student privacy Tweets discussing students’ privacy issues or concerns but not in the context of EdTech.
Other privacy Tweets that discussed generic security or privacy concerns and issues with other technologies.
Info branding
Tweets advertising online courses, tutorials, or other resources that may help mitigate EdTech’s privacy/security
issues. These tweets were usually posted by the creator of the advertised items.
Tech branding
Tweets advertising software products created to prevent security/privacy risks in the context of using EdTech.
Exchanging knowledge Tweets posting questions (e.g., how to congure some app), discussion prompts, or tips related to EdTech.
EdTech generic Tweets about generic questions, discussions, or complaints regarding education apps.
EdTech support Tweets advocating technologies’ deployment or usage in education.
Other Tweets that were unrelated to education technologies or security/privacy threats from technology.
Table 2: Tweet categories and their explanations.
this by manual searches) and thus represented contextually related
information.2
Surprisingly, we did not nd any cluster representing students.
The Academic techie group contained “student” among the top 10
words; we manually reviewed 100 proles randomly selected from
this group, but only one of them was a student prole; the rest in-
cluded the word “student” in contexts other than group identity (e.g.,
“love my students”). Similar searches based on keywords that could
be present in students’ proles (e.g., “learner” and “researcher”)
yielded only 0–2% of proles belonging to students. Thus, we did
not create a separate group for students.
4.1.1 The number of tweets across user groups. Academic techie
posted the highest number of EdTech-related tweets (1,791,852),
followed by Personal brand (1,144,087). Inuencer,Academic,News,
Personal, and Techie posted 948,885, 723,787, 213,018, 161,053, and
141,259 tweets, respectively. Based on the keywords describing
these groups, people who build software and analytical systems
(possibly including EdTech) and advocate technologies’ integra-
tion in pedagogical processes seem to be the most vocal on Twitter
regarding EdTech. Relatively fewer tweets were from advertising ac-
counts (likely maintained by online inuencers [
57
,
108
]); this result
is unsurprising since we excluded tweets from EdTech providers’
ocial proles.
4.2 Tweets containing privacy or
security-related keywords.
We identied 216,667 tweets, posted from 90,579 unique accounts,
that contained at least one keyword related to privacy or security.
Twitter’s terms of service [
122
] prohibit the use of tweets posted
from accounts that were later deleted. We identied 53,009 tweets
that were posted by such accounts (N=26,772), i.e., the accounts
do not exist anymore; thus, we discarded those tweets. Among
the remaining 163,658 tweets, 108,666 tweets were posted by the
user groups identied above. Table 4 shows the number of tweets
2Appendix A.3 lists all 40 clusters separately.
containing privacy/security-related keywords along with the ratio
of such tweets to the total number of tweets from each group. In the
following sections, we report how many of these tweets actually
expressed concerns related to EdTech’s privacy and security issues,
break it down across user groups, and identify EdTech apps that
were most frequently mentioned in those tweets.
4.3 Tweets related to EdTech’s privacy and
security.
4.3.1 Identifying and labeling tweets expressing concerns. Senti-
ment analysis identied 7,309 tweets with negative, 148,082 with
neutral, and 8,267 with positive sentiment. We manually categorized
the concerns expressed by the negative tweets.
3
As Table 5 shows,
4,281 tweets were assigned to dierent concern categories. The rest
3,028 tweets were duplicates of other tweets. For example, variants
of the news regarding the Electronic Frontier Foundation accusing
Google of tracking students [
103
], and students being suspended
for refusing to wear a school-issued RFID tracker [
80
] appeared in
our dataset more than 400 times. Notably, more tweets mentioned
specic risks or expressed concerns regarding certain technology
than tweets expressing general concerns, and four tweets advo-
cated EdTech’s increased use. Also note that only 424 (
<
6%) tweets
were false positives (i.e., unrelated to EdTech’s privacy/security),
demonstrating the reliability of our automated selection method.
4.3.2 Most important keywords and entities that were associated
with the identified concerns. Table 6 shows the most important 25
keywords and entities (based on Term Frequency Inverse Document
Frequency or TF-IDF score [
34
]) that occurred in tweets expressing
either generic or specic privacy concerns, or common to both. Note
that many tweets in the latter group referred to specic privacy
violations or invasive behaviors by certain technologies, and men-
tioned (i.e. contained Twitter handles) 129 unique EdTech providers
3
As discussed in Section 3.3, we did not include tweets with positive or neutral
sentiment since they are less likely to include any concerns about privacy issues or
complaints about related experiences.
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Group name and description Keywords representing the group #Users
Academic: proles emphasizing educa-
tor roles in research or teaching insti-
tutes.
school, principal, research, researcher, researching, lunduniversity, researched,
keeleuniversity, reseach, stfxuniversity, universitysa, simaduniversity, teacher,
learner, paraeducator, director, professor, assistant, associate.
195,762
Academic techie: educator proles ad-
vocating EdTechs’ adoption in pedagog-
ical processes or showcasing related cer-
tications.
education, technology, learn, tech, passionate, student, techology, msuepet, spe-
cialist, certied, trainer, integrationist, nearpodcertied, classcraft, certiedmie,
edtechie, educator, edugator.
133,749
Techie: proles describing computer
science, engineering, and related pro-
fessions.
software, engineer, developer, programmer, developper, coldfusion, softwares,
getpostman, klarna, couchbase, data, scientist, datum, analytics, analytic, visu-
alization, analysts, datavisualization, boozallen, linkeddata.
54,057
Personal brand: proles claiming lead-
ing positions or expertise in tech or busi-
ness elds.
founder, entrepreneur, entreprise, dealmaker, wantrepreneur, angellist, capi-
talfactory, businessperson, lead, professional, provide, development, training,
focus, expert, develop, create, inspire, empower, mission, strive.
164,168
Influencer: proles maintained
by celebrities, inuencers, or en-
trepreneurs and may be used for
product endorsements.
online, website, blogspot, prospectus, service, company, solution, multinational,
enabling, follow, twitter, account, ocial, instagram, facebook, business, social,
digital, market, strategist
191,884
News: proles describing journalist or
editorial professions.
editor, journalist, vethirtyeight, primetime, journaliste, wlrn, journalistin,
geekwire, journos, thismorne, news, information, event, bring, late, source,
daily, issue, cover, relate.
43,134
Personal: proles highlighting per-
sonal relationships.
husband, father, proud, christian, amazing, awesome, marry, married, wonder-
ful, lucky, wife, mother, friend, daughter, sister, mama, mommy, grandmother,
momma, grandma.
68,667
Table 3: Prole groups after clustering and merging, 20 most important keywords, and number of Prole in each group.
User group #Tweets % total % group
Academic 8,197 7.5% 1.13%
Academic techie 19,170 17.5% 1.07%
Inuencer 44,097 40.5% 4.65%
News 12,314 11.3% 5.78%
Personal brand 20,846 19.2% 1.82%
Techie 4,042 3.7% 2.86%
Personal 2,254 2.1% 1.4%
Table 4: The number of tweets related to privacy and secu-
rity across user groups (and the percentage of such tweets
relative to the total number of tweets and tweets posted by
the respective group).
(top ve: goguardian, chegg, examsoft, edpuzzle, and pearsonvue).
In contrast, tweets expressing generic concerns mentioned only 32
unique EdTech providers. Most tweets with mentions complained
about privacy threats experienced while using the mentioned ap-
plication.
Above results not only conrm past studies [
9
,
71
] (reporting
concerns about invasive data collection and vague privacy policies)
but also extended those studies by identifying EdTech apps and
providers that users perceived as the most threatening.
4.3.3 Which stakeholder group expressed what concerns? Table 7
breaks down the number of tweets expressing concerns by each
Label Count Label Count
Other student privacy 864 Privacy incident 190
Privacy specic 725 Info brand 131
Other privacy 574 Tech branding 86
Article 494 EdTech generic 76
Other 424 Knowledge exchange 45
Privacy generic 406 EdTech support 4
Policy 262
Table 5: Distribution of tweets related to EdTech’s privacy
and security concerns.
stakeholder group. Interestingly, the group with the highest enthusi-
asm for EdTech, Academic techie, also expressed the most concerns.
Another surprising nding is that Twitter proles for self- or busi-
ness advertisements posted reasonably large proportions of such
tweets. To examine any group-specic peculiarities in discussion
topics, we report the top 10 words (according to TF-IDF score)
from the tweets in Table 7. Most notably, the Personal user group
went beyond discussing privacy risks from EdTech’s surveillance
to users’ expectations and EdTech providers’ accountability.
4.4 Trend and reach of tweets expressing
concerns.
To understand the discussion trends on EdTech’s privacy issues,
incidents, and regulatory policies, we plotted the average number
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Proceedings on Privacy Enhancing Technologies 2023(4) Rakibul Hasan
Category Most frequent keywords
Common
username, privacy, student, edtech, datum, surveillance, school, issue, teacher, concern, app, kid, security, bad, online
Generic eddata, parent, education, protect, child, tool, company, time, learn, information
Specic software, invasion, violation, google, exam, policy, zoom, violate, proctorio, feel
Table 6: Most important keywords and entities in tweets that expressed (generic or specic) privacy concerns.
Label Author group Count Most important keywords
privacy-generic Academic 33 privacy, student, edtech, surveillance, datum, law, time, platform, issue, security.
Academic techie 86 privacy, student, edtech, datum, school, surveillance, issue, app, teacher, kid.
Inuencer 29
privacy, student, datum, school, edtech, byod, surveillance, onlinesafety, security, fear.
News 7 student, mooc, loss, major, drawback, blow, surveillance, people, rep, appropriately.
Personal brand 25 privacy, student, datum, edtech, eddata, teacher, concern, school, issue, sxswedu.
Techie 1 (omitted due to the low number of tweets.)
Personal 6
student, expectation, accountability, understand, secure, threaten, pjnet, keepyour-
promise, intrusive, stopcommoncore.
privacy-specic Academic 80 student, privacy, issue, datum, surveillance, concern, school, violation, teacher, tool.
Academic techie 125 privacy, student, edtech, school, issue, security, google, surveillance, app, datum.
Inuencer 44 privacy, student, school, facebook, issue, security, concern, kid, reliance, danger.
Personal brand 34
privacy, student, edtech, datum, surveillance, breach, security, school, issue, software.
News 16
student, privacy, surveillance, issue, online, username, exam, unfair, global, spark,
software.
Techie 21
security, privacy, exam, student, delete, business, fail, information, account, company.
Personal 13 student, violate, edtech, zoom, time, assumption, guilt, right, danger, invasion.
Table 7: Tweets expressing privacy concerns and top 10 keywords in those tweets across user groups.
Figure 3: The trend of tweets related to EdTech’s pri-
vacy/security issues.
of tweets on those topics posted every three months (Figure 3).
Importantly, tweets mentioning specic risks started to increase in
number after March 2020 (as the lockdown started due to Covid-19),
and peaked at the beginning of 2021, when most educational institutes
had already gone fully remote. Tweets expressing generic concerns
followed similar patterns but on a smaller scale. Discussions around
regulating EdTech and laws to protect students’ privacy, and tweets
mentioning privacy incidents experienced occasional small bumps,
but seem to be uncorrelated to the recent surge in EdTech’s de-
ployment. The number of tweets sharing news articles slightly
correlated with the recent surge but had larger spikes in the past.
Next, we looked at whether such discussions were limited to a
small group of people. The tweets were posted from 3,577 unique
accounts. Only 21 accounts posted more than 10 tweets, and eight
accounts posted more than 20 tweets. The maximum number of
tweets from a single account was 76, and 75% of the accounts posted
only one tweet. Thus, user participation was fairly broad and a few
accounts did not dominate discussions.
Finally, we investigated whether such discussions drew attention
from the audience (and potentially raised awareness among the
public). On average tweets expressing concerns received 4.83 likes
and 2.02 retweets—two ways content gets propagated to a new
audience on Twitter.
4
Figure 4 shows the number of retweets and
likes received by tweets of dierent categories aggregated over
three months. Attention to tweets that expressed specic concerns
increased starting from 2020 and peaked near the end of 2021.
Surprisingly, generic concerns, while tweeted more frequently after
the pandemic began than before (Figure 3), did not receive much
attention from the public.5
4
For comparison, for the full set of English EdTech-related tweets, the average
number of likes and retweets were 2.19 and 0.84, respectively. For general tweets,
retweets usually follow an exponential decay [
69
] where only 4% of tweets get more
than 50 retweets; in previous work, we found that average retweets can range from
3.5–9 based on the user [57]
5
Due to indexing and ranking, results from APIs do not always match with
manual searches (see https://twittercommunity.com/t/tweets-missing-from-twitter-
api-v2-recent-search/179031. Thus we might have missed some highly popular tweets.
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Understanding EdTech’s Privacy and Security Issues Proceedings on Privacy Enhancing Technologies 2023(4)
Figure 4: Visibility of tweets expressing concerns and inci-
dents related to EdTech. The size of the dots corresponds to
the average number of likes.
4.5 Comparing results with data labeled by
independent annotators.
To establish further reliability of our ndings, we hired three under-
graduate students (majoring in computer science) as independent
annotators. They were paid at a rate of $14.5 per hour following
our institutional guidelines.
4.5.1 Labeling privacy/security-related tweets. The rst task was
to identify privacy/security-related tweets from a random sample
of 3,000 English tweets. The annotators were briefed about the task
and shown some positive and negative examples. They then inde-
pendently labeled the tweets with high consistency (Krippendor’s
𝛼=0.93),6conicts were resolved by majority voting.
The annotators identied 47 (1.6%) tweets related to EdTech’s
privacy/security issues, while our semi-automated analysis identi-
ed 2.4% (3,857 out of 163,658) such tweets. Importantly, 43 of the
47 tweets (91.2%) overlapped with the tweets we identied. The
seeming paradox of annotators identifying positive samples at a
lower rate than the semi-automated system could be a result of too
many false positives in the latter case or annotators’ unfamiliarity
with technical or domain-specic hashtags and keywords, or both.
We test this in the next study.
4.5.2 Labeling samples identified by the automated approach. To
investigate the precision of our automated analysis mechanism, we
conducted another study to estimate the number of false positives
it generated. We randomly sampled 1,000 tweets from the set of
3,857 tweets identied as positive by this method. As before, we
hired three undergraduate students (dierent than the previous
study) to annotate these tweets. We found that, for 912 tweets, at
least one annotator agreed with the categorization (i.e., labeled
them as positive samples), and all three annotators agreed with our
classication for 784 tweets. At least two annotators agreed with
our classication for 852 tweets; Thus, a majority voting-based
scheme indicates 1.48% false positives in our dataset. We inspected
the tweets for which annotators diered with us in classication;
6
We used Krippendor’s alpha [
81
] as a measure of inter-rater reliability, as it is
more suitable for textual content analyses and more generalized than other measures
(such as Fleiss’ kappa and Cohen’s kappa) in terms of the number of coders and
categories.
While we found no strong pattern, tweets including special key-
words, hashtags, or Twitter handles, and tweets about the security
of BYOD paradigm in school, tweets including URLs to news arti-
cles that are not fully comprehensible from the title (such as the
collaboration between Facebook and Udacity to train models in
privacy-preserving ways and how deploying WiFi 6 protocol in
schools may protect privacy) were most prevalent. We note that
in general identication rate is less than 1% when crowd workers
labeled such data [
56
,
90
,
93
,
125
]. In our study, we employed under-
graduate students majoring in computer science, who presumably
are more knowledgeable in the subject matter than crowd work-
ers, and indeed achieved a higher detection rate (1.6% as reported
above). Extrapolating this observation, we infer that the false posi-
tives in this study also resulted from a lack of domain knowledge
and technical terms relevant to the context, since we used such
terms in the keyword-based search phase to identify tweets express-
ing privacy concerns. Additionally, false positives can be ltered
out with manual reviews and preferred to false negatives; Thus,
our semi-automated analysis pipeline proves to be a valuable one
in this domain.
4.5.3 Categorizing concerns expressed in tweets. We randomly se-
lected 100 tweets from each of the following categories: privacy
generic,privacy specic,privacy incidents,policy, and article (Ta-
ble 2), totaling 500 tweets. The annotators, after familiarizing them-
selves with the categories and seeing two examples from each cate-
gory, independently labeled these tweets. Conicts were resolved
through discussion and majority voting. For 462 tweets (92.4%), the
annotators agreed with our labels, again demonstrating the high
reliability of our results. Looking at the tweets with mismatched
labeling, we found that most of them fell into specic privacy and
policy categories and contained keywords or hashtags that are not
widely used in general discussions.
4.5.4 Profile groups. The clustering of Twitter proles was simi-
larly validated. The same annotators independently assigned one
of the seven groups (Table 3) to 700 randomly selected proles
(100 per group). Their grouping matched with ours for 639 (91.3%)
proles, demonstrating the correctness of our cluster identication
and assignment methods.
4.6 Summary of ndings
Our ndings reveal the trend of EdTech-related discussions over 12
years. Comparing Figure 2 with Figure 3 reveals that both generic
and privacy/security-related discussions followed similar temporal
patterns—they peaked after the Covid-19 lockdown—but the latter
was discussed at a much smaller scale than the former, indicating a
low level of risk awareness among the public despite popular outlets
are publishing news on data breach [
35
,
95
,
131
] and abuse [
24
,
42
,
59, 103] at an increasing rate.
Manual categorization of tweets revealed that specic concerns
were expressed more frequently than generic concerns, especially af-
ter the lockdown. Importantly, we uncovered concerns with specic
technologies, such ndings are generally dicult to obtain from
surveys and interview-based studies due to resource constraints .
As Table 6 shows, surveillance, data collection, and privacy risks
to students were the most frequently discussed topics in tweets
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Proceedings on Privacy Enhancing Technologies 2023(4) Rakibul Hasan
expressing both generic and specic concerns; tweets in the lat-
ter category also mentioned risks experienced in a given context
or from a specic technology. Frequent keywords were common
across user groups (Table 7). Only the Personal group that likely
included parents and other guardians touched on concepts such as
students’ expectations and privacy rights, and EdTech providers’
accountability.
Interestingly, while Zoom appeared among the most frequently
mentioned words, we did not nd a single mention of Zoom bomb-
ing [
127
], which was a hot topic in 2020 on Twitter and other
platforms [
86
]. A manual search of related keywords (e.g., Zoom
bombing,Zoom_bombing, and Zoom_bomb) on Twitter revealed that
tweets containing those terms, as well as Twitter accounts (such as
zoom_bombing) that were, presumably, being used to share Zoom
meeting IDs for bombing attacks [
86
], were removed. While exclud-
ing such tweets may aect ndings related to discussions on Zoom
security, we note that our focus was identifying a broader set of
concerns, rather than a few topics that went viral.
Mapping the Twitter proles into potential EdTech stakehold-
ers, we found limited engagement from academics in discussing
EdTech’s privacy and security. Techies, who are likely to possess
the most technical knowledge, touched on those topics in most
of their tweets (Table 4), but rarely expressed concerns about the
potential risks (Table 7). Interestingly, the most EdTech enthusiastic
group, Academic techies, expressed the most concerns related to
EdTech (Table 7).
5 METHODS OF REDDIT DATASET
COLLECTION AND ANALYSIS
5.1 Subreddit selection and data collection
To identify subreddits related to our research, we conducted web
searches and consulted Reddit posts listing education-related sub-
reddits [
28
]. Additional subreddits were identied from Reddit’s rec-
ommendations. In total, we identied eight subreddits related to ed-
ucation: /r/education,/r/edtechhelp,/r/higheredsysadmin,/r/students,
/r/professors,/r/k12sysadmin,/r/teaching, and /r/teachers. Using the
PMAW [
97
] wrapper around the Pushshift API [
12
], we collected
all posts and comments that were submitted on those subreddits
anytime before February 2022.
5.2 Identifying EdTech-related posts and their
manual labeling
Similar to the Twitter dataset, we searched for EdTech-related terms
(i.e., names of EdTech providers, tags, and phrases) in the title and
body of the posts; this step yielded 3,792 posts containing at least
one keyword. These posts were then ltered using the same set of
privacy and security-specic keywords as for tweets (Section 3.3).
Only 186 posts contained one or more of those keywords in their
title or body. The sentiment analysis step was omitted as the number
of posts for manual analyses was much smaller than the Twitter
dataset. These 186 posts were manually coded using the labels
created in section 3.4. Additionally, since discussions on Reddit were
more detailed and nuanced than tweets, we performed qualitative
analyses on this dataset (Section 6.3).
Label # Posts in subreddits
Privacy specic Teachers: 18, k12sysadmin: 10, Professors: 9
Exchanging tips Teachers: 10, k12sysadmin: 9, Professors: 3
Privacy generic Professors: 1, Teachers: 2, k12sysadmin: 1
EdTech support Professors: 2, Teachers: 3
EdTech generic Professors: 1, Teachers: 1
Other privacy k12sysadmin: 3
Privacy incident Professors: 1
Tech branding Professors: 1, k12sysadmin: 1
Table 8: The number of posts in each concern category.
6 FINDINGS FROM THE REDDIT DATASET
In total, we collected 545,502 Reddit posts from the seven subred-
dits (/r/education: 334581, /r/teachers: 165573, /r/professors: 23666,
/r/k12sysadmin: 12659, /r/student: 8906, /r/higheredsysadmin: 97,
/r/edtechhelp: 20).
6.1 Statistics of posts related to EdTech’s
privacy and security
Initially, we identied 3,792 posts that contained EdTech-related
terms. Among these posts, at least one keyword related to secu-
rity/privacy was found in 186 posts on the following subreddits:
/r/teachers: 81, /r/professors: 45, /r/k12sysadmin: 43, /r/education: 10,
/r/student: 2, /r/higheredsysadmin: 0, and /r/edtechhelp: 0.
6.2 Distribution of types of concerns expressed
We manually reviewed and assigned 83 posts under the concern
categories. Table 8 breaks down this number into categories and
subreddits, and the following section provides qualitative insights
from these posts. Notably, similar to Twitter data, we found more
posts mentioning specic threats than general concerns. The most im-
portant 20 keywords were: zoom, students, school, privacy, google,
online, goguardian, teacher, student, meeting, class, security, policy,
kids, camera, feel, proctoru, access, teachers, issues, video, peo-
ple, classroom, app, parents. This list greatly overlaps with the
top words in tweets for both generic words (e.g., security, privacy,
and student) as well as EdTech providers (e.g., Google, Proctoru,
and Zoom). Unlike Twitter data, a relatively large number of posts
shared detailed tips on properly conguring and using EdTech
to avoid security, privacy, and legal issues; the following section
provides qualitative insights from these analyses.
6.3 Qualitative ndings
While fewer posts discussed EdTech on Reddit compared to Twitter,
they were more detailed and thus provided richer information about
people’s attitudes and concerns. We discuss the major patterns we
observed below from dierent stakeholders (e.g., educators and
system administrators). The stakeholder groups were identied by
manually reviewing the prole descriptions and the posts (e.g., “...in
the class I teach...”), and based on membership in relevant subreddits
(e.g., /r/teachers,/r/professors, and /r/sysadmin).
Educators generally cared about students’ privacy, but not
everyone. Educators discussed EdTech’s privacy implications for
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their students. They expressed anxiety about improperly congur-
ing remote teaching applications and wanted access to “premium”
versions of the software that were considered to oer enhanced se-
curity and compliance with relevant policies (e.g., COPPA). Several
instructors pointed out the lack of preparation and administrative
support for remote teaching. An instructor was concerned about
tracking students across devices when they used their home com-
puters to remotely attend classes. Additionally, concerns about
surveillance and censorship surfaced when using district-owned
devices and federally monitored networks.
In contrast, ve posts advocated increased use of technology in
education and complained about the backlash against EdTech from
students and their parents. They reasoned that EdTech’s benets
outweigh their risks, and students’ unwillingness to use EdTech
due to privacy concerns contradicts their use of social media.
Educators also expressed worries about self-privacy. Eight
posts from educators expressed concerns about their own privacy
being violated. A faculty member of a higher education institute
remarked that many other educators felt the “chilling eect” as
EdTech monitor and log their activities, which can be reviewed by
authorities. Two educators felt discomfort being recorded during
remote teaching from their homes and other private spaces. Two
posts discussed a recent incident where an instructor was publicly
mocked after a recorded lecture was leaked on TikTok [
5
], a social
media platform.
Tension between educators and administrations. One teacher
mentioned that, on two occasions, their school administration asked
them to use remote teaching applications in a way that may violate
terms of service. The said teacher did not feel comfortable with
the administration’s assurance of taking the liability should any
problem arise as the administration refused to provide a written
statement.
Another senior faculty member revealed that they were given full
access to all data collected by their institute’s learning management
system and such “backdoor” access was given to all high-level
administrators without faculty members’ knowledge. They also
condemned the lack of institutional policy or guidance about such
access privileges, as it was unclear who should take responsibility
if problems arise.
Surfacing novel privacy threats. One educator pointed out that
some apps may automatically install (browser) extensions even
when students use personal devices to log into school accounts.
Such stealthy behaviors not only enable device-wide tracking of
students’ activities but also risk the privacy of other people who
share those devices (e.g., family members). A student posted their
observation that GoGuardian [
50
] was ltering their internet activ-
ities even at home while using school-issued Chromebooks. One
educator complained about other teachers’ online sharing of photos
taken during virtual meetings with students; such photos could
facilitate online harassment.
Reddit: a medium to discuss security vulnerabilities and
share tips to avoid them. Several posts in /r/teachers,/r/professors,
and /r/k12sysadmin discussed apps’ vulnerabilities and shared tips
to avoid them in detail. Two posts explained how Zoom bombing
Figure 5: The trend of engagement with privacy and security-
related posts on Reddit. The dot size correlates with the av-
erage number of comments or replies to those posts.
works and what precautions may help prevent it. Three posts cau-
tioned fellow educators about using Zoom and laid out long lists
of preparatory steps before deploying and using Zoom. Four posts
compared two or more alternative applications in terms of security
and privacy protection they oer. While such discussions may help
navigate times of crisis, risks of wrong or misleading information
remain [
101
]. Moreover, spammers or advertisers may hijack those
discussion threads; future research could investigate these issues.
6.4 Trends and popularity of posts related to
privacy concerns
Similar to Twitter, we wanted to understand the number and popu-
larity of posts related to EdTech’s privacy and security issues on
Reddit. Figure 5 shows the average number of posts (aggregated
across all categories) in every six months and the average number
of comments on those posts (indicating other users’ participation
in those discussions). Similar to Twitter, the number of posts ex-
pressing concerns peaked during mid-2020. Unsurprisingly, those
posts had a larger number of comments on average compared to
pre-pandemic posts.
6.5 Summary of ndings.
Overall, we found that students, instructors, and system adminis-
trators were concerned about EdTech’s privacy and security impli-
cations, conrming prior research examining educators’ attitudes
toward EdTech [
71
]. Our analysis, however, also identied posts
from educators arguing for EdTech’s increased use; we identied
similar content on Twitter as well (Section 4.3.1). Such ndings
demonstrate the suitability of studying unsolicited expressions to
discover dierent opinions. Additionally, we uncovered instructors’
concerns regarding self-privacy, new privacy threats from using
school-issued devices or monitored networks, and EdTech’s sneaky
behaviors, as well as tensions between instructors and other school
ocials.
7 LIMITATIONS
Similar to any research based on online data, our ndings may not
fully reect the public perception of EdTech, since the online sam-
ples, even though taken from two of the most popular platforms,
may dier from the rest of the population in their experience with
279
Proceedings on Privacy Enhancing Technologies 2023(4) Rakibul Hasan
EdTech and its reporting. The ndings also depended on several
automated analysis steps (e.g., keyword search, clustering, and sen-
timent analysis). While these steps were evaluated through manual
reviews, we may still have missed some data since all relevant posts
may not contain the keywords we searched with, and sentiment
analysis is not 100% accurate. Therefore, our results set a lower
bound on the privacy and security concerns expressed on Twitter
and Reddit.
Another limitation of our method is that the stakeholder groups
were identied based on prole information on Twitter, titles and
descriptions of subreddits, and employment status reported on the
posts. While we manually reviewed the identied groups, we cannot
verify the post authors’ real-world occupation. Thus, the results
should be interpreted with caution.
A key EdTech stakeholder group, students, was underrepresented
in our datasets. There are many possible reasons underlying their
absence in the dataset. For example, both platforms have a minimum
age requirement of 13 [
100
,
121
] that excludes a large number of
students, students who use these platforms may not reveal that they
are students in their prole or may feel discomfort in expressing
their views online where it could be monitored by school authori-
ties [
48
], and so on. While our ndings may not generalize to this
population, they complement prior studies where the focus was
mostly on students and other stakeholders were absent.
Finally, our data do not allow us to dierentiate between EdTech’s
use in K-12 or higher educational institutes. These two settings may
dier in the apps they use, the types and levels of their concerns,
and the technical and nancial resources they can leverage to tackle
anticipated risks.
8 ETHICAL CONCERNS
Our data collection and analysis procedure was reviewed and ap-
proved by our institutional ethics board. We took additional mea-
sures to protect data subjects’ privacy and safety since our datasets
may contain information about members of vulnerable popula-
tions. These measures aimed at maximizing the potential benets
and minimizing harms, according to the principle of benecence
in the widely followed ethical standards detailed in the Belmont
Report [
15
]. Before data collection, we reected on the potential
benets of this research to society, and in particular to the student
population, including minors and marginalized groups. Since our
research aims to improve students’ privacy and safety, we believe
that the potential benets justify undertaking this research.
To minimize harm, we followed suggested guidelines for the
ethical use of online data for research [
99
,
128
], avoided replicating
tweets and Reddit posts in the paper, and carefully presented the
ndings to maintain platform users’ anonymity. We followed the
terms and conditions of the respective platforms in analyzing the
data. The datasets are stored in a secured server and will be deleted
post-publication. We will only retain post IDs for reproducibility
and may share them on request with other research groups only
after reaching an agreement over data protection standards [99].
9 DISCUSSIONS AND CONCLUSIONS
Overall, the results from the two platforms converged and corrobo-
rated previous research, establishing the robustness of our methods
and the reliability of our ndings. On both platforms, a tiny fraction
of EdTech-related discussions touched privacy or security issues—
this result agrees with prior studies looking into privacy concerns
of mobile applications [
56
,
90
,
93
,
125
]. The absence of student posts
corroborates prior research that found students to be unaware of
or unconcerned about EdTech’s use or data collection. Such disre-
gard, however, may also be attributed to enhanced trust towards
educational institutes [
9
] (compared to e.g., private entities), and
power asymmetry (e.g., between students and instructors or school
administration) where students feel ‘pressured’ to adopt EdTech
and resign their privacy [
110
] due to learned helplessness [
61
]. Sim-
ilarly, a general acceptance of technologies in education due to their
usability [
82
], lack of risk awareness [
71
], as well as organizational
factors, e.g., fear of being monitored on online platforms by employ-
ers [
48
], may have dissuaded academics from critically engaging
with EdTech’s increasing use. Future research could investigate the
roles of these factors in creating or exacerbating privacy/security
risks in educational contexts.
While many ndings supported prior research, we also highlight
the novelty of our study. Unlike any prior studies (e.g., [
9
,
19
,
68
,
71
,
72
,
124
]), we investigated the awareness and perception of two large
online populations regarding EdTech; These platforms were used
in understanding security threats [
64
,
78
,
98
] as well as other socio-
technical issues [
57
] as they oer a glimpse of public perceptions of
those issues. Our longitudinal data spanning over 12 years provides
insights into how perceptions and concerns about EdTech’s privacy
and security varied over time, in particular, before and after the
Covid-19 pandemic.
Most studies investigating EdTech were conducted pre-pandemic
and focused mostly on LMS (Learning Management Systems) [
19
,
68
,
72
,
124
]. In contrast, we discovered privacy concerns regarding
more than 130 EdTech apps that are diverse in nature and are used
in various educational contexts. Future research can use our results
to rank apps based on the number of concerns that were raised
about them. We further identied novel threats and discussed their
eects on marginalized student groups (see below), an important
topic that has not been highlighted enough in the EdTech context.
Our focus on observational data avoided biasing opinions and
allowed us to gather diverse perspectives from dierent stakehold-
ers. Tsai et al. remarked that structural issues, trust, and power
imbalance may exacerbate students’ privacy risks [
120
]; we pro-
vide evidence of such issues (e.g., conicts between educators and
school administration). Additionally, the stakeholder group Per-
sonal that likely included parents and other guardians, underscored
accountability for EdTech providers and their obligation to meet
users’ expectations of privacy, possibly hinting toward a shift in
how EdTech is conceptualized, developed, and deployed. Analyz-
ing unsolicited expressions also allowed us to discover attitudes
toward EdTech and their privacy implications that are unlikely to
be surfaced in interview/survey-based studies (e.g., due to social
desirability bias). For example, multiple prior studies reported that
educators value student privacy highly [
70
,
71
], and select technolo-
gies to use in the classroom keeping students’ privacy and safety
in mind [
71
,
84
]. In both of our datasets, however, we discovered
a preference for EdTech because of its usefulness and a disregard
toward students’ concerns by a few educators. We hypothesize that
such attitudes are more prevalent than what we found (since they
280
Understanding EdTech’s Privacy and Security Issues Proceedings on Privacy Enhancing Technologies 2023(4)
are unpopular and less likely to be posted online) and need to be
considered by researchers studying and improving EdTech’s pri-
vacy and security (e.g., to identify ways to properly communicate
the risks and encourage to adopt mitigation strategies).
Discussions about EdTech’s privacy peaked in 2021, raising an
interesting question: did the pandemic create more risks or just
surfaced the existing ones? We maintain that the pandemic exposed
a broader population to the existing risks, and simultaneously, cre-
ated new risks. For example, remote participation in classes and
exams using tools that continuously record private spaces threat-
ens the privacy of students as well as other coexisting people (e.g.,
family members and roommates). Additionally, using school-issued
devices at home, or device sharing among family members resulted
in new privacy threats for both students and their friends and fam-
ily. Future studies could look into an in-depth understanding of
privacy threats to people who are not EdTech users.
Technical research in this domain, such as detecting vulnerabili-
ties or improper data collection and sharing practices of dierent
apps, is just getting traction [
21
,
26
]. Similar research in other do-
mains generally selected apps to analyze based on popularity; our
ndings oer another selection criteria: based on the (perceived
or experienced) risks. The overlap in risks and apps found in both
datasets suggests the generalizability of the results and their poten-
tial to guide future research in this direction.
Some of the identied threats, such as multi-device tracking and
home network monitoring, may disproportionately aect marginal-
ized and at-risk populations. Students from historically disadvan-
taged communities rely more on school-issued or shared devices,
and consequently, they and their families may face increased surveil-
lance [
55
]. Inferring demographic information from activity logs [
58
]
may out gender-nonconforming students, and social media scan-
ning by EdTech apps can create legal issues if students discuss
pregnancy or abortion on those platforms [
47
,
74
,
76
,
94
]. We recom-
mend future research in this area pay special attention to vulnerable
populations.
An important avenue for future research is to explore ways to
raise risk awareness among students—the most vulnerable stake-
holders, and educators—who prescribe EdTech’s use in their classes.
In addition to trust and learned helplessness, the ne-grained nature
of data (e.g., clicks on web pages) can contribute to underestimating
the risks: collecting such data may seem harmless to those who
may not realize that private information, including identity, demo-
graphic attributes, personality traits, and behavioral patterns, can
be inferred by aggregating such data [
22
,
58
,
79
,
112
,
132
]). Future
studies could examine whether communicating concrete risks—e.g.,
instead of stating what data will be collected, explaining what sen-
sitive information that data will reveal—aects risk perception and
awareness.
EdTech’s safe use also depends on other stakeholder groups who
are involved in EdTech’s procurement, setting institutional policies
regarding data collection and ownership, or responsible for their
development or maintenance. A lack of or conicting understanding
of potential risks among them may negatively aect end users’
privacy and safety. We suggest future research to investigate the
organizational procedures for EdTech’s procurement, whether and
how privacy and security issues are prioritized while creating data-
sharing guidelines and negotiating the ownership and use of data
with EdTech vendors, and ensuring accountability of the responsible
parties if data is misused.
Finally, we make two recommendations about data collection
and analyses. First, we noticed that tweets tend to focus on spe-
cic events (such as data breaches and data abuse by EdTech ven-
dors) and activism (e.g., the movement against Common Core [
2
]),
whereas Reddit posts focus on nuanced discussions and personal
experiences. Thus, future research focusing on a qualitative un-
derstanding of interpersonal or organizational factors may benet
more by analyzing data from discussion forums like Reddit than
micro-blogging platforms like Twitter. Second, regarding manual la-
beling of data, we suggest employing either experts or crowd work-
ers with domain knowledge for annotation; the average crowd work-
ers may mislabel data due to unfamiliarity with domain-specic
entities or contexts.
In conclusion, we believe that this study will motivate and guide
future research exploring ways to enhance risk awareness among
stakeholders and invent techniques to mitigate the identied risks.
ACKNOWLEDGMENTS
We thank the students and crowd annotators for their participation
in this research. This research received no specic grant from any
funding agency in the public, commercial, or not-for-prot sectors.
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A APPENDICES
A.1 Online resources
In addition to the top 100 EdTech apps on iOS and Android market-
places, the following sources were used to collect additional app
names.
(1)
https://www.commonsense.org/education/selections-for-learning
(2)
http://www.iu19.org/top-10-education-hashtags-to- follow-
on-twitter
(3)
https://www.teachthought.com/twitter-hashtags-for-teacher
(4)
https://www.teachthought.com/technology/top-education-hashtags
(5)
https://www.techlearning.com/how-to/recommended-hashtags-
to-discover-top-edtech-tools-and-apps
(6)
https://www.lamentgames.com/blog/top-10-hashtags- for-
amplifying-your-edtech-and-game-based-learning-tweets
(7) https://builtin.com/edtech/edtech-companies
(8)
https://www.oceanfrogs.com/list-of-edtech-companies-in-the-
usa
(9) https://spdload.com/blog/top-education- startups
(10)
https://www.softwaresuggest.com/blog/best-online-exam-proctoring-
software
A.2 Search terms
Hashtags
#crisisteaching, #edtech, #eddata, #k12online, #teachingtools, #dis-
tancelearning, #Udacity, #edtechchat, #edadmin, #OnlineEd, #mlearn-
ing, #googleedu, #edapp, #edtechinHE, #ipclass, #edreformtribe ,
#LMS, #remoteteaching, #schoology, #onlinelearning, #remotelearn-
ing, #e-learning, #elearning, #openeducation, #edreformtribe, #ipad-
chat, #studentdataprivacy, #BYOD, #blendchat, #globaled, #edreform,
#schoolreform, #homeschooling, #EdTechX, #ERL, #iPaded, #Gsuit-
eEdu, #digped, #GoogleEdu, #blendedlearning, #disted, #MovingE-
duForward, #Schoology, #digitalequity, #moodle, #Edtools, #mooc,
#EdApps, #virtuallearning, #schoolreform, #dropouts, #atclass
Twitter handles
@moodle, @Schoology, @udacity, @PearDeck, @Schoolytics, @Ver-
icient, @MercerMettl, @LearningAtScale, @ExamSoft, @exam-
ity, @AIProctor, @Educ_Technology, @edXOnline, @mergeedu,
@quizizz, @speedexam, @EdTech_K12, @BookCreatorApp, @Mi-
crosoftEDU, @ProctortrackPro, @edpuzzle, @ProctorU, @Merlyn-
Mind, @Newsela, @udemy, @edmodo, @Flipgrid, @PSIServices-
LLC, @coursera, @pearsonvue, @blackboard, @Seesaw, @procto-
rio, @conductexam, @CanvasLMS, @merittracsvc, @edtechdigest,
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@MyPowerSchool, @Examus1, @ClassDojo, @DigitalPromise,
@TeachFX, @GoogleForEdu, @in_examonline, @ProctorExam
A.3 User clusters
The following Table 9 lists all 40 user clusters identied based on
prole descriptions, which clusters were grouped together accord-
ing to their resemblance to potential EdTech stakeholders, top key-
words representing the cluster, and the cluster consistency score.
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Proceedings on Privacy Enhancing Technologies 2023(4) Rakibul Hasan
Group Cluster keywords Score
Academic
research, researcher, researching, lunduniversity, researched, keeleuniversity, reseach, stfxuniversity, universi-
tysa, simaduniversity
0.740
teacher, learner, math, lifelong, multiage, lpselma, paraeducator, nkcsd, classkick, lbpsb 0.638
university, director, professor, assistant, associate, universityofky, professors, drakeuniversity, educause, rowa-
nuniversity
0.602
school, principal, hillcrest, elmhurst, oakdale, annandale, haddoneld, prekindergarten, olmsted, beechwood 0.700
Academic
techie
education, student, technology, learn, tech, passionate, teach, teaching, techology, msuepet 0.585
educator, specialist, certied, trainer, integrationist, nearpodcertied, classcraft, certiedmie, edtechie, edugator
0.649
Techie software engineer developer programmer developper coldfusion softwares getpostman klarna couchbase 0.692
data scientist datum analytics analytic visualization analysts datavisualization boozallen linkeddata 0.676
Personal brand
founder, entrepreneur, entreprise, dealmaker, wantrepreneur, angellist, entrepreneure, exited, capitalfactory,
businessperson
0.652
lead, professional, provide, development, experience, training, focus, expert, develop, include 0.574
create, inspire, empower, mission, dierence, strive, bringing, collectively, striving, invigorate 0.589
Inuencer
follow, twitter, account, ocial, instagram, page, update, oce, feed, facebook 0.612
online, website, start, check, visit, click, standout, blogspot, prospectus, peruse 0.528
service, company, solution, solutions, operation, multinational, enabling, managment, caliber, allocation 0.618
business, social, digital, marketing, medium, media, strategy, market, strategist, programmatic, 0.622
Business editor, journalist, vethirtyeight, primetime, journaliste, wlrn, journalistin, geekwire, journos, thismorne, 0.661
news, information, event, bring, late, source, daily, issue, cover, relate 0.554
Personal husband, father, proud, christian, amazing, awesome, marry, married, wonderful, lucky 0.668
wife, mother, friend, daughter, sister, mama, mommy, grandmother, momma, grandma 0.743
Ungrouped
calhoun, augustana, frankfort, muskegon, canandaigua, susquehanna, chattahoochee, valparaiso, wyandotte,
agsta
0.699
illegitimate, yover, covfefe, andrewyang, ronpaul, inalienable, twill, gerrymandering, moreincommon, strong-
hold
0.556
lover, enthusiast, nerd, geek, avid, traveler, addict, fanatic, foodie, adventurer
suppose, heartless, supposedly, tagline, annoyance, undecided, vomit, eervescent, unwilling, suess 0.708
feminist, lgbtqs, sexnotgender, progressivism, p
¯
akeh
¯
a, downtrodden, letlenilead, sisepuede, nohumanisillegal,
saynotoracism
0.637
sport, football, soccer, dodgeball, fantasyfootball, racquetball, basketballer, kickball, lpga, sportsball 0.679
health, care, interprofessional, medicate, phlebotomist, delirium, uhsft, tracheostomy, multimorbidity, greator-
mondst
0.613
yvonne, frazier, mcbride, wilcox, michaela, mcclure, macleod, marjorie, cuthbert, davey 0.694
writer, author, book, write, creator, kidslisten, authored, hardcover, chicagoreader, truestory 0.592
environmental, environ, groundwater, mangrove, tectonic, enviroment, disturbance, builtenvironment, vegeta-
tion, deforestation,
0.630
view, tweet, opinion, personal, endorsement, tweets, opinions, post, express, retweet 0.704
hourglass, hokage, concoction, clockwork, groundskeeper, lamborghini, cauldron, parakeet, groundhog, marzi-
pan
0.566
ction, novelist, fairytale, historicalction, unpublished, novella, histc, thrilling, labyrinth, shortstorie 0.701
design, artist, designer, graphic, photographic, artistry, sketching, photographe, photographs, creating 0.585
community, support, dedicate, commit, advocating, coordinating, communion, wraparound, commence, nfps 0.511
love, life, world, live, people, family, time, share, enjoy, passion 0.599
game, geekdom, stus, warframe, nintendoswitch, gunpla, chiptune, lulz, gumroad, ghibli 0.599
music, musician, soundtrack, baritone, crossover, radiohead, singersongwriter, rocknroll, drumming, dubstep
0.585,
uzbekistan, libyan, caracas, latvian, kyrgyzstan, daodil, whoeurope, internacional, tashkent, yokohama 0.593
londonderry, northwich, skipton, highbury, llandudno, pontypridd, northernireland, basildon, birkenhead,
westmidlands
0.716
food, coee, wine, cooking, cheesecake, avocado, cheeseburger, guacamole, pretzel, chocolatier 0.619
Table 9: All 40 prole clusters, one per line. The horizontal lines indicate which clusters were combined based on the similarity
of the most important words representing them. The middle and right columns show the top 10 words dening the clusters
and their consistency scores, respectively.
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