=Paper=
{{Paper
|id=Vol-2737/long paper02
|storemode=property
|title=Students' Perceptions about Data Safety and Ethics in Learning Analytics
|pdfUrl=https://ceur-ws.org/Vol-2737/FP_2.pdf
|volume=Vol-2737
|authors=Nevaranta, Matias,Lempinen, Katja,Kaila, Erkki
|dblpUrl=https://dblp.org/rec/conf/tethics/NevarantaLK20
}}
==Students' Perceptions about Data Safety and Ethics in Learning Analytics==
https://ceur-ws.org/Vol-2737/FP_2.pdf
Proceedings of the Conference on Technology Ethics 2020 - Tethics 2020
Students' Perceptions about Data Safety and Ethics in
Learning Analytics
Long paper
Nevaranta, Matias 1[0000-0001-8447-856X], Lempinen, Katja 2[0000-0003-4549-4377] and
Kaila, Erkki 3
1 Education services, Satakunta University of Applied Sciences, Pori, Finland
2 Education services, Satakunta University of Applied Sciences, Pori, Finland
3 Department of Computer Science, University of Helsinki, Helsinki, Finland
1 2 3
matias.m.nevaranta@samk.fi, katja.lempinen@samk.fi, erkki.kaila@helsinki.fi
Abstract. Analytics and its various subfields such as data analytics and learning
analytics enjoy varying popularity as measured by public attention. Commonly,
applications of learning analytics in the European area have been talked about
lately partly because of the still fresh European General Data Protection
Regulation (GDPR). Higher education institutions in Finland have all become
active in standardizing learning analytics in recent years, with multiple nation-
wide multi-institutional projects conducted around the topic. In this article, we
present a study where we surveyed the students about the ethical issues of data
collection and usage in learning analytics. The survey was conducted in two
universities in Finland: University of Helsinki and Satakunta University of
Applied Sciences. In addition to finding out students' general perceptions of the
topic, we wanted to find out if there are differences in the attitudes and opinions
between different types of higher education institutes. Based on the results,
students seem to be quite positive about the possibilities of learning analytics but
are also concerned about the safety and usage of their personal data. The
university students seem to be even more cautious, but also less informed about
the data handling procedures.
Keywords: Learning analytics, ethics, data ethics, education
1 Introduction
The need for constant improvement is not a strange thing in today’s economy. We look
for new and innovative ways to improve what we do in our everyday life and education
is no different. Analytics is part of this improvement process where we are slowly
progressing towards the efficient use of big data and as of the last decade or so, we have
slowly been seeing subfields of analytics merge that factor in this trend. Learning
analytics is the equivalent of using analytics to improve education and the learning
process. As stated by numerous sources and studies, learning analytics is the
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
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Proceedings of the Conference on Technology Ethics 2020 - Tethics 2020
measurement, collection, analysis and reporting of data about learners and their
contexts, for purposes of understanding and optimizing learning in the environments in
which it occurs. (Siemens, 2013)
Many countries have been involved in the study of learning analytics as of late and
so has the Finnish higher education community as well. The usual scope of such studies
has been the shaping of and application of learning analytics. (eAMK, 2019) To this
extent, the national education ministry has approved and now funds two back-to-back
learning analytics standardization projects with the goal of creating a tomorrow’s
framework for the application of learning analytics in higher education. (APOA, 2020)
(AnalytiikkaÄly, 2020) These two national projects are divided by the Finnish higher
education institute dual model into one with universities and the other with universities
of applied sciences. As a part of these national projects, we have also become active in
research of learning analytics. In this paper, we report the conduction of a study that
focuses more specifically on the differences of expectations, attitudes and ethics in
learning analytics of students in the higher education. The two universities were
selected to illustrate the diverse nature of higher education institutes in Finland, one
being an example of more theoretical hard sciences universities and the other of the
more practical universities of applied sciences. As a distinction, universities of applied
sciences offer professionally oriented higher education and have strong ties with
working life and regional development while universities focus on scientific research
and the education they provide is based on it. Doctoral programmes are offered by
universities. (Ministry of Education and Culture Finland, 2020)
The focus of the study is to find out students' attitudes and opinions about learning
analytics and the ethical dilemmas often associated with it. We also wanted to find out
if there are any defining deviations between students’ attitudes and opinions in these
two differing higher education models. It should be noted that the study was concluded
during the beginning of spring 2020 and was not affected by the 2020 COVID19-
pandemic since the survey for the study was done prior to the virus reaching pandemic
status. (Finnish Institute for Health and Welfare, 2020)
This paper is structured as follows. First, we present related studies with focus on
articles related to learning analytics and ethics. Next, we discuss the topic of ethics in
learning analytics in more detail. After this, we present the setup of our study, including
participants, the design and the conduction of the survey. Next, the results are presented
and discussed. Finally, we present our conclusion and some ideas for future research.
2 Related Work
As previously stated there has been a substantial amount of studies in the subfield of
learning analytics in the last decade or so some of which will be used as original study
sources for our study. Learning analytics applications studies compared to many
dominant new fields like big data and AI have thus far been limited to a few but fruitful
studies and ever since the beginning of 2010’s several research communities have been
formed around the role of data analytics in education. (Siemens, 2013) The field of
learning analytics (LA) has developed ever since and since the first Learning Analytics
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and Knowledge (LAK) conference in 2012 there has been ever increasing amounts of
studies in the field. (LAK’11, 2011) Since then several studies have applied different
methods to research learning analytics and its applications. Most of the studies thus far
have been focusing on data mining techniques and statistics, these being the most
prominent methods of analysis.
What do we actually mean when we talk about learning analytics? Early definitions
(see e.g. Siemens & Fog 2011) often associated learning analytics with big data, but as
we have recently seen, learning analytics setups can vary from a handful of students to
whole countries. Ferguson (2012) was probably one of the first authors to combine
learning analytics with educational technology, and this has been indeed quite typical
way to use analytics (although learning analytics can perfectly well be used in settings
where no technology enhanced learning is involved). The definition of Gašević et al.
(2015), which states LA as "The analysis of data collected from the interaction of users
with educational and information technology" is quite close to a general description.
However, the "official" definition announced in the first International Conference on
Learning Analytics (see eg. Siemens & Baker 2012) is probably the most conclusive so
far: “Learning analytics is the measurement, collection, analysis and reporting of data
about learners and their contexts, for purposes of understanding and optimizing learning
and the environments in which it occurs.”
More recently Teasley (2019) stated that learning analytics has ever since become
increasingly broad as a subfield of analytics including now but not limited to the
aforementioned methods like data mining and statistics, but also semantics, learning
theories and more precisely focused studies. Since this would suggest that learning
analytics is getting more precision-based as more varying methods are used to do more
narrowed and focused studies, the methods to improve the learning experience for
individual students further would tempt to do more in-depth research about the primary
benefactors of said analytics. (Teasley, 2019) (Khalil & Ebner, 2016) However there
are only a few researches about students’ beliefs and expectations towards learning
analytics services. (Whitelock-Wainwright et al 2019, 633) In Finland and in Europe
the study and application of learning and any form of analytics has also been dealt a
significant blow due to the inception of the European general data protection regulation
that dictates the application of data that can be identified as personal or profitable data.
The regulation affects every industry with education also being affected as most of the
analytics data in educational institutes is closely tied to students in said institutes.
(European Commission, 2019) This is something that should always be kept in mind
when doing studies like this, which heavily rely on the data of students, how it is
handled and processed during the study.
3 About Learning Analytics and Ethics
The application of all and any analytics does come with its own questions about data
ethics. Generally, ethics in technology question the problems that technological
advancements, digital tools and methods produce. This can range from privacy to
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autonomy to discrimination by data. (Johnson, 2017) The overall idea of data is to make
the world more transparent and efficient towards individuals of which data is collected
from but at the same time, these individuals know little about the data collected from
them, referred to as the transparency paradox. (Richards & King, 2013) So ethics in the
context of learning analytics questions for example whether it is ethically permissible
to pursue learning analytics, and whether the benefits outweigh the costs. (Rubel et al.
2016) As such, it was considered a worthwhile effort to analyze the ethicality of
learning analytics through the different students' viewpoints as students represent the
individuals of whom the data mostly is gathered from in this context. At present time,
most of the population in European Union should have been activated into thinking
about data regulation and protection due to the GDPR through studies, work or other
sources over the last two years. The abundance of data and information everywhere
affects everyone on a daily basis and is closely tied to their personal life. (Marr, 2019)
We would dare say that whether or not the general population regard data ethics
important is not about the lack of interest but lack of awareness at this point in 21st
century.
To draw a line from data ethics to LA, we have to consider the owners and producers
of data in question. In a learning environment, this would mean institutes and students.
These educational institutes can gather data about their students to facilitate the
education requirements dictated by law which can be used for LA but also collect data
for other varying purposes if they have been given permission to do so which also could
benefit LA among other things. (Data protection law 1050/2018, 2 chapter 6 §) To
satisfy the framework requirements for education qualifications and be able to offer
studies institutes need to possess certain information about its students. This contains
but is not limited to personal information (previous education record, social security
details etc.) and depending on the extent of digitalization, the amount of student data
on each individual student can rightfully cause concern among students.
Now to elaborate on the reasoning for the study, the ethics point of view; it is a
common conception that the dual model presents a divide in the train of thought in the
general population which stems all the way from second degree education level, called
the upper secondary level, where the dual model is first proposed. (Ministry of
Education and Culture Finland, 2020) You typically choose between getting a
profession fast through vocational school or entering high school (also known as
general upper secondary education, fin. lukio) to get more literate before moving
onwards to third educational level (which is the university level in Finland, National
Qualification Framework (NQF) 6 and up). (Finnish National Agency for Education,
2020) This creates the basis for the dual model divide in which two sides represent
different emphasis in their studies, commonly depicted as practice and theory.
Therefore, it seems that different educational environments could nurture different
ideas and thoughts that could be more distinct at a higher education level. Our study
introduced these students to the concept of LA and data ethics, which we could now
measure differences in. Data privacy and the ethicality of using individuals' data in
analytics is something that does not easily unfold without delving into the subjects
and/or having prior knowledge about. We also previously conducted a study about the
students’ and teachers’ initial thoughts on LA. Through that study, we concocted the
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research interrogative about the dual model divide. (Nevaranta, Lempinen & Kaila,
2019) The question we try to ask and answer in this study is effectively; is there a
distinguishable deviation in the perception of LA and data ethics between students
representing each side of the common dual model and how does it manifest? Of course,
to answer these questions we must first humor the idea that the dual model divide exists.
4 Research Setup
In this section, the research setup is presented. The data gathering was spread between
two universities in Finland: University of Helsinki (which is the largest university in
the country) and the Satakunta University of Applied Sciences. The reasoning behind
this was to find out if the students in different universities have different perceptions
about use of learning analytics and the ethical perspectives associated with it. To further
diversify the student population, the study in University of Helsinki was conducted in
a programming Massive Open Online Course (MOOC), where many students come
from outside university. However, in this article we have filtered out the answers from
students who did not have a status as a student in either of the selected universities
when the survey was conducted.
4.1 Procedure
The study was conducted as an online survey. In the university, the survey was attached
to a programming MOOC. The MOOC in question is a popular programming MOOC
aimed for students with multidisciplinary backgrounds and can be completed by
students outside universities as well. In addition, the MOOC works as an entrance
exam: the students who pass the MOOC with a high enough grade can apply to be
students in the Department of Computer Science. Because of this, the MOOC has been
quite popular. More than 5,000 students in total have started the current
implementation. The survey was included in one of the weekly exercise rounds in the
MOOC. To motivate the students in answering, one point was awarded for it.
In the university of applied sciences, the survey was conducted through basic face
to face (F2F) methods. The same survey was used but the students were actively
encouraged to participate in it by engaging them. Students of two separate campuses in
the university of applied sciences were surveyed in their respective locations (Pori and
Rauma), and motivated to participate by awarding traditional Finnish overall badges (a
Finnish student custom in which students collect badges to overalls that represent your
faculty) upon survey completion.
The survey was constructed with Google Forms and Microsoft Forms. Forms were
selected to conduct the survey in the educational institutes as they offered adequate
form functionalities and were deemed viable solutions for surveying. The survey
questions were composed mostly of multichoice questions and statements based on the
5-step Likert scale and open form questions.
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4.2 Participants
The participants for the survey were a group of students from both of the higher
education institutes. The total number of replies received in the survey was 201 of
which 56 were from the university and 145 from the university of applied sciences. 652
students answered the survey in the university programming MOOC, but only 56 of
these had an official status as university students. Hence, the other answers were
excluded from this study. Student numbers are displayed in Table 1.
Table 1. Number of participating students in both universities. The largest age
group was 20 to 25 year olds in both universities. The final column depicts the
number of students who answered to open questions as well.
Educational institute Participants Largest age group (%) Open answer %
University 56 20 to 25 (58%) 73%
University of 145 20 to 25 (46%) 43%
Applied Sciences
Biggest age group was 20 to 25 year olds (58% and 46%, respectively). Second
largest age group was 26 to 30 (21% and 25% respectively). Hence, a total of 79% and
71% of the students in the survey were between the ages 20 and 30. 73% of university
students and 43% of university of applied sciences students answered to at least one of
the open questions at the end of the survey, which can be considered as a good total
percentage.
The division of students between different majors is displayed in Figures 1 and 2.
Figure 1. The students' majors in university.
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Figure 2. The students' majors in university of applied sciences.
As seen from the figures, the largest groups were Math and Science and Engineering,
respectively. The students in the university had more diverse backgrounds than students
in the university of applied sciences. It should be noted that the major of Social and
Human in university of applied sciences consists mostly of business majors.
5 Materials
As the idea was to collect data on students' ethical perceptions about learning analytics,
the majority of the questions were constructed around this theme. The form of data
collection was an online survey that was made to reflect the themes of the study in
learning analytics and its ethics. A total of twenty multichoice questions and statements
and three open questions divided into three categories were included in the final survey.
The themed categories were:
1. Attitudes: Students' attitudes towards the application of learning analytics.
The first section also measures students' current knowledge about analytics.
2. Expectations: What students expected learning analytics to be, and for whom
are they collected.
3. Ethical issues: How students viewed the possibilities and problems posed by
learning analytics and associated individual data in learning environments.
This survey was then replicated into two exact copies, which were then shared with
students in both institutes early 2020. The questions and/or statements in these
categories were based on the themes found in the student-teacher workshops made in
APOA-project (Avain Parempaan Oppimiseen Ammattikorkeakouluissa) and
published by the Jyväskylä University of Applied Sciences late 2019 (Hartikainen, S.
et al, 2019) and Whitelock-Wainwrights (2019) pilot studies article about LA
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questionnaire in students’ expectations. The form of the questions and statements in the
survey were chosen with unequivocality in mind; each student should be made to
understand the questions and statements in the same way.
6 Results
In this section, we present the results from the surveys conducted in both universities.
First, the means and differences between the two universities for all three themes are
displayed. After that, we discuss the answers to open questions.
In Table 2, the average answers for questions in Theme 1, attitudes are displayed.
Table 2. The mean values for statements in Theme 1, measuring students' attitudes
towards learning analytics (LA). The difference is calculated by subtracting the
university of applied sciences mean value from the university mean value. The
differences with statistical significance are bolded.
University
Statement University Difference
of A.S.
1. I know what analytics means 3.518 3.552 -0.034
2. IT systems help organizing and scheduling my
studies 4.196 4.517 -0.321
3. LA helps following my own progress 3.821 4.138 -0.317
4. It is a positive thing, that a teacher can follow
my progress via LA 4.125 4.283 -0.158
5. It is a positive thing, that I'm assisted with my
studies without my own request, if a need is
indicated by data 4.179 4.331 -0.152
6. Institution can freely utilize all data about me to
progress my studies 2.804 3.621 -0.817
As seen in the Table, there seems to be no major differences between the two
universities. However, the students in the university of applied sciences seem to value
learning analytics and IT systems in general higher than the university students. The
university students also seem to have a tighter attitude towards institutions utilizing
data in analytics. The differences in questions 2, 3 and 6 are statistically significant
(two-tailed T-test, p < 0.05).
The mean values and differences for questions under Theme 2, expectations are
displayed in Table 3.
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Table 3. The mean values for statements in Theme 2, measuring students' wishes and
expectations towards learning analytics. The difference is calculated similarly to
previous table.
University
Statement University Difference
of A.S.
7. I wish that LA would help me follow my
progress in studies 4.055 4.110 -0.056
8. I wish LA would help me design my studies
better 3.911 4.200 -0.289
9. I wish LA would help visualize my progress in
studies 4.232 4.145 0.087
10. I wish teachers would use LA to follow my
progress 3.745 3.972 -0.227
11. I wish that the institution would use LA to
progress my studies 3.709 3.966 -0.256
According to results, the students' wishes towards learning analytics were quite similar
in both universities. Most students hope that utilization of analytics would help them to
design and visualize their studies better. Notably, the students also wish that learning
analytics would provide better chances for teachers to follow their progress in studies.
There are no statistically significant differences between statements under Theme 2.
The mean values and differences for questions under Theme 3 are displayed in Table
4.
Table 4. The mean values for statements in Theme 3, measuring students' perceptions
towards ethical considerations of LA. 'Institution' in statements refers to the university
where the data was collected. The difference is calculated similarly to previous tables.
The differences with statistical significance are bolded.
University
Statement University Difference
of A.S.
12. I get enough information about my data
collected by institution 2.582 3.131 -0.549
13. I can affect how my data is collected and hande
in institution 2.722 3.179 -0.457
14. I know what data is collected by institution 2.091 2.876 -0.785
15. I accept the collection of my data by institution 3.782 3.938 -0.156
16. I accept the utilization of my data by institution 3.855 3.903 -0.049
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17. Institution should follow laws and regulations
to keep my data safe 4.891 4.338 0.553
18. My data collected by institution can be used for
my own benefit 3.855 4.117 -0.263
19. My data collected by institution can be used for
institutions benefit 2.855 3.821 -0.966
20. I know that my data is removed by institution
after my graduation 2.182 3.345 -1.163
Notably, the answers to the third category were somewhat different between the two
universities. In fact, the difference is statistically significant in all questions under
Theme 3 except for question number 16. The biggest absolute differences were in
statements 19 and 20. The first one was about attitudes towards institutions using data
for their own benefit (such as marketing), which the students from the university of
applied sciences found more acceptable than the students from university. The
university students also seemed to lack knowledge about their personal data being
erased after their graduation, which may indicate that the university of applied sciences
had informed the students better about the topic.
In addition to questions answered in the Likert scale of 1 to 5, the survey contained
three open questions. In general, the students in the university were more verbose in
their answers, but there were no huge differences in the tone or content. The first
question asked the students about their opinions in using their own data to progress
their studies. The students were also asked to consider both positive and negative
aspects of the topic.
In general, the students found the possibilities of data utilization positive (all
comments are translated from Finnish and in some cases re-worded and/or shortened
by authors).
- "Data collection is a good thing for course design and implementation"
- "I found the data collection as positive thing, since my data benefits me"
- "I think the data can help me study more effectively"
In a modern society where data usage is common especially younger generation
seems to be more accepting of it. On the other hand, most students expressed their
concerns about the safety and ethicality of data storage and handling by third parties:
- "I found it risky if third parties are used to handle the data"
- "I'm wondering if my data is used to something I don't know about"
- "I am very suspicious about any data collection or utilization where the
participants are not clearly informed about the procedure"
- "The students should be able to restrict the utilization of their data"
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Now that GDPR is in effect the results could be affected in one way or the other
about data safety; GDPR made the public more aware about data protection but at the
same time it stipulated it. In the second open question, the students were asked about
the additional value learning analytics could produce for them. Again, most of the
students considered analytics as potentially quite useful:
- "I expect analytics to improve the development of study materials and the
better teaching of difficult topics"
- "Recommendations on what courses to take"
- "More personal guidance"
- "Personally tailored courses"
Personal guidance and adaptive learning materials and courses were repeated in most
of the answers. It seems that the students find this as one of the most important thing
learning analytics can offer them. Analytics generally is seen as added value to an
existing system and in LA the benefits are easily perceived.
As the final open question, the students were asked to consider if they find learning
analytics as ethically justifiable part of future studies, where student instruction is based
on data collection and analysis. While previously students gave positive feedback about
LA, in the utilization of analytics, they found both positive and negative chances:
- "In principle, it's in everyone's interest to make learning more effective.
Practical procedures may prove to be problematic"
- "If the learning results are improved, the development is ethically provable.
The ethical problems may arise from data collection and storage."
- "In the future lot of the studies are likely implemented over internet. Hence,
LA will be needed more."
- "I find LA ethically sustainable as the results will benefit students. Students
should have a chance to limit the data collection and usage."
Some students mentioned that even though learning analytics has potential ethical
issues, it is still ethically more acceptable than most of the data collection happening
already in different systems, such as social media platforms.
7 Discussion
As seen on the results, the students in general seemed to find the possibilities learning
analytics offers quite positive, but were worried about whether their data is stored and
utilized safely and ethically sustainable. The biggest things that students value in
analytics are visualizing their progress and the possibility to get assistance when
required, even without requesting for it. From the future analytics, the students seem to
wait for personalized and adaptable learning environments and materials. This is
perfectly understandable, as in online education (especially in the form of massive open
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online courses), there is often a lack of communication and personal guidance. Hence,
the idea of learning environments that fit to everyone's skill levels and learning
preference must sound tempting.
Students' worries about data safety and misconduct with their data are also perfectly
understandable. In general, most of the students seem to agree that data collected about
their learning can be used in analytics as long as the results benefit themselves.
However, the students find it problematic if data is used for anything else (even inside
the institutions) or delivered outside institutions. From a perspective of learning
analytics researchers, we find this attitude quite reasonable: as long as learning
analytics is conducted openly and in collaboration with students, they seem to have no
problems with it.
Although students in both participating universities seemed to mostly agree on the
ethical issues of learning analytics, there were some significant differences. The
students in university seemed to be more cautious about the usage of their data. For
example, while students in university of applied sciences seem to find the usage of their
data to benefit the institution within acceptable boundaries, the students in university
find it significantly less acceptable. Similar differences can be found with other
statements and in open comments as well: the comments from university students were
more verbose, and the same basic message was repeated in them: using data is perfectly
fine as long as it is used to benefit students and not shared with anyone outside the
institution.
There are some likely explanations for the differences between the two universities.
First, the students in university of applied sciences are likely more familiar with the
concept of learning analytics and with the procedures associated with it (such as data
safety for example), due to the ongoing APOA project (APOA, 2020) which has
familiarized the students and teachers with analytics. Second, the university students
came from a more diverse selection of majors, and up to this date, the university
probably has not informed students well enough on ethical procedures of data handling.
For example, on average university students seem to have no idea whether their data is
erased from the institution after their graduation. In addition, since the survey in the
university was done within a programming MOOC the more verbose answers could be
speculated with the participants interest in computer sciences and therefore with data
ethics.
It should also be noted that in the survey, we first introduced the concept of LA to
the students in order to give them an idea about it, so they would not rush into the survey
with a false understanding. Even though we created the survey with unequivocality in
mind, while presenting the survey in university of applied sciences we also encouraged
people to use the neutral answer “I don’t know” when they did not feel like they fully
understood the question as to prevent falsifying the data. No similar procedure was used
in university, which may have skewed the results in some degree against or in our favor.
These details and their effects could be ruled out by replicating the research setup in
future on different groups in different educational institutes.
Overall, it seems that the students have strong opinions about the subject. For
example, it is quite typical that openly answered questions, particularly at the end of a
survey do not collect that many replies. In the survey, we had many very interesting
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and well thought out answers on all three categories. The fact that university students'
answers were much longer could indicate more interest on the topic, but likely
explanation may also be that the topic was already familiar to students in university of
applied sciences. Moreover, it is possible (although cannot be confirmed) that students
in the latter case answered the survey mostly with mobile devices while the students in
university used laptops, which enables easier text input. From the students point of view
the survey did seem to be a tad too long, which we found out when interviewing some
students about it after completion. The amount of unreplied open answers supports this.
By now, with the accelerated digital leap forced on us by the 2020 pandemic, the
collection of student data should have become more obvious in a modern educational
environment. More and more of the lessons are held via digital tools, conferencing
systems, learning management systems etc. supplemented by digital registers that keep
track of the students' studies and progress. As it is at least somewhat likely that some
of the newly widely adapted digital tools will become part of the permanent workflow
after the pandemic as well, the need for extensive use of learning analytics has become
imminent. For this reason, we should listen to students' perceptions carefully to come
up with ethically sustainable analytics. LA is seen as a necessary form of analytics
somewhat comparable to healthcare analytics, where the data primarily serves the
individual, but also the public and common welfare.
8 Conclusion and Future Work
In this study, we found out that students find learning analytics as a potentially
promising tool for tracking and visualizing their own progress. They also see the
potential of analytics to create adaptive and personalized learning paths for students
with different skills, mindsets and learning preferences. However, the students are also
worried about the safety of their data and the transparency of the data collection and
utilization. In short, the students feel that learning analytics can and should be used, but
only if the data collection and handling is done in ethically justifiable methods.
Curiously, university students were stricter about this than their counterparts in the
university of applied sciences were.
In future, we are planning to expand the study to contain more universities and other
institutions, and to collect even more data about students' backgrounds to find out if
there are factors that may explain the attitudes and expectations towards analytics. It
would also be useful to find out how much the universities have formally informed the
students about the data safety procedures used in the institutes, f.ex. how GDPR has
been implemented and voiced in a institute. Finally, it would be interesting to combine
the survey data with performance data from courses to find out if the students' success
or failures in courses affect their attitudes towards analytics. Data ethics should be given
more consideration in future studies since it ultimately affects in which way the public
opinion, attitudes and expectations, sway and as the world of IT it will evolving and
changing constantly.
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Proceedings of the Conference on Technology Ethics 2020 - Tethics 2020
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