=Paper=
{{Paper
|id=Vol-2985/paper4
|storemode=property
|title=Finnish Education Professionals’ Thoughts on Adaptive Learning Technologies
|pdfUrl=https://ceur-ws.org/Vol-2985/paper4.pdf
|volume=Vol-2985
|authors=Joonas A. Pesonen,Ville Kivimäki
}}
==Finnish Education Professionals’ Thoughts on Adaptive Learning Technologies==
https://ceur-ws.org/Vol-2985/paper4.pdf
Finnish Education Professionals’ Thoughts on
Adaptive Learning Technologies
Joonas A. Pesonena , Ville Kivimäkia
a
University of Helsinki, Finland
Abstract
With the rapidly increased use of digital technologies in education due to the COVID-19 pandemic, it is
important to discuss these technologies’ impact on the teaching profession. Using thematic analysis and
epistemic network analysis (ENA), we analyzed 114 social media posts by Finnish education professionals
commenting on an opinion piece about technology partly taking responsibilities usually attributed to
a teacher. Out of the analyzed posts, 32 were supportive, 30 ambivalent, and 52 critical towards the
presented scenario. The epistemic network analysis graphs showed some differences between posts
with a different attitude. Supportive posts, on average emphasized technological capabilities and their
connections with teacher workload and self-directed/self-regulated learning. In comparison, the critical
posts on average emphasized human presence and its connections with pupil diversity and technological
capability. Our findings both reveal the relevant themes in the discussion about technologies’ impact on
the teaching profession and underline the differences in supportive and critical argumentation.
Keywords
adaptive learning technologies, self-regulated learning, teaching profession
1. Introduction
While digitalization and automation have disrupted many industries in the last few decades,
education has largely been an exception to this trend. As Selwyn puts it, “most people intuitively
feel that education is an essentially human undertaking” and “the belief persists that learning is
something best guided by expert human teachers in socially rich setting” [1, p. 1].
However, now that the COVID-19 pandemic has interrupted classroom learning for at least 9
out of 10 students worldwide [2], schools and teachers around the world have been forced to
find ways to maintain continuity of learning without physical proximity. While the exceptional
methods used during the school closures have mostly been ad hoc and in many ways inferior
to classroom learning, this large-scale experimentation with digital technologies will likely
affect education also in the post-covid world. In particular, it is interesting whether the new
technologies have an impact on how teacher’s role and the teaching profession are seen.
In the present study, we examine Finnish education professionals’ thoughts on digital technolo-
gies in education and their impact on the teaching profession. First, we present relevant back-
ground related to artificial intelligence in education and technology-enhanced self-regulation
of learning, followed by our research questions.
Nordic Learning Analytics Summer Institute (NLASI) 2021, August 23, 2021
Envelope-Open joonas.pesonen@helsinki.fi (J. A. Pesonen); ville.kivimaki@helsinki.fi (V. Kivimäki)
Orcid 0000-0003-4166-4762 (J. A. Pesonen); 0000-0003-4939-7400 (V. Kivimäki)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
1.1. Artificial Intelligence and the Teaching Profession
Novel technologies such as machine learning and artificial intelligence are changing labour
markets and disrupting industries. Computerization of work is moving fast from routine tasks in-
volving explicit rule-based activities to non-routine cognitive tasks [3]. There are two prominent
scenarios when applying artificial intelligence at work, namely automation and augmentation.
Whereas automation implies that machines take over a human task, augmentation means that
humans collaborate closely with machines to perform a task [4]. From employee perspective,
a study shows that perception of technology replacing one’s job is generally low, although
participants under 30 were more worried than older age groups [5]. In another study among
a range of industries employees perceived “human touch” and “soft skills” as irreplaceable by
novel technology [6].
Regarding the teaching profession, teachers are considered to have a low risk of computeriza-
tion [3], and thus, the augmentation scenario is more likely than the automation scenario. The
concept teaching augmentation has been used to describe tools extending teachers’ pedagogical
abilities [7]. These technologies potentially shaping the future of teacher’s profession are
evolving rapidly and include e.g. learning analytics applications [8], intelligent tutoring systems
[9], adaptive learning technologies [10] and educational chatbots [11, 12].
It is essential to acknowledge, that no technological innovation will inevitably change teaching
profession. As Selwyn [1] points out, integration of any technology to society should be
approached as a choice: “it is crucial that we consider the possibility of alternative technological
pathways and different digital futures for education”. He presents two scenarios how AI may
change the teaching profession. In the first scenario AI frees up teachers to engage in meaningful
acts of leading, arranging, explaining, and inspiring while technology takes care of routines
and duties. In the second scenario, technology becomes an institutional tool of performance
management, and teachers end up losing their autonomy while fulfilling the expectations of the
technology, e.g., encouraging students to write in ways favored by automatic grading systems
[1].
1.2. Technology-enhanced Self-regulation of Learning
Self-regulated learning refers to how learners systematically activate and sustain their cognitions,
motivations, behaviors, and affects toward the attainment of their learning goals [13]. In a
classroom setting, the teacher can support learners’ self-regulation and then gradually decrease
the amount of support, promoting the learning of self-regulation skills.
With the development of adaptive learning technologies (ALTs, see e.g., [10]), this role
of scaffolding support may partly be transferred to technological systems. Researchers have
envisioned “a hybrid human-system regulation with fluctuating boundaries between student and
system control” where “embedded learning analytics techniques can support a new generation
of SRL support that adjusts external regulation based on insights gained from data” [10, p.
473] and presented a framework for human–AI hybrid adaptivity [14], describing how humans
and educational AI systems can augment each other in many ways. Moreover, it has been
pointed out that the development of human-AI systems should engage practitioners (in this
case, teachers) throughout the lifecycle of system development and that these systems should
be studied in real-world contexts [15]. While these technologies are still at their preliminary
stages, their sophistication will grow over time.
In summary, it is highly likely that new technologies will change the teaching profession
in one way or another. Therefore discussing these topics within the community of education
professionals is extremely important.
1.3. Aims of the Current Study
With the massive increase in the use of digital tools in teaching and learning during the pandemic,
it is important to investigate implications to educational arrangements in general and to teaching
profession in particular. Based on literature, we know that machines can successfully handle
many routine teacher tasks such as grading. However, supporting learners’ self-regulation has
been an exclusively human endeavour and central to the teaching profession. As digital systems
get more sophisticated, it is important to discuss the possibilities and limitations of a machine
to guide, encourage and support students with their self-regulation broadly in the educational
community.
In the present study, we analyze social media posts commenting on an opinion piece published
in the largest daily newspaper in Finland. The author of the opinion piece describes how learning
of new content is increasingly guided by adaptive learning technologies instead of a human
teacher and how this kind of shift would free teachers’ time to focus on tasks where humans
are best. Our research questions are following:
• RQ1: What kind of argumentation Finnish education professionals use in social media
discussions to support or criticize a scenario of adaptive learning technologies taking
responsibilities traditionally attributed to teachers?
• RQ2: How do Finnish education professionals see the role of a teacher in contrast to the
role of technology in supporting pupils’ self-directed and self-regulated learning?
2. Methodology
2.1. Context
In Finland, all schools shifted to remote learning because of the covid-19 pandemic for eight
weeks during spring 2020. Afterwards, schools have shifted between contact teaching and
remote learning depending on the development of the epidemic in each region.
During the pandemic, there have been active discussions in Finnish media about learning
with digital technologies. One contribution to this discussion is an opinion piece [16] written
by a Helsinki-based elementary school teacher and published on April 19th 2021 by Helsingin
Sanomat, the largest daily newspaper in Finland. The author suggested introducing weekly
remote learning, where elementary school pupils would study remotely one day a week lever-
aging digital learning platforms. He describes how this kind of shift would free teachers’ time
to focus on things where humans are best, such as contextual interaction.
The piece generated lots of comments in social media, especially in two Facebook groups: a
general forum for discussion among Finnish education professionals (ca. 17 500 members) and
a group specializing on the teaching profession (ca. 13 000 members). Comments were posted
during April 2021.
2.2. Material
Social media posts were gathered from the Finnish education professionals group (N=48),
teaching profession group (N=81) on Facebook, Twitter (N=12), and newspaper comment board
(N=10). Posts that did not include any argumentation (e.g. posts with only one word or emoticon)
were removed from the dataset, ending up with 114 posts. In addition to the post content, the
amount of social media reactions (i.e. likes) were collected for each post. The opinion piece as
well as all the comments were originally written in Finnish and the excerpts presented here
have been translated into English by the authors.
2.3. Analyses
2.3.1. Attitude towards the scenario.
The posts were rated by both authors as supportive, critical or ambivalent, based on the attitude
towards the presented scenario. The inter-rater reliability was moderate (Cohen’s kappa
0.68)[17].
2.3.2. Thematic analysis.
Inductive thematic analysis was used to analyze the themes included in the social media posts.
Both authors took part in the process. First, the first author coded all the posts and created the
initial coding scheme. Typically each post was assigned two to five codes. Next, the second
author used the coding scheme with 20 first posts. Then, inter-rater differences were discussed,
and the coding scheme was readjusted (e.g. two of the initial codes were removed). Finally, both
authors used the agreed flat coding scheme with all the posts. The codes with their descriptions
and inter-rater reliability are presented in Table 1. Inter-rater reliability ranged from moderate
(Cohen’s kappa 0.61) to strong (Cohen’s kappa 0.84)[17].
2.3.3. Epistemic Network Analysis.
In this study, we applied Epistemic Network Analysis [18, 19, 20] to our data using the ENA
1.7.0 Web Tool [21]. Our ENA model included the codes presented in Table 1. We defined con-
versations as all posts associated with a single social media forum. The ENA model normalized
the networks for all units of analysis before they were subjected to a dimensional reduction,
which accounts for the fact that different units of analysis may have different amounts of coded
lines in the data. For the dimensional reduction, we used a singular value decomposition, which
produces orthogonal dimensions that maximize the variance explained by each dimension (see
[18] for details).
In this study, ENA was used to compare mean networks of posts with supportive, critical or
ambivalent attitude towards the presented scenario. Networks were visualized using network
graphs where nodes correspond to the codes, and edges reflect the relative frequency of co-
occurrence between two codes. The positions of the network graph nodes are determined by
an optimization routine that minimizes the difference between the plotted points and their
corresponding network centroids. Because of this co-registration of network graphs and
projected space, the positions of the network graph nodes and the connections they define
can be used to interpret the dimensions of the projected space and explain the positions of
plotted points in the space. Our model had co-registration correlations of 0.93 (Pearson) and
0.93 (Spearman) for the first dimension and co-registration correlations of 0.91 (Pearson) and
0.91 (Spearman) for the second. These measures indicate that there is a strong goodness of fit
between the visualization and the original model.
Table 1
Codes with their descriptions and inter-rater reliability
Code Description Cohen’s kappa
ARR Educational arrangements: Considerations how teaching and/or learn- 0.65
ing are arranged
PRE Human presence and interaction: Mentioning human presence, impor- 0.71
tance of interaction or interaction skills
TEC Technological capability: Properties and possibilities of technology 0.65
are discussed
WOR Teacher workload and efficiency: Expressions related to teacher work- 0.84
load and/or efficiency
DIV Pupil diversity and equality: Differences between pupils or expressions 0.61
related to equality between pupils
SEL Self-directed/self-regulated learning: Mentioning either pupils’ self- 0.73
directed/self-regulated learning skills or the role of teacher/system in
supporting pupils’ self-regulation
3. Findings
The number of posts including each code as well as sum of likes of those posts are presented in
Table 2. Out of 114 posts analyzed, 32 (28 %) were supportive towards the presented scenario,
whereas 30 (26 %) were ambivalent and 52 (46 %) critical. While this presents the attitudes of
professionals taking part in the discussion, the sum of social media likes shows attitudes of the
larger community. Out of 1251 likes, 103 (8 %) were connected with posts showing supportive
attitude, whereas 192 (15 %) were connected with posts showing ambivalent and 956 (76 %) with
posts showing critical attitude.
3.1. Connections Between Codes
The epistemic network analysis graphs are presented in Figure 1. The most central code
was Educational arrangements (ARR), having strong connections with all other codes. When
comparing the networks of supportive and critical posts, it is clear that different themes are
ARR Educational arrangements WOR Teacher workload and efficiency
PRE Human presence and interaction DIV Pupil diversity and equality
TEC Technological capability SEL Self-directed/self-regulated learning
All posts Ambivalent
Supportive Critical
Figure 1: Mean networks of posts with different attitudes towards the presented scenario.
highlighted. In the supportive network, the focus is on connections with Technological capability
(TEC) and in the critical network on connections with Human presence and interaction (PRE)
Moreover, it is interesting to compare which connections are missing in each graph. In the
Table 2
Number of posts and sum of likes by code
Code Supportive Ambivalent Critical Total
Posts Likes Posts Likes Posts Likes Posts Likes
ARR 22 84 18 157 37 699 77 940
PRE 13 44 7 20 24 563 44 627
TEC 21 57 7 9 15 402 43 468
WOR 12 47 13 42 17 309 42 398
DIV 8 40 4 28 16 377 28 445
SEL 10 36 3 123 14 390 27 549
Total 32 103 30 192 52 956 114 1251
supportive network, connection between Pupil diversity and equality (DIV) and Human presence
and interaction (PRE) is missing, as well as connection between Technological capability (TEC)
and Human presence and interaction (PRE). Respectively in the critical network, the connection
between Technological capability (TEC) and Teacher workload and efficiency (WOR) is missing,
as well as the connection between Technological capability (TEC) and Self-directed/self-regulated
learning (SEL).
In the following, we take a deeper look into the roles of technology and teacher in guiding
and supporting pupils.
3.2. The Roles of the Teacher and Technology in Supporting Pupils
Many comments emphasized pupils limited self-directed and self-regulated learning skills. This
was often coupled with the notion of challenges in supporting self-regulation remotely:
“Uppermost the delusion that you could just ’leave’ the children to study the vocab-
ulary etc. Not at all. Online the teacher presence is highlighted more intensively so
that high-quality learning and continuous assessment can happen. It requires a lot:
both from teachers and pupils. The most self-directed learners can surely do something
as described, but I would not in any case - and fortunately it’s not legally possible -
categorically increase remote learning in basic education.”
“[–] The pupils aren’t self-directed except for a few cases. Most of them need
interaction and discussion about other things among teaching. That works best live,
worse remotely.”
Some of the critical comments directly addressed the shortcomings of technology in guiding
and supporting pupils:
“This digital-environment-self-directed-learning mantra has been tooted as forth-
coming for thirty years now. Nevertheless, give any ’self-directing’ learning material
or digital platform to a group of 24 pupils and follow the situation for 45 minutes.
I’ll eat my hat if even half of the group is doing what they’re supposed to with the
program, or if they’re even in the program. In small bits, yeah, but replacing teachers,
doubt it.”
Other comments did not mention the technology but emphasized that guiding and supporting
should be carried out by the teacher:
“Oh my god! Teacher’s role is to follow how learning proceeds and help when
necessary. For example, there are a lot of tools and collaborative learning plans for
multiplication tables.”
On the other hand, there were many comments which described how technology would be
able to support pupils :
“The technology should distribute exercises, grade them, correct errors, praise success,
and guide otherwise too. The teacher would get some kind of overview.”
“Using digital materials does not necessarily lead to an increase in distance education,
but creates also possibilities for individual work by providing direct feedback and
differentiation possibilities. At school, or at home.”
As described in the Introduction, a skilled teacher gradually decreases the amount of support
for learners to learn to regulate themselves. It is an interesting question, whether this kind of
fine-grained adaptive regulation support is expected from a machine. One comment included
the idea about developing students self-directed and self-regulated learning skills, but it was
left a bit unclear whether this development is seen as a side effect of individual studying with
technology or as a consequence of scaffolding by either teacher or technology:
“I think that while provocatively formulated, the idea is rather good. Many digital
tools fit really well to differentiation and e.g., vocabulary drilling. Pupils could and
should be guided to plan, test, and differentiate their learning themselves and, for
example, proceed to more advanced exercises or broader vocabulary when the basics
work out. [–]”
In another comment, it was suggested that self-regulatory skills could be practised with
technology, but there should be a human “backup”:
“[–] it’s worthwhile to let those with [self-regulatory] problems to have a chance
to practise at school. In short snippets and so that someone is there to return drifters
back to the track.”
There were no comments about technology being capable of directly helping with challenges
related to pupil diversity and equality. One comment even pointed out the contrary:
“Technology can help a lot in teaching, but will never be a substitute for teacher’s
help to those with challenges in studying - and it is an increasing crowd!”
However, some comments noted that technology could indirectly help by freeing up teachers’
time:
“This can also be seen in a way that if those competent in the independent study
are in remote learning, those who are unable may get tailored contact teaching.”
“Let’s teach just those who need teaching. Others might find learning even easier
alone on their own.”
In summary, in the posts that were supportive towards the vision presented in the opinion
piece, technology was seen as capable of guiding and supporting students with their self-
regulation. On the other hand, the critical posts mostly, rather than criticizing the technology,
emphasized the role of the teacher. It seems that the possibilities of technology in guiding and
supporting students are recognized to some extent, but technological scaffolding alone is not
considered sufficient. Therefore, the role of teacher is seen as necessary even with adaptive
learning technologies available.
4. Discussion
In line with previous work among different industries [5], teachers were not concerned of being
replaced be novel technologies. When compared to Selwyn’s [1] two scenarios of how AI may
change the teaching profession, some posts as well as the opinion piece that started the discussion
resemble Selwyn’s first scenario on AI freeing up teachers time for more meaningful activities.
However, in the critical posts, the threat of technology becoming a tool for performance
management (as in Selwyn’s second scenario) was not visible. The themes of technology
and teacher efficiency were not connected in the critical network. Instead, the critical posts
emphasized the role of teacher and human presence in facing student diversity and varying
levels of self-directedness and self-regulation. Only 29 % of the critical posts even mentioned
technological capability. This emphasis on human presence or “human touch” is in line with
previous work on employees’ perceptions among different industries [6].
In the supportive posts, on the other hand, 66 % mentioned technological capability. Even
these posts described the technological possibilities on a rather conservative level, at least if
compared to visions of hybrid human-system regulation [10]. For example, the possibilities of
learning analytics were mentioned only once.
Moreover, while ethical and legal issues related to collecting and using student data in various
applications are active topics in the learning analytics community, these themes were largely
absent in this discussion among education professionals.
4.1. Limitations
The present study has some limitations. First, the sample size is small with only 114 social
media posts. This is due to the fact that posts considering such a specific topic are scarce - we
were lucky to find this kind of discussion occurring spontaneously. In future work, alternative
data gathering methods such as surveys and interviews should be considered.
Second, a clear limitation in this kind of research setting is the question of representativeness.
We cannot objectively tell, how well the views presented in the analyzed posts represent the
views of Finnish education professionals in general. However, the Facebook groups in question
are rather large when taking into account the population of Finland, so at least a substantial
share of Finnish education professionals had a chance to take part in this discussion.
5. Conclusions
We analyzed 114 social media posts commenting on an opinion piece about technology partly
taking responsibilities usually attributed to a teacher. Our findings indicate that Finnish edu-
cation professionals mostly do not see adaptive learning technologies disrupting the teaching
profession.
There were some differences between supportive and critical posts. Supportive posts con-
nected technological capabilities and self-directed or self-regulated learning, emphasizing that
technology can also guide and support students. Critical posts connected human presence,
educational arrangements, and student diversity and equality, emphasizing the importance of
teacher’s presence in addressing pupils’ varying needs.
Our findings reveal themes relevant when discussing the development of adaptive learning
technologies and their potential impact on teaching profession. Moreover, the findings increase
the understanding of how supportive and critical argumentation on technology differ from each
other.
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