=Paper=
{{Paper
|id=Vol-2209/paper6
|storemode=property
|title=Reflections on the Workshop
|pdfUrl=https://ceur-ws.org/Vol-2209/paper6.pdf
|volume=Vol-2209
|authors=Angela Fessl,Stefan Thalmann
|dblpUrl=https://dblp.org/rec/conf/ectel/FesslT18
}}
==Reflections on the Workshop==
https://ceur-ws.org/Vol-2209/paper6.pdf
Reflections on the Workshop
Angela Fessl1, and Stefan Thalmann1,2
1 Know-Center GmbH, Inffeldgasse 13, 8010 Graz, Austria
2 Institute for Interactive Systems and Data Science, Graz University of Technology, Austria
{afessl,thalmann}@know-center.at
Abstract. In this paper we shortly summarize the discussions and outcomes of
the first workshop on Analytics for Everyday Learning co-located with the 13th
European Conference on Technology Enhanced Learning in Leeds (UK) on the
4th September 2018. First we will briefly summarize the discussions for each of
the five papers and finally synthetize the most important findings.
Keywords: Learning Analytics, Everyday Learning.
1 Introduction
The workshop on “analytics for everyday learning” brought together researchers,
practitioners, educational developers, entrepreneurs and policy makers from different
backgrounds and provided a forum for discussing the multi-faceted area of analytics
for everyday learning. The papers presented on the AFEL workshop centered around
learning analytics in the context of everyday learning on many levels and in different
contexts.
The paper by S.S.E. Zainab and M. d’Aquin titled “Detection of Online Activity
Scope” [1] reported about a clustering approach on how to identify key areas based on
a learner’s online activities using a browser. The browser activities including the used
resources are tracked, afterwards these resources are analyzed and enriched to create a
profile of the resource. This enriched information is then used for clustering the re-
sources and to identify the most representative cluster of the user. The most interesting
discussion on this topic focused on the clustering itself, and if this clustering should be
performed for every learner individually or for groups of learners.
The paper by Yenikent et al. titled “Evaluating the AFEL Learning Tool: Didactalia
Users’ Experiences with Personalized Recommendations and Interactive Visualiza-
tions” [2] presented an experimental study of a learning application for everyday learn-
ing. By analyzing the learning activities of learners, the learning scopes and trajectories
of the learner were analyzed. After having presented the study and the corresponding
results to the audience a discussion about how tools for everyday learning can be eval-
uated has arisen.
The paper by Fessl et al. titled “Challenges in Developing Automatic Learning Guid-
ance in Relation to an Information Literacy Curriculum” [3] presented an automatic
learning guidance widget integrated in a learning and training platform. The goal of the
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widget is to track and present the learning progress of an individual user of the platform
with regard to a defined curriculum. Here, the discussion focused on how to track the
user’s learning progress with the help of activity log data. Suggestions were on using
different heuristics, like for example opening a page relevant for the curriculum, the
average stay on the page, and if the user is scrolling down the document to the end.
Although all suggestions are meaningful, they also have some disadvantages. For ex-
ample, although the average stay on a page might be useful, it cannot be ensured that
the user opening a page is still in front of the screen.
Paper four by Fessl et al. titled “Analytics for Everyday Learning from two Perspec-
tives: Knowledge Workers and Teachers.” [4] reported about the results of two con-
ducted focus group interviews on the effects of a learning resource recommender sys-
tem and a dashboard based on analytics for everyday learning. The first focus group
focused on knowledge workers as self-regulated everyday learners (i.e., informal learn-
ing) and the second focus group dealt with teachers who serve as instructors for learn-
ers. The workshop participants were interested in the recommender as recommending
relevant learning resources is always an important feature for learning. Especially, in
informal learning scenarios it is not that easy to provide the right learning resources at
the right time. Furthermore, another topic of interest was the diversity of resources, thus
how diverse such recommendations can be in order to be still useful for the learner.
The last paper by Mutlu et al. titled “Towards a Learning Dashboard for Community
Visualization.” [5] presented a learning dashboard for community visualizations. The
dashboard serves the purpose of (statistically) analyzing and exploring the behavior of
communities and users. The discussion centered on using such dashboards for informal
vs. formal learning. While in formal learning, learning activities are often conducted in
learning environments, the collected activity data in such environments is easy to use
for providing meaningful visualizations. In contrast, in informal learning settings this
is not the case as there is often no learning environment available and the learning ac-
tivities – if they are somehow extracted – consist of different types of data not known
beforehand – thus is it not that easy to provide general applicable visualizations for all
types of data.
In the following we will summarize the most important outcomes of the discussions.
1. Learning Analytics helps to organize everyday learning:
Informal learning and workplace learning are established research fields in the do-
main of technology enhanced learning (TEL) focusing primary on the learning needs
of employees in their business processes. However, learning takes place everywhere
and also outside of professional contexts in everyday situations. Everyday learning as
self-steered and curiosity-driven learning will become more and more important as part
of life-long learning and personal development in future as knowledge and information
develop rapidly. However, due to the rising complexity of available learning resources,
support for self-organization and reflection about the own learning behavior seems use-
ful. Inspired by the “quantify yourself” trend and current applications in sports and
dietary, learning analytics can offer promising approaches to better organize personal
learning.
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2. Everyday learning becomes more important:
Everyday learning becomes more and more important as learners, educators,
knowledge workers, professionals etc. need to stay-up-to date for their daily learning
and working activities. As technology evolves rapidly, continuous everyday learning in
fast changing environments will become a crucial part of the personal development. An
important aspect in this regards is the blurring of boarders between private and profes-
sional life, making the distinction between private and professional learning more dif-
ficult. Hence, the main conclusion we draw in the workshop was, that new concepts of
work also require new concepts of learning and that taking everyday learning serious
could be one answer.
3. Tool support for everyday learning:
There exist different approaches on how everyday learning can be supported. For
example, learning analytics provides mechanisms for analyzing digital traces to support
learners with regard to their learning goals, learning progress or learning strategies.
Data-driven reflective learning is an approach to re-evaluate past experiences with the
goal to improve future behavior. Furthermore, there already exist manifold technolo-
gies and tools that imitate everyday learning without recognizing it as „learning tool or
technology“. For example, gamification approaches that motivate for learning in lan-
guage learning platforms (Duolingo), or tools that automatically give you an overview
of your working or learning activities depending on the browser history, or tools that
provide you guidance to improve your search behavior.
4. Ethical issues around analytics for everyday learning
Analytics for everyday learning requires a holistic tracking of user interactions. This
leads to a comprehensive data set about users and also bear the risk of misuse. Espe-
cially as data about the everyday learning can be considered highly sensitive. Hence,
data privacy and misuse by employers or other stakeholders need to be restricted. We
further discussed the challenge of evaluating tools for learning analytics by using real
user data.
Acknowledgement
The project “AFEL – Analytics for Everyday Learning” is funded under the Horizon
2020 of the European Commission (project number 687916). The Know-Center is
funded within the Austrian COMET Program - Competence Centers for Excellent
Technologies - under the auspices of the Austrian Federal Ministry of Transport, Inno-
vation and Technology, the Austrian Federal Ministry of Economy, Family and Youth
and by the State of Styria. COMET is managed by the Austrian Research Promotion
Agency FFG.
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References
1. Zainab, S.S.E., D’Aquin, M. Detection of Online Activity Scope. In Proceedings of the 1th
Workshop on Analytics for Everyday Learning co-located with the 13th European Confer-
ence on Technology Enhanced Learning (EC-TEL 2018), Leeds, UK. (2018).
2. Yenikent, S., Holtz, P., Thalmann, S., D’Aquin, M., Kimmerle, J. Evaluating the AFEL
Learning Tool: Didactalia Users’ Experiences with Personalized Recommendations and In-
teractive Visualizations. In Proceedings of the 1th Workshop on Analytics for Everyday
Learning co-located with the 13th European Conference on Technology Enhanced Learning
(EC-TEL 2018), Leeds, UK. (2018).
3. Fessl, A., Wertner, A. Pammer-Schindler, V. Challenges in Developing Automatic Learning
Guidance in Relation to an Information Literacy Curriculum. In Proceedings of the 1th
Workshop on Analytics for Everyday Learning co-located with the 13th European Confer-
ence on Technology Enhanced Learning (EC-TEL 2018), Leeds, UK. (2018).
4. Fessl, A., Kowald, D., López, S.S., Moreno, A., Maturana, R. A., Thalmann, S. Analytics
for Everyday Learning from two Perspectives: Knowledge Workers and Teachers. In Pro-
ceedings of the 1th Workshop on Analytics for Everyday Learning co-located with the 13th
European Conference on Technology Enhanced Learning (EC-TEL 2018), Leeds, UK.
(2018).
5. Mutlu, B., Sabol, V., Cicchinelli, A., Veas, E., Simic, I. Towards a Learning Dashboard for
Community Visualization. In Proceedings of the 1th Workshop on Analytics for Everyday
Learning co-located with the 13th European Conference on Technology Enhanced Learning
(EC-TEL 2018), Leeds, UK. (2018).