In this presentation, we present a concept for automatic learning guidance in relation to an information literacy curriculum.

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TraininG towards a society of data-saVvy inforMation
prOfessionals to enable open leadership INnovation
Angela Fessl, Alfred Wertner, Viktoria Pammer-Schindler
Challenges in Developing Automatic Learning
Guidance in Relation to an Information Literacy
Curriculum
AFEL Workshop @ EC-TEL 2018

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MOVING Project
TraininG towards a society of data-saVvy inforMation
prOfessionals to enable open leadership INnovation.
• 2016 – 2019
• Budget: 3,5€ millions
• Testbed partners
• Students
• Auditors from EY
This project has received funding from the
European Union’s Horizon 2020 research
and innovation action programme under
grant agreement No 693092

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MOVING Project
(https://moving.mz.tu-dresden.de)

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Information Literacy & Digital Competence
• Information literacy and the access to and use of knowledge are
becoming a precondition for individuals to actively take part in
social, economic, cultural and political life in societies of the 21st
century.
• Information literacy is a “fundamental competency” like the ability
to read, write and calculate.
• The UNESCO considers it “a basic human right”.
• American Library Association calls it a “survival skill in the
information age”.

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Curriculum
• Curriculum: Information Literacy und Digital Competency
• 3 different modules
• Searching information in digital environments
• Communication and collaboration in digital environments
• Content creation in digital environments
• Each module consists of sub-competences on different levels.

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Curriculum: Example
MODUL SUB-COMPETENCES LEARNING GOALS
SEARCHING
INFORMATION IN DIGITAL
ENVIRONMENTS
Browsing, searching and filtering
data, information and digital
content
to articulate information needs,
turn a (research) question into a
search strategy,
to search and access data,
information and content;
to create and update personal
search strategies;
Evaluating data, information and
digital content
to analyse, compare and critically
evaluate the credibility and
reliability of sources and the
data, information and content;
Managing data, information and
digital content
to organise, store, retrieve and
process data, information, and
content;

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Related Work
Open Learner
Model
Learning
Analytics
Reflection
Guidance
Automatic
Learning
Guidance
Recommender
Systems

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Related Work
Open Learner
Model
Learning
Analytics
Reflection
Guidance
Automatic
Learning
Guidance
Contains knowledge that
a computer has about a
user and is made
available to a user.
Stored data is used to
adapt systems to users,
for learning analytics and
for reflection
Open Learner
Model
Recommender
Systems

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Related Work
Open Learner
Model
Learning
Analytics
Reflection
Guidance
Automatic
Learning
Guidance
Researches methods
and usage of data
analysis and pattern
mining.
Provides support for
formal as well as informal
learners.
Learning
Analytics
Recommender
Systems

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Related Work
Open Learner
Model
Learning
Analytics
Reflection
Guidance
Automatic
Learning
Guidance
Reflection is the re-
evaluation of past
learning experiences to
learn from them.
Prompts (reflective
questions or sentence
starters) are a viable
mean to guide learning
through reflection.
Reflection
Guidance
Recommender
Systems

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Related Work
Open Learner
Model
Learning
Analytics
Reflection
Guidance
Recommender
Systems
Automatic
Learning
Guidance
Suggests items related to
the learning goals of a
user based on the users‘
learning activities.
Recommendations are
used to facilitate the
achievement of the
information literacy
curriculum.
Recommender
Systems

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Moving Platform
(https://moving.mz.tu-dresden.de)

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Automatic Learning Guidance Widget
Visualisation of the
learning status w.r.t. the
curriculum

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Automatic Learning Guidance Widget
Updating the
competence status
according the user’s
activities
Recommending learning
resources
Presenting reflective
questions

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Research Question 1:
• How accurately can users’ competence w.r.t. the
learning goal be assessed automatically?
Research Question 2:
• How can reflection prompts be phrased for different
learner competence status such that they can be
understood, are perceived as appropriate w.r.t. the
users’ expertise, and lead to reflection?
• How does the appearance of prompts need to be such
that they are not perceived as interruptive.
Challenges

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• Implementation of the widget in a search
platform
• Autum: experimental field studies with
social sciences university student to address
our two research questions.
Future Work

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Try out MOVING
https://moving.mz.tu-dresden.de

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PhD on „Technology Enhanced Learning“
• An opportunity to obtain a PhD at
Graz University of Technology
• Permanent employment
• Opportunities for professional and personal development
• Dynamic, creative and multi-cultural team
• Flexible working time and optimal conditions for personal work-
life balance
• http://www.know-center.tugraz.at/en/career/phd-with-focus-on-
technology-enhanced-learning/

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Thank you for your attention!
Any questions?
Project consortium and funding agency
MOVING is funded by the EU Horizon 2020 Programme under the project number INSO-4-2015: 693092