1.
Using AI to understand
everyday learning on the Web
Prof. Dr. Stefan Dietze
GESIS / Heinrich Heine University (Germany)
08.11.2018, Digital Enlightenment Forum,
8 November 2018, Brussels

2.
Learning on the Web
08/11/18 2Stefan Dietze

3.
Learning Resources on the Web? - LinkedUp Catalog
Dataset
Catalog/Registry
http://data.linkededucation.org/linkedup/catalog/
 “LinkedUp” (FP7 project): L3S, OU, OKFN, Elsevier, Exact Learning Solutions
 Publishing and curation of educational/learning resources according to Linked Data principles
 Largest collection of Linked Data about learning resources
(approx. 50 datasets, 50 M resources)
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4.
08/11/18 4Stefan Dietze
 Anything can be a learning resource
(when used for „informal, „just in time“,
micro-learning)
 The activity makes the difference (not the
resource): i.e. how a resource is being used
 Challenges:
o How to detect „learning“?
o How to detect learning-specific notions
such as „competence“, „learning
performance“ etc?
 Learning Analytics in online/non-learning
environments?
o Activity streams (e.g. Twitter),
o Social graphs (and their evolution),
o Behavioural traces (mouse movements,
keystrokes)
o ...
Analytics for everyday (online) learning? (as opposed to “education”)
Figure courtesy of Mathieu d‘Aquin

5.
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Analytics for everyday (online) learning? (as opposed to “education”)
 AFEL: H2020 project (since 12/2015) aimed at understanding/supporting learning in social Web environments
 SALIENT: Leibniz project with various German research labs and educational organisations
 Learning efficiently on the Web = finding reliable
and relevant information for a particular
topic/learning need
 „Learning to learn“ = supporting learners/users
to find information efficiently
Figure courtesy of Mathieu d‘Aquin

6.
08/11/18 6Stefan Dietze
Learning while searching the Web (“Search As Learning”)?
Challenges & results
 Detecting coherent search missions?
 Detecting learning throughout search?
detecting “informational” search missions (as
opposed to “transactional” or “navigational”
missions [Broder, 2002])
o Search mission detection with average F1
score 75% (experiments based on AOL query
logs, [CHIIR19])
 How competent is the user? –
Predict/understand knowledge state of users in
absence of assessment data
 How well does a user achieve his/her learning
goal/information need? - Predict knowledge gain
throughout search missions
o Correlation of user behavior (queries,
browsing, mouse traces, etc) with user
knowledge gain/state in various search tasks
[CHIIR18]
o Prediction of knowledge gain/state through
supervised models [SIGIR18, WWW19]

7.
Predicting knowledge gain/state of user during search?
08/11/18 7Stefan Dietze
 Prediction through supervised machine learning models
(after 10-fold cross-validation)
 KG prediction performance
 Feature importance (KG prediction)
Ran Yu, Ujwal Gadiraju, Peter Holtz, Markus Rokicki, Philipp
Kemkes and Stefan Dietze. Analyzing Knowledge Gain of Users in
Informational Search Sessions on the Web. ACM SIGIR 2018.
Key findings
 User knowledge gain / state can be predicted
from user behavior during search missions
 In particular browsing behavior and queries of
importance
 Ongoing work: investigating resource features
(e.g. document complexity, analytic/emotional
language tone, multimodality of resources) as
additional signals
 Turning such models into actual applications
=> SALIENT & AFEL project

8.
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Outlook: supporting learning in online platforms
 Search as part of domain-specific and cross-domain
online platforms (“bringing learning analytics to
platforms where users learn on daily basis”)
 Examples (SALIENT & AFEL project)
o Didacatalia – social online community of approx.
200.000 users
o gesisDataSearch search of > 100.000 research
datasets in the social sciences
o Social Sciences Open Access Repository (SSOAR) –
online archive of social sciences literature
o TIB AV Portal – lecture videos and tutorials
provided by the German National Library of
Science & Technology
http://GNOSS.com
http://datasearch.gesis.org
https://www.gesis.org/ssoar/home/
https://av.tib.eu/

9.
08/11/18 9Stefan Dietze
http://stefandietze.net
http://gesis.org
http://hhu.de
http://l3s.de
https://uebermorgen.haz.de/2018/08/kuenstliche-intelligenz/