Activities and Trends Analytics in a Widget based PLE using Semantic
Technologies
Selver Softic
1
, Behnam Taraghi
1
and Laurens De Vocht
2
1
Department for Social Learning, Graz University of Technology, Graz, Austria
2
iMinds, Ghent University, Gent, Belgium
Keywords:
PLE, Semantic Web, Learning Analytics.
Abstract:
We report about work in progress on tracking the activities and trends of users from logs in a widget based
Personal Learning Environment (PLE) using semantic technologies and standards for retrieval. As input for
the observations, we are using the data from our self developed PLE with around 4000 active users. Last
two years we logged their activities and modeled them with RDF (Resource Description Framework)
as base
for improvement analysis of existing system. The main objective of this work is to outline how learning
environments like PLE can benefit from Semantic Web and its contribution for such efforts like analytics,
profiling, recommendations and usability.
1 INTRODUCTION
Emergence of the Web 2.0 (O’Reilly, 2005) intro-
duced participation of users as part of the web. The
transformation of internet from consuming into inter-
action medium goes hand in hand with the advances
of the web technologies. These changes also influ-
ences how we think, inform ourselves, organize our
everyday activities but also how we learn. Several re-
search studies have been carried out to analyze how
Web 2.0 applications such as Blogs (Farmer, 2005;
Holzinger et al., 2009; Ebner et al., 2007), Wikis (Au-
gar et al., 2004), Podcasting (Towned, 2005) as well
as Microblogs and Social Networks generally(Ebner
and Maurer, 2009; Ebner et al., 2010a) influence
users and can enhance education. Various studies on
Web 2.0 usage amongst students (Ebner and Nagler,
2010) outline how hard it is to follow the trends and
even more to monitor them in an appropriate way.
Mashups (Tuchinda et al., 2008) and personalization
can be used to manage this challenge in learning en-
vironments. Nowadays, with increasing number of
smart phones the online presence is getting more in-
tensive for a huge population of users. They share dif-
ferent resources and contribute to the Web with their
mobile devices. This trend applies also for teachers
and learners in context of E-Learning (Ebner et al.,
2008). Those activities consider also Web 2.0 appli-
http://www.w3.org/RDF/
cations and services raised like YouTube (for shar-
ing Videos), Flickr (for sharing pictures), Slideshare
(for sharing presentations), Scribd (for sharing docu-
ments), Delicious (for sharing bookmarks) etc. The
huge amount of such applications and their usage
in learning and teaching has changed the online be-
havior and attitude of learners in respect to the new
arising technologies (Downes, 2005). This led to
the idea of Personal Learning Environment (PLE),
where tiny applications (widgets) can be integrated
and combined within a learning environment man-
aged by the learners according to their actual personal
needs. Such approach resembles to the mobile ap-
plication environments in many ways, i.e. a widget
store is offered where the learners can install widgets
on one or many spaces or personal desktops. Due
to the fact that mobile technologies and social web
are available ubiquitously as well pervasively used,
they have influence our every day life and learning
environments (Holzinger et al., 2005; Klamma et al.,
2007). It is quite challenging for education not to
be overwhelmed by all these various opportunities
within a learning environment. Todays learning pro-
cess became more individual, multi facetted and ac-
tivity driven with the tendency to ad-hoc initiated col-
laboration and information exchange. All these pa-
rameters increase the complexity of online learning
platform design and organization. Dynamics involved
in this process require nowadays shorter optimization
cycles in adaptation process of Learning Management
199
Softic S., Taraghi B. and De Vocht L..
Activities and Trends Analytics in a Widget based PLE using Semantic Technologies.
DOI: 10.5220/0004414701990203
In Proceedings of the 5th International Conference on Computer Supported Education (CSEDU-2013), pages 199-203
ISBN: 978-989-8565-53-2
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: PLE Desktop at Graz University of Technology.
Systems and Personal Learning Environments. In or-
der to provide the learners an attractive surrounding
and to tackle the named problems use of learning an-
alytics regarding activities and trends for optimization
of learning process and design of learning surround-
ing emerges as the time passes by. Such data con-
tributes to the personalization and adaptation of the
learning process and belonging hosting environments.
For these purposes we logged in anonymous way user
data regarding activities on widgets in our PLE for
last two years and modeled them into learning context
using RDF in order to generate statistics that would
help us to improve the concept of the widget based
PLE. For querying the the context we used semantic
retrieval standard SPARQL
2
.
In following sections we will offer a short overview
over related work and explain how we managed to
model the activities in order to track them. We will
also show some preliminary results on current pro-
duction system. This paper will be concluded by the
discussion about the first results and some future work
announcements.
2 RELATED WORK
The main idea of using and developing a widget based
PLE at Graz University of Technology
3
was to com-
bine and integrate existing university services (Ebner
and Taraghi, 2010) as well as resources and services
on the World Wide Web in one platform and in a per-
sonalized way (Ebner and Taraghi, 2010). It bases
on meshup of widgets (Taraghi et al., 2009a; Taraghi
et al., 2009b; Taraghi et al., 2009c) that represent
the resources and services integrated from the World
Wide Web within the PLE. On the other hand Web
provides lots of different services; each can be used
as supplement for teaching and learning. The PLE
has been redesigned in 2011, using metaphors such
as apps and spaces for a better learner-centered appli-
cation and higher attractiveness (Ebner et al., 2010b;
2
http://www.w3.org/TR/rdf-sparql-query/
3
http://ple.tugraz.at
Taraghi et al., 2012). A sample of PLE Desktop with
Widgets can bee seen in figure 1. The PLE has been
running since two years. In order to enhance PLE in
general and improve the usability as well as useful-
ness of each individual widget a tracking module was
implemented (Taraghi et al., 2011). Different works
outlined the importance of tracking activity data in
Learning Management Systems (Santos et al., 2012;
Verbert et al., 2011). None of them addressed the is-
sue of intelligently structuring learner data in context
and processing it to provide a flexible interface that
ensures maximum benefit from collected information.
The Semantic Web standards like RDF and SPARQL
where data is structured and queried as graphs and
projected on specific knowledge domain using ade-
quate ontologies has been fairly successful used to
generate correct interpretation of web tables (Mulwad
et al., 2010) to advance the learning process (Prinsloo
et al., 2012; Jeremi
´
c et al., 2012) as well to support
the controlled knowledge generation in E-learning en-
vironments(Softic et al., 2009). The retrieval standard
provided by Semantic Web named SPARQL enables
easily querying of semantically enriched data. This
potential was also recognised by current research in
the EU project Intelligent Learning Extended Organ-
isation (IntellLEO)
4
which produced in the published
ontology framework: ActivitiesOntology
5
to model
learning activities and events related to them along
with the surrounding environment and Learning Con-
text Ontology
6
which offers formalization of learning
context as general learning situation. Due to their ac-
curacy to the problem that is addressed by this work
these ontologies have been used to model the context
of analytic data collected from user logs in this work.
Our method is based on a tracking model as a
knowledge domain related context using ontologies
and query languages like SPARQL similar to cur-
rent research in the area of Self-regulated Learn-
ers(SRL)(Jeremi
´
c et al., 2012). Exploratory graph-
ics show that the sum of (web) user data on the ac-
cess paths and the linkage of the resources within
an environment(site) at a particular time window
gives sufficient insight at what constitutes relevance;
important properties and linkages between data re-
sources(Siadaty et al., 2011). The overall goal of
is summarization of visualizations and evaluation of
statistic data that enable the PLE optimization and
present the research community used generic tech-
niques and metrics for problems in design and adap-
tation of learning environments.
4
http://intelleo.eu
5
http://www.intelleo.eu/ontologies/activities/spec/
6
http://www.intelleo.eu/ontologies/learning-context/spec/
CSEDU2013-5thInternationalConferenceonComputerSupportedEducation
200
3 ANALYTICS FROM LEARNERS
LOGS
3.1 Modelling User Activities
The main objective for tracking is appropriate mod-
elling since RDF offers only the framework how the
data is aligned and organized in such constructions.
This task concerns mostly the choice of the right
vocabulary or ontology. A bunch of experience is
usually necessary to complete such effort especially
when ontology has to be designed on your own. In
current research in IntellLEO EU project however
this objective has been practically implemented. One
of the main goals of this project according to the
statement from project page is building an innovative
ontological framework for learning representation
which includes learners, context and collaboration
models, serving to achieve the targeted synergy
7
.
In the realm of the IntellLEO project inside the
provided ontology framework two special ontologies
are eminent for current work. The first is the Activity
Ontology which offers a vocabulary to represent
different activities and events related to them inside
of a learning environment with possibility to describe
and reference the environment (in this case PLE)
where these activities occur. The second contribution
from current Ontology research work in IntellLEO
project is the Learning Context Ontology which
describes the context of a learning situation. We
used as it will be shown in following sections this
Ontology to reflect the user activity context.
Formulation in listing 1 depicts an instance of
lc:LearningContext class in compact N3 RDF No-
tation derived from our tracking module that stores
them into a RDF Store and makes them accessible
via relying SPARQL Endpoint. Translated into nat-
ural language this instance from listing 1 reflects that
a ao:Logging event which tracked the learning ac-
tivity of ao:Viewing by certain anonymous um:User
inside the learning widget named LatexFormulaToP-
ngWidget as ao:Enviroment at certain time point. As
shown in this sample modeling example vocabularies
and ontologies which fit appropriate for the special
case can enable a high level of expressiveness in a
very compact manner.
3.2 Querying of User Activities Trends
In order to retrieve the data relevant for the analytics
RDF instances that reside in a RDF Store at our PLE
7
http://intelleo.eu/index.php?id=5
Listing 1: Sample model of a learning context in N3 nota-
tion.
@prefix ao: <http://intelleo.eu/ontologies/activities/ns/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix lc: <http://www.intelleo.eu/ontologies/learningcontext/ns/> .
@prefix rdfs: <http://www.w3.org/2000/01/rdfschema#> .
@prefix um: <http://intelleo.eu/ontologies/usermodel/ns/> .
<https://ple.tugraz.at/ns/activity/#Viewing>
a ao:Viewing .
<https://ple.tugraz.at/ns/users/#FSKSN>
a um:User;
foaf:name ”FSKSN” .
<http://ple.tugraz.at/ns/events/log/#7912>
a ao:Logging;
ao:performedBy <https://ple.tugraz.at/ns/users/#FSKSN>;
ao:timestamp ”20121004T07:52:52” .
<https://ple.tugraz.at/ns/widgets/#LatexFormulaToPngWidget>
a ao:Enivironment;
rdfs:label ”LaTeXFormulaPNG Converter” .
<http://ple.tugraz.at/ns/learningcontext/#7912>
a lc:LearningContext;
lc:activityRef <https://ple.tugraz.at/ns/activity/#Viewing>;
lc:environmentRef
<https://ple.tugraz.at/ns/widgets/#LatexFormulaToPngWidget>;
lc:eventRef <http://ple.tugraz.at/ns/events/log/#7912>;
lc:userRef <https://ple.tugraz.at/ns/users/#FSKSN> .
environment SPARQL query language has been used.
Operability over the data is much easier then in the
case if the log data would be stored in specific struc-
ture without standardization. In this way we are able
to answer the questions like ”Show me the a monthly
activity intensity for year 2012?”. Listing 2 represents
exactly the question stated in the manner of SPARQL
syntax. The advantage of this approach is that data
formulations are flexible and tolerant against any ex-
tension of representation schema, which means that
adding supplementary properties would not change
the expressiveness and retrieval of already existent in-
formation. Further since Semantic Web support the
Open World Assumption (OWA), an answer whether
or not is something reproducible out of the knowledge
base is guaranteed.
4 PRELIMINARY RESULTS
As preliminary result we are able to track the ac-
tivity trends overall time periods like presented in
figure 2. This violin graph depicts the visual an-
swer of the query from listing 2. We can see
that for year 2012 top three favored activities were
”Reading”,”Search” and ”Authoring” while activi-
ties like ”Quizzing”,”Computing”and ”Listening” are
least frequent ones. Also the intensity shows that as
expected that most activity happens at the beginning
and at the end of academic terms when PLE is pre-
sented in introductory lectures to the newcomers and
freshmen.
ActivitiesandTrendsAnalyticsinaWidgetbasedPLEusingSemanticTechnologies
201
Listing 2: Querying the intensity of all activities in PLE
after certain date.
PREFIX ao: <http://intelleo.eu/ontologies/activities/ns/> .
PREFIX foaf: <http://xmlns.com/foaf/0.1/> .
PREFIX lc: <http://www.intelleo.eu/ontologies/learningcontext/ns/> .
PREFIX rdfs: <http://www.w3.org/2000/01/rdfschema#> .
PREFIX um: <http://intelleo.eu/ontologies/usermodel/ns/> .
SELECT ?actname, COUNT(?actname)
WHERE
{
?x rdf:type
lc:LearningContext;
lc:activityRef ?a;
lc:eventRef ?e;
lc:userRef ?u.
?e rdf:type
ao:Logging;
ao:timestamp ?date.
?a rdf:label ?actname;
FILTER ( ?date > ”20120101T00:00:00Z”ˆˆxsd:dateTime )
}
Figure 2: Tracking the intensity of activities for year 2012.
We have chosen this example to demonstrate the
usefulness of semantic approach in a very simple case
where a vast of e.g. usability and improvement input
can be generated with a simple query and a visual-
ization interface. Demonstration like this reveals in a
very efficient manner which potentials are hidden in
appliance of Semantic Web for learning analytics.
5 CONCLUSIONS AND FUTURE
WORK
The overview over distribution of activities can re-
flect the overall interest of the learners within PLE.
It can be concluded that in case of our PLE users
are more consumers that contributers. Visualisation
of statistics can help to improve the PLE usability
in general. Activities such as e.g. ”Quizzing” and
”Listening” (from some learning object widgets) are
not quite popular. Corresponding widgets that sup-
port those activities must be revised regarding usabil-
ity. The statistics visualisation help us to gain deep
insight into the behaviour of a single user in a certain
period of time. In this simple case we demonstrated
that using semantic technologies enables the extensi-
bility of learning analytics. Our approach generates
uniform interfaces for information exchange, enables
flexibility for visual evaluation, and also includes the
scalability regarding the enrichment of learning ana-
lytics data, since it is tolerable because of the RDF
to the schema changes. Our future efforts are aiming
the improvement of semantic structure data layer in
order to reflect as many aspects as possible. We also
want to review our widget store regarding the gen-
erated results in order to decide which widgets will
further provided in our PLE and which of them need
to be re-factored.
ACKNOWLEDGEMENTS
The research activities that have been described in this
paper were funded by Graz University of Technology,
Ghent University, iMinds (an independent research
institute founded by the Flemish government to stim-
ulate ICT innovation), the Institute for the Promotion
of Innovation by Science and Technology in Flan-
ders (IWT), the Fund for Scientific Research-Flanders
(FWO-Flanders), and the European Union.
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