=Paper=
{{Paper
|id=None
|storemode=property
|title=A Mobile and Adaptive Language Learning Environment based on Linked Data
|pdfUrl=https://ceur-ws.org/Vol-717/paper13.pdf
|volume=Vol-717
|dblpUrl=https://dblp.org/rec/conf/esws/DeursenJWTLPMW11
}}
==A Mobile and Adaptive Language Learning Environment based on Linked Data==
https://ceur-ws.org/Vol-717/paper13.pdf
A Mobile and Adaptive Language Learning
Environment based on Linked Data
Davy Van Deursen1 , Igor Jacques2 ,
Stefan De Wannemacker2 , Steven Torrelle1 , Wim Van Lancker1 ,
Maribel Montero Perez2 , Erik Mannens1 , and Rik Van de Walle1
1
Ghent University - IBBT,
Gaston Crommenlaan 8/201, B-9050 Ledeberg-Ghent, Belgium
firstname.lastname@ugent.be
2
ITEC - Interdisciplinary research on Technology, Education and Communication,
K.U. Leuven Campus Kortrijk, Etienne Sabbelaan 53, B-8500 Kortrijk, Belgium
firstname.lastname@kuleuven-kortrijk.be
Abstract. The possibilities within e-learning environments increased
dramatically the last couple of years. They are more and more deployed
on the Web, allow various types of tasks and fine-grained feedback, and
they can make use of audiovisual material. On the other hand, we are
confronted with an increasing heterogeneity in terms of end-user de-
vices (smartphones, tablet PCs, etc.) that are able to render advanced
Web-based applications and consume multimedia content. Therefore, the
major contribution of this paper is an adaptive, Web-based e-learning
environment that is able to provide rich, personalized e-learning expe-
riences to a wide range of devices. We discuss the global architecture
and data models, as well as how the integration with media delivery can
be realized. Further, we give a detailed description of a reasoner, which
is responsible for the adaptive selection of learning items, based on the
usage environment and the user profile.
Keywords: Adaptive, Language Learning, Mobile, Web-based
1 Introduction
The last years, the use of e-learning environments has increased spectacularly,
not only in formal educational settings, but also in working and private environ-
ments. At the same time, the possibilities within these e-learning environments
increased dramatically: learning environments for instance have become easier
and more pleasant to use, they allow various types of tasks and fine-grained feed-
back, and they can make use of audiovisual material. Moreover, while e-learning
environments were traditionally offered as applications on stand-alone comput-
ers, nowadays they are more and more being rendered over the Internet. It is
clear that these evolutions are related to technological evolutions, and the wide
availability of fast multimedia computers and internet access.
Next to the fact that e-learning environments are more and more deployed
over the Web, we are confronted with an increasing heterogeneity in terms of
end-user devices that are able to connect to the Web and consume multimedia
content. Therefore, personal devices such as tablet PCs and smartphones could
be used as learning devices, next to traditional desktop and laptop devices. Also,
the role of personalization within e-learning environments has become more and
more important. Personalization can be applied both at the learning level (i.e.,
adjust learning sessions according to the learner’s capabilities) and at the envi-
ronmental level (i.e., adjust the rendering of the learning environment according
to the characteristics of the usage environment).
The above described challenges are exactly the ones that are currently tack-
led in the IBBT MAPLE project (Mobile, Adaptive & Personalized Learning
Experience3 ), which aims to make adaptive mobile e-learning possible. There-
fore, in this paper, we present a Web-enabled e-learning environment that is
able to offer personalized learning sessions on any device, primarily focused on
language learning making optimal use of digital multimedia. In order to realize
such an environment, we need the following key components:
– a common, machine-understandable data model that is independent of usage
environments and is able to express both learning content and metadata
about the learning content;
– a logging framework that allows to capture the behaviour and performance
of the learner on a detailed level;
– a reasoner that is able to select learning items based on the learner’s capa-
bilities and behaviour;
– a media delivery platform taking into account usage environment character-
istics and restrictions.
In the remainder of this paper, we provide an overview of the architecture
of our adaptive e-learning platform. Further, we discuss the above described key
components in more detail. Finally, we discuss related work, draw a number of
conclusions, and discuss some future work.
2 MAPLE platform
In order to offer a highly adaptive e-learning platform that can also deal with
(mobile) multimedia delivery, we designed the architecture that is depicted in
Fig. 1. Two major parts can be distinguished: the e-learning platform and the
media delivery platform. The e-learning platform relies on two RDF stores, i.e.,
a store for learning exercises and a store for learner profiles. The learning items
store is filled through the learning item ingest service. More details regarding
the creation of learning items and the data model according to which they are
modeled are provided in Section 3. Further, the learner profile store is build up,
based on the learners’ actions and preferences (see Section 3.5). The reasoner
is responsible for selecting the most adequate exercise, based on the learner’s
profile and environment and the available learning items. Detailed information
3
http://www.ibbt.be/en/projects/overview-projects/p/detail/maple-2
media delivery platform
media
delivery
selection channels
media &
ingest media store packaging
service ADTE
service
learning
item ingest learning item reasoner
service DB
learning
endpoint
learner profile
DB
e-learning platform
Fig. 1. The MAPLE e-learning platform
regarding the reasoner is provided in Section 4. Finally, the learning endpoint is
the communication point between learner devices and the e-learning platform.
The media delivery platform corresponds to NinSuna4 , which is a metadata-
driven media adaptation and delivery platform [25]. At its core, format-independent
modules for temporal selection and packaging of media content are present. Al-
most all existing media delivery channels are supported by NinSuna: RTSP,
RTMP, HTTP progressive download, and HTTP adaptive streaming. Moreover,
native support for Media Fragments 1.0 [24] is provided, which enables the de-
livery of media fragments (i.e., temporal or track fragments) in a standardized
way [15]. Finally, NinSuna comes with an Adaptation Decision-Taking Engine
(ADTE), which is able to 1) detect the capabilities of the device issuing the
request and 2) take a decision regarding which quality version of the requested
media resource is the most adequate for the detected device. A more detailed
description of the NinSuna platform can be found in [25].
The presented e-learning platform exposes its data (i.e., learning content and
accompanying media resources) as linked data. More specifically, it follows the
guidelines regarding the publication of linked data5 : use dereferencable HTTP
URIs as names for things, provide useful information using the standards (RDF,
SPARQL), and include links to other URIs. Hence, within our platform, the
learning items and learner profiles are available through a SPARQL endpoint,
while the metadata of the media resources are published as RDF URIs. This
way, services such as the reasoner and the ADTE can rely on the linked data
and can start reasoning over it.
4
http://ninsuna.elis.ugent.be
5
http://www.w3.org/DesignIssues/LinkedData.html
A typical e-learning scenario using this architecture is then as follows:
(1) the learner logs in into the Web-based e-learning application using its mobile
device, which contacts the learning endpoint of the e-learning platform; the
end point approaches the reasoner which provides a personalized overview
of the available courses;
(2) based on the course selected by the learner, the reasoner selects an exercise
from the learning item store, taking into account the learner profile and the
available exercises within that course;
(3) when the selected exercise contains media content (audio, video, or images),
the ADTE of NinSuna is contacted in order to select the media resource
version that fits best for the current device;
(4) the learning endpoint renders the selected exercise in HTML and sends the
response to the learner;
(5) when the learner is solving the selected exercises, his/her answers and his/her
behaviour in terms of clicks and timing is logged and sent back to the e-
learning platform;
(6) the received answers and behaviour information are used to update the
learner’s profile.
In the next sections, more detailed information regarding a number of com-
ponents in the architecture is provided.
3 Data Models and Instance Generation
A number of different data models need to be developed in order to structure
and define the content used on the e-learning platform. More specifically, we
need the following data models:
– model for the learning items and their metadata (e.g., question, possible
answers, difficulty level);
– model for the learning domain;
– model for the metadata of the media resources (e.g., bit rate);
– model for the learner profile;
– model for the logging.
In the following subsections, we provide more information regarding these dif-
ferent models and how they are populated. Note that all ontologies are modelled
in OWL and published online.
3.1 Model for learning items and their metadata
The model for learning items consists of two ontologies: one for the learning items
themselves6 and one for their metadata7 . An example instance of a learning
6
http://multimedialab.elis.ugent.be/organon/ontologies/maple/content
7
http://multimedialab.elis.ugent.be/organon/ontologies/maple/llomp
Listing 1.1. Representing a learning item and its metadata in RDF (in Turtle).
1 @prefix mplc : < http :// multimedialab . elis . ugent . be / organon / ontologies /
maple / content # >.
@prefix llomp : < http :// multimedialab . elis . ugent . be / organon / ontologies /
maple / llomp # >.
@prefix xsd : < http :// www . w3 . org /2001/ XMLSchema # >.
@prefix dc : < http :// purl . org / dc / terms / > .
5
< http :// ninsuna . elis . ugent . be / rdf / resource / maple / blcc_47363 >
a llomp : Exercise ;
dc : title "47363" ;
mplc : exerciseType mplc : Multiple Choice ;
10 mplc : media < http :// ninsuna . elis . ugent . be / Media / Maple / FLAA2V0 # t =0 ,19 > ;
mplc : task " What do Belgians eat , according to the reporter ?" @en ,
" Wat eten de Belgen volgens de reporter ?" @nl ;
mplc : answerSpace " Les Belges mangent ..." ;
mplc : input [
15 a mplc : Input ;
mplc : answer [
a mplc : Choice ;
mplc : isCorrect " false "^^ xsd : boolean ;
mplc : scoreCorrect "0"^^ xsd : int ;
20 mplc : scoreFalse "0"^^ xsd : int ;
mplc : text " des frites " @fr .
] ;
mplc : answer [
a mplc : Choice ;
25 mplc : isCorrect " true "^^ xsd : boolean ;
mplc : scoreCorrect "1"^^ xsd : int ;
mplc : scoreFalse "0"^^ xsd : int ;
mplc : text " de la glace " @fr .
] .
30 ] ;
mplc : maxScore "1" ;
mplc : minScore "0" ;
llomp : educational [
35 a llomp : Educational ;
llomp : difficulty llomp : medium ;
llomp : level llomp : A2 ;
llomp : l e a r n i n g C o m p o n e n t : l e a r n i n g C o m p o n e n t _ 4 4 8 5 4 .
] ;
40 llomp : lifeCycle : l if eC y cl e_ 47 3 63 .
item modelled according to our model is shown in Listing 1.1. We explain and
illustrate both ontologies based on this example. The model is heavily based
on the Learning Object Metadata (LOM, [2]). LOM specifies a conceptual data
scheme and the corresponding XML-binding for metadata of learning items.
We started from LOM and defined a number of extensions in order to provide
improved support for learning subject, feedback and scoring, as well as better
integration with media resources. Further, as mentioned before, we split our
model between learning items and their metadata.
We describe not only the metadata of learning items, but also the exer-
cises themselves. This way, they are formally represented, independent of any
rendering. Moreover, they can be easily integrated with their metadata and cor-
responding media resources. Also, the reasoner (Section 4) will not only rely on
the learning item metadata, but also on the items themselves (e.g., this type of
exercise is preferred by the learner). For the moment, six mplc:exerciseTypes
are supported (focussed on language learning):
– Multiple Choice: given a number of answers, the learner has to choose exactly
one answer;
– Multiple Response: given a number of answers, the learner has to choose one
or more answers;
– Fill Gaps: given a text with some gaps, the learner needs to fill in missing
text in text boxes;
– Dropdown: same as Fill Gaps, but instead of free text fields, the learner can
choose between a number of predefined answers;
– Click on Text: given a text, the learner needs to click/tab on one or more
words;
– Click on Zone: given an image or video, the learner needs to click/tab at one
or more regions within the image or video.
Note that media elements can also occur within the first five types of exercises.
For instance, a movie can be played followed by the question to solve. Only the
last type (Click on Zone) uses multimedia in an interactive way as described
in [19].
In Listing 1.1, a multiple choice exercise is used as example (line 9). A link
to a movie fragment is provided via the mplc:media property (line 10), which
takes as value a Media Fragment URI (see Section 3.3). The mplc:task descrip-
tion (line 11) provides the question or task in multiple languages (based on the
level of the learner, the reasoner can choose if the language is presented in the
native language of the learner or not). Further, the mplc:answerSpace (line 13)
corresponds to the zone where the learner can enter its answers. Within such
an answer space, mplc:input is provided (line 14), where each mplc:answer
corresponds to one possible answer. In case of a multiple choice type, each an-
swer corresponds to a mplc:Choice. It contains information such as ‘is this
possible answer the correct one?’, ‘how much does the learner score when (s)he
selects this one?’, and the possible answer itself. LOM-specific elements such
as llomp:lifeCycle (line 40) and llomp:educational (line 34) are present as
well.
As a part of the aforementioned LOM extensions, we added the learning
component property to the educational component. Since the MAPLE project
focusses on language learning, we extended this learning component property
with specific support for language learning. The learning component is split up
into three separate subcomponents: target language, theme and language compo-
nent. The latter component can have one or more of the following subproperties:
– knowledge property: vocabulary, pronunciation, etc.;
– skill property: reading, listening, writing or speaking
We also defined a hierarchical structure for the range of the knowledge property
based on which the exact knowledge URIs can be deduced. This was done in
a language-independent way extendable with language-specific elements. As the
Listing 1.2. Representing a learning component in RDF (in Turtle).
1 @prefix lang : < http :// kuleuven - kortrijk . be / itec / ext / ontologies /
i t e c _ e l e a r n i n g _ o n t o l o g y / l a n g u a g e c o m p o n e n t /# >.
@prefix llomp : < http :// multimedialab . elis . ugent . be / organon / ontologies /
maple / llomp # >.
< http :// ninsuna . elis . ugent . be / rdf / resource / maple / learningComponent_40001 >
5 a llomp : L e a r n i n g C o m p o n e n t ;
llomp : theme " agriculture " ;
llomp : targe tLangua ge " en - UK " ;
llomp : l a n g u a g e C o m p o n e n t [
a llomp : L a n g u a g e C o m p o n e n t ;
10 llomp : knowledge < http :// kuleuven - kortrijk . be / itec / ext / ontologies /
i t e c _ e l e a r n i n g _ o n t o l o g y / l a n g u a g e c o m p o n e n t / grammar / partsOfSpeech /
substantive > ;
llomp : skill lang : writing .
] .
skill and knowledge property exists next to each other, it is possible to specify
the subject of an exercise very accurately. In Listing 1.2 an example instance
of a learning component can be found. The exercise in this instance trains the
writing skill of substantives related to agriculture.
Within the MAPLE project, we use learning items from Televic Education
(TEDU)8 . Currently, TEDU stores their learning items and accompanying meta-
data in a SQL store. Through XML feeds, the store can be accessed from outside.
Hence, we implemented a converter taking as input the XML feeds and produc-
ing RDF learning items according to the above described model.
3.2 Model for the learning domain
The learning items are not physically arranged into courses. Which learning ob-
jects belong together is determined by the metadata, namely the learning compo-
nent within the educational component of each item. The domain model consists
of two type of relations: prerequisite and hierarchical relations. In the project,
the domain model is supposed to be simple. It is a three level hierarchical model
in which the items are first distinguished by their target language, secondly by
their theme, and thirdly by their language component. Additionally, there exist
prerequisite requirements between the language components, expressing one lan-
guage component depends on the knowledge of another. The reasoner will take
into account these prerequisites when determining what courses are available for
the learner.
3.3 Model for media metadata
To model media resources, we rely on the W3C Media Annotations ontology [11],
which is supposed to foster the interoperability among various kinds of metadata
formats currently used to describe media resources on the Web. Moreover, it
8
http://www.televic-education.com/en/
Listing 1.3. Representing a learner profile in RDF (in Turtle).
1 @prefix itec : < http :// kuleuven - kortrijk . be / itec / ext / ontologies /
i t e c _ e l e a r n i n g _ o n t o l o g y # >.
@prefix foaf : < http :// xmlns . com / foaf /0.1/ >.
@prefix mplc : < http :// multimedialab . elis . ugent . be / organon / ontologies /
maple / content # >.
5 < http :// kuleuven - kortrijk . be / itec / ext / ontologies / i t e c _ e l e a r n i n g _ o n t o l o g y /
maple / learners # blcc_piet_lambrecht >
foaf : nick " piet_ lambrec ht " ;
foaf : firstName " Piet " ;
foaf : lastName " Lambrecht " ;
itec : ha sProfic iency [
10 a itec : Proficiency ;
itec : h a s L e a r n i n g S u b j e c t : l e a r n i n g C o m p o n e n t _ 4 7 5 8 4 ;
itec : h a s S c o r e d E v a l u a t i o n [
a itec : S c o r e dE v a l u a t i o n ;
itec : score "3.2"^^ xsd : float ;
15 itec : scoreVariance "1.1"^^ xsd : float ;
itec : scoreScale itec : d e f a u l t E u r o p e a n L a n g u a g e L e v e l S c a l e .
] .
] ;
itec : ha sL e ar ni n gG oa l [
20 a itec : S c o r e d E v a l u a t i o n L e a r n i n g G o a l ;
itec : h a s S c o r e d E v a l u a t i o n [
a itec : S c o r e dE v a l u a t i o n ;
itec : score "4"^^ xsd : float ;
itec : scoreScale itec : d e f a u l t E u r o p e a n L a n g u a g e L e v e l S c a l e .
25 ] ;
itec : h a s L e a r n i n g S u b j e c t : l e a r n i n g C o m p o n e n t _ 4 7 5 8 4 .
] ;
itec : p r e f e r r e d E x e r c i s e T y p e mplc : DropDown .
already contains mappings to many other existing metadata formats. Further,
the ontology also provides support for Media Fragment URIs.
3.4 Model for the learner profile
In order to steer the decision making of the reasoner, an up-to-date learner
profile is required for each of the learners in the learning system. This profile
holds proficiency score estimations for each of the appropriate learning subjects.
Each of these values is supplemented with a reliability parameter, namely the
variance of the estimator. As we focus on language learning, the proficiency scores
are expressed on a continuous scale based on the discrete European Language
Levels [4]. The level of A1 conforms to a score of 0, A2 to 1, B1 to 2, etc. Also,
the profile keeps a list of the learning goals which were set for that learner. An
example of such a learning goal could be “Achieve the B2 level for the French
verb form imparfait”. The type of learning items the learner prefers can also be
saved in the profile. An example instance can be found in Listing 1.3.
The properties in the model will be caught either automatically either by
means of preference setting. The learner’s favourite learning item types can be
edited through a preference menu and the learner’s proficiency scores will be
updated by a module of the reasoner. Additionally, the ontological model sup-
Listing 1.4. Representing a logging abstract in RDF (in Turtle).
1 @prefix itec : < http :// kuleuven - kortrijk . be / itec / ext / ontologies /
i t e c _ e l e a r n i n g _ o n t o l o g y # >.
@prefix learners : < http :// kuleuven - kortrijk . be / itec / instances / maple /
learners # >.
@prefix log : < http :// kuleuven - kortrijk . be / itec / instances / maple / logging # >.
@prefix maple : < http :// ninsuna . elis . ugent . be / rdf / resource / maple / >.
5
log : l e a r n e r S e s s i o n 1 2 4 5 2
a itec : Learn erSessio n ;
itec : ha sS e ss io n St ar t "2010 -10 -26 T21 :32:52.126"^^ xsd : dateTime ;
itec : ha sSessio nStop "2010 -10 -26 T21 :38:52.526"^^ xsd : dateTime ;
10 itec : hasLearner learners : b l c c _ p i e t _ l a m b r e c h t ;
itec : hasSubSession [
a itec : L ea r ni ng Se s si on ;
itec : ha sS e ss io nS t ar t "2010 -10 -26 T21 :32:52.229"^^ xsd : dateTime ;
itec : ha sSession Stop "2010 -10 -26 T21 :38:52.501"^^ xsd : dateTime ;
15 itec : h a s I t e m O b j e c t S e s s i o n [
a itec : I t e m O b j e c t S e s s i o n ;
itec : hasItemObject maple : blcc_47363 ;
itec : ha sS e ss io nS t ar t "2010 -10 -26 T21 :32:56.233"^^ xsd : dateTime ;
itec : ha sSession Stop "2010 -10 -26 T21 :32:59.999"^^ xsd : dateTime ;
20 itec : h a s A n s w e r S u b m i t t e d E v e n t [
itec : ha sInputOb ject maple : i n p u t O b j e c t _ 5 7 4 9 5 ;
itec : ha sGivenAn swer maple : answer_57495 ;
itec : dateTime "2010 -10 -26 T21 :32:59.526"^^ xsd : dateTime .
] .
25 ] .
] .
ports properties like motivation, learning style, learner strategy, and cognitive
ability’s, but currently these are not used in the MAPLE e-learning platform.
3.5 Model for logging the learner’s activity
Finally, we developed a model for describing logging information. For instance,
the model is able to express information such as the start and stop of a learner
session or the learner’s course selection. Once the learner has chosen a course, a
learning session is initiated in which the reasoner successively selects a new learn-
ing item, each time resulting in a learning item session which lasts for the time
the learner interacts with the item. During such an item session a learner can give
an answer, request a hint, or change his mind by changing his answer. All these
interactions are logged by the system. This results in a huge amount of informa-
tion which is consumed in two ways. Firstly, a part of the logging information is
used at run-time by the reasoner. For instance, a score attained by the learner
will affect the proficiency score of a learner’s profile through the functionality of
the reasoner’s proficiency manager. Secondly, after runtime, the logged informa-
tion will be used as input for statistical research tracing how certain interactions
of the learner give information about the learning process. In Listing 1.4 an ex-
ample instance can be found. The learner and learning session, and the session
of the item are respectively interconnected by the itec:hasSubSession and the
itec:hasItemObjectSession relation.
These resulting triples are partially generated in the core of the reasoner,
e.g. the start en stop of the learner and the learning sessions. The low level
interactions concerning one specific exercise are generated at the client and sent
back to the reasoner which processes the logging and stores it in the learner
profile RDF store.
4 Adaptive Learning Item Selection
The reasoner, introduced in Section 2, is a crucial component within the MAPLE
learning system architecture as it is responsible for the adaptive learning item
selection. If a learner logs in, the reasoner will first of all provide a short list
of courses from which the learner can choose. As the reasoner is aware of the
learning goals for each learner through the learner profile model, only courses
that contribute to the not yet attained learning goals can be selected. Next, once
the learner has chosen a course, the reasoner will start up a learning session and
will successively decide on the exact exercise to deliver to the learner.
The reasoner takes into account the learner profile as well as some real time
environmental properties. For the environmental adaptivity, both the screen ca-
pacity and connection quality of the user’s device are sources of adaptivity. In
case the screen size is too small, the reasoner will avoid the use of exercises with
media. A slow network connection will also result in avoiding media exercises.
For the learner profile adaptivity, there are two main policies which can steer
the decision process. The first one is based on a theory stating that the exer-
cise difficulty needs to be increased each time a learner has answered a series of
four exercises correctly. Similarly, when four consecutive exercises are answered
incorrectly, it should go down [12]. The second policy is based on a pedagogical
theory which tries to keep the learner’s motivation high by chasing a predefined
(e.g. 70 %) correct-answer probability. This probability can be estimated based
on the IRT theory ([5]) by combining the current proficiency estimation with the
level and difficulty of the exercise [28, 7]. The aforementioned policies are supple-
mented with an event-driven feedback system. The system allows the sequencer
to shift in a feedback item (instead of an exercise) to explain a learning subject
once a specific and predefined condition is met. For instance, “the learner made
five errors in a row against the same learning subject”. This feedback item is
chosen based on the learning component property which both the feedback and
the exercise item have in their metadata. For both policies, also the preferred
exercise types of the learner are taken into account by favouring them though
not completely cold-shouldering the other exercise types.
To fulfil the aforementioned tasks, the architecture of the reasoner (shown
in Fig. 2) consists of six modules, supplemented by a facade for communicating
with the learning endpoint. The six reasoner modules are the Learner manager,
Environment manager, Learning task decision manager, Sequence manager, Log-
ging manager and Proficiency manager. We elucidate the functionality of these
modules by means of the following example.
Environment
manager
Learning task
Learner
decision
manager
Learner profile manager
DB
Learning
Facade
endpoint
Learning item Sequence
DB manager
Proficiency Logging
manager mananger
Reasoner
Fig. 2. The reasoner architecture
Suppose a learner’s initial profile was set by a teacher thereby providing the
learning goal “Achieve the B2 level for the French verb form imparfait” and also
providing an estimation for the learner’s initial level, namely A2, for “the French
verb form imparfait”. When the learner logs in, the Learner manager produces
a learner session. Consequently, the Learning task decision manager loads the
learner’s learning goals in order to compose a three-level tree representation of all
courses relevant for this learner, as explained in Section 3.2. This tree is sent to
the learning endpoint which produces a representation such that the learner can
navigate through the tree. Let us assume that the learner first selects ‘French’
followed by the theme ‘General’ and finally the language component ‘Imparfait’.
Besides, the learner opens the preferences menu and sets the dropdown exercise
type as his favourite one.
Next, the Learning task decision manager composes a learning task object
which is send to the Sequence manager. Here the learning task is sequencing the
items (exercises and feedback) with the first policy of adaptivity, starting from
level A2, having as a stop criterion the achievement of the level B2, and taking
into account the learner’s preferred exercise types and environmental proper-
ties. Subsequently, the Sequence manager loads the sequencer necessary for the
learning task. To this end, the sequencer makes use of the Environment manager,
which is an access point for information on the current connection quality and
the screen size of the device of the learner. At this point, the sequencer can suc-
cessively decide on the id of the next item and passes its choice to the learning
endpoint, which automatically generates a visual representation and makes use
of the delivery platform in case media are present.
Once the learner finishes the exercise or has read the feedback in case of a
feedback item, the logging information about the interactions of the learner with
the item are sent back to the Logging manager of the reasoner. The latter sends
this information as a specific logging object to a couple of observer objects which
all have different functionalities. For instance, there is an observer writing these
logs to the learner profile RDF store. Another observer warns the sequencer when
for example four exercises have been consecutively answered correctly and yet
another sends the learner’s score to the Proficiency manager together with the
level, difficulty and the learning subject of the answered exercise. The Proficiency
manager keeps the proficiency scores up to date. Prior to every decision of the
sequencer, the stop criterion is tested based on a proficiency that is retrieved from
the Proficiency manager. If this criterion is reached, the sequencer sequences a
special concluding feedback item announcing the end of the learning session to
the learner.
5 Related Work
The architecture of the reasoner builds further on existing proposals for generic
learning system architectures such as in [20]. These architectures however have
mostly been designed having an adaptive hypermedia learning system in mind.
Even though most systems currently developed are based on providing learner
control based on adaptive links, e.g. [3], our system is specialized in adaptive
curriculum sequencing, meaning that the learning objects are sequenced in an
automated way. To create an adaptive learning system the method of using on-
tologies has often been proposed in literature, e.g., in [23, 17, 8]. We partially rely
on existing ontologies and data models, and introduced new data models such as
a model for describing learning exercises and language-learning specific informa-
tion. The latter were all done in collaboration with educationalists. Additionally,
both the delivery platform and the reasoner take into account connection quality
and screen size either to choose the right video format either to avoid sending
any media to a device if they cannot be delivered in an optimal way. This way,
our system implements a part of the context-awareness which has been claimed
to be crucial in mobile learning [23, 27].
The ontology for the learner profile is a compact non-exhaustive synopsis of
the most common learner characteristics found in literature [21, 13, 10] which
can be used in steering an adaptive learning system. For the preservation of
the learner’s knowledge we used what is classified as an overlay model in [13].
Until now, the IEEE Learning Object Model standard LOM is considered to be
the standard for many repositories storing thousands of learning objects with
metadata. There have been attempts to transform the LOM metadata model
into an RDF version (e.g., [18]). However, the model provided by LOM was not
sufficient. Hence, we adopted part of the LOM model (by relying on previous
LOM RDF efforts) and extended it with our own needs.
Our realizations in this project largely replace the functionality of the restric-
tive SCORM standard [1]. SCORM, an abbreviation for Sharable Content Object
Reference Model, is a collection of specifications imposing a format for bundling
Web-based exercises into courses, thereby imposing LOM for the metadata, as
well as a data model for communicating learning scores between server and client.
The standard was updated in 2004, now supporting a limited set of instructions
for adaptive behavior. In practise however, the imposed syntax for adaptivity
had low expressivity but remaining very complicated [14]. Although in the past
SCORM had an important impact on the sharing of bundled learning courses on
the web and although many tried to improve the SCORM standard [16, 22, 29],
we think its starting point has become outdated. After all, we believe grouping
learning objects in a container format conflicts with the principle of the Semantic
Web of data in which objects are scattered over the web. Additionally, its exten-
sibility pointed out to be low [14, 6] and the data model for exchanging learning
results is limited to the exchange of a single score, thereby not fulfilling our
needs of more advanced reporting of a learner’s interactions with the exercises.
Our formalized representation model for recording scores and interactions with
exercises makes it possible to develop true interoperable exercises that are able
to report learning results in a universal way. Until now, the importance for adap-
tive learning systems having an extendible although universally understandable
learning result reporting system was largely ignored.
Gang et al. proposed a framework for mobile learning in [9] that approaches
the challenges similarly as we did here. More specifically, a media delivery sys-
tem was developed, as well as an adaptive module for learning item selection.
However, they relied on MPEG-21 technology while we use the NinSuna plat-
form, which is based on MPEG-21 principles but proven to be more efficient
and generic [26]. Further, learning item selection is not based on educational
properties such as skills or experience, but solely on environmental properties.
6 Conclusions and Future Work
In order to exploit the possibilities of Web-based e-learning environments, we
proposed an e-learning architecture that is able to provide rich, personalized
e-learning experiences to a wide range of devices. We discussed the various data
models used within the e-learning framework. Moreover, we provided details of
the reasoner, a crucial component allowing to select learning items based on the
usage environment and the learner profile.
Future work consists of exploiting the possibilities of the Semantic Web even
more by linking learning items to the Linked Open Data cloud. Further, data
models could be optimized and linked to upcoming efforts (e.g., how to represent
the life cycle of a learning item as provenance information on the Web). Also,
more detailed domain models should be investigated. Regarding the reasoner,
future work consists of taking into account more information obtained from the
logging framework, as well as investigating how error-specific feedback could be
generated (e.g., link frequently occurring errors to answers).
Acknowledgments
The research activities as described in this paper were funded by Ghent Uni-
versity, the Interdisciplinary Institute for Broadband Technology (IBBT, 50%
co-funded by industrial partners), the Institute for the Promotion of Innovation
by Science and Technology in Flanders (IWT), the Fund for Scientific Research-
Flanders (FWO-Flanders), and the European Union.
References
1. Scorm 2004 4th edition version 1.1 overview, http://www.adlnet.gov/
Technologies/scorm/SCORMSDocuments/2004%204th%20Edition/Overview.aspx
2. Standard for learning object metadata, http://standards.ieee.org/findstds/
standard/1484.12.1-2002.html
3. Grapple, a generic responsive adaptive personalized learning environment. http:
//www.grapple-project.org (Jun 2010)
4. European language levels - self assessment grid (2011), http://europass.cedefop.
europa.eu/LanguageSelfAssessmentGrid/en
5. Baker, F.B., Kim, S.H. (eds.): Item Response Theory: Parameter Estimation Tech-
niques, Second Edition (Statistics: A Series of Textbooks and Monographs). CRC
Press, 2 edn. (July 2004)
6. Bohl, O., Scheuhase, J., Sengler, R., Winand, U.: The sharable content object
reference model (scorm) - a critical review. In: Computers in Education, 2002.
Proceedings. International Conference on. pp. 950 – 951 vol.2 (2002)
7. Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using item
response theory. Computers & Education 44(3), 237 – 255 (2005)
8. Chi, Y.L.: Ontology-based curriculum content sequencing system with semantic
rules. Expert Syst. Appl. 36, 7838–7847 (May 2009)
9. Gang, Z., Zongkai, Y.: Learning Resource Adaptation and Delivery Framework
for Mobile Learning. In: Frontiers in Education, 2005. FIE ’05. Proceedings 35th
Annual Conference (October 2005)
10. Jia, B., Zhong, S., Wang, W., Yang, B.: The construction and evolution of learner
model in adaptive learning system. Computer Technology and Development, In-
ternational Conference on 1, 148–152 (2009)
11. Lee, W., Bürger, T., Sasaki, F., Malaisé, V., Stegmaier, F., Söderberg, J. (eds.):
Ontology for Media Resource 1.0. W3C Working Draft, World Wide Web Consor-
tium (June 2010)
12. Leutner, D.: Instructional design principles for adaptivity in open learning envi-
ronments. Curriculum, Plans, and Processes in Instructional Design: International
Perspectives pp. 289–307 (2004)
13. Loc Nguyen, P.D.: Learner model in adaptive learning. World Academy of Science,
Engineering and Technology 45 (2008)
14. Mackenzie, G.: Scorm 2004 primer, a (mostly) painless introduction to
scorm. Tech. rep. (2004), http://www.pro-ductivity.com/Compliance21CFR/
CTMW/scormintro.pdf
15. Mannens, E., Van Deursen, D., Troncy, R., Pfeiffer, S., Parker, C., Lafon, Y.,
Jansen, J., Hausenblas, M., Van de Walle, R.: A URI-Based Approach for Ad-
dressing Fragments of Media Resources on the Web. To appear in Multimedia
Tools and Applications – Special Issue on Multimedia Data Semantics
16. Melia, M., Barrett, R., Pahl, C.: A model-based approach to scorm sequencing. In:
In Proceeding of the Sixth Annual Irish Educational Technology Users Conference
(EdTech06) - Research Track. ILTA (2006)
17. Min, W.X., Wei, C., Lei, C.: Research of ontology-based adaptive learning system.
In: Proc. Int. Symp. Computational Intelligence and Design ISCID ’08. vol. 2, pp.
366–370 (2008)
18. Nilsson, M., Palmer, M., Brase, J.: The LOM RDF binding - principles and imple-
mentation. In: Proceedings of 3rd Annual Ariadne Conference (2003)
19. Perez, M.M., Cornillie, F., Senecaut, M.P., De Wannemacker, S., Desmet, P.: Mo-
bile vocabulary learning: activities allowing for interaction through input enhance-
ment. In: De Wannemacker, S., Clarebout, G., De Causmaecker, P. (eds.) Interdis-
ciplinary Approaches to Adaptive Learning: A Look at the Neighbours,. pp. 15–27.
Springer-Verlag (2011)
20. Rani, S., Ashok, M., Palanivel, K.: Adaptive content for personalized e-learning
using web service and semantic web. pp. 1 –4 (jul 2009)
21. Roll, I., Baker, R.S., Aleven, V., Koedinger, K.R.: A metacognitive act-r model of
students’ learning strategies in intelligent tutoring systems. In: Intelligent Tutoring
Systems. pp. 854–856 (2004)
22. Silva, L., Mustaro, P.N., Stringhini, D., Silveira, I.F.: Using conceptual lattices
to represent fine granular learning objects through scorm meta-objects. The Elec-
tronic Journal of e-Learning 4, 141–148 (2006)
23. Soylu, A., Vandewaetere, M., Wauters, K., Jacques, I., De Causmaecker, P.,
Desmet, P., Clarebout, G., Van Den Noortgate, W.: Ontology-driven Adaptive
and Pervasive Learning Environments - APLEs: An Interdisciplinary Approach.
In: De Wannemacker, S., Clarebout, G., De Causmaecker, P. (eds.) Interdisci-
plinary Approaches to Adaptive Learning: A Look at the Neighbours,. pp. 99–115.
Springer-Verlag (2011)
24. Troncy, R., Mannens, E., Pfeiffer, S., Van Deursen, D. (eds.): Media Fragments
URI 1.0. W3C Working Draft, World Wide Web Consortium (January 2011)
25. Van Deursen, D., Van Lancker, W., De Neve, W., Paridaens, T., Mannens, E.,
Van de Walle, R.: NinSuna: a Fully Integrated Platform for Format-independent
Multimedia Content Adaptation and Delivery based on Semantic Web Technolo-
gies. Multimedia Tools and Applications – Special Issue on Data Semantics for
Multimedia Systems 46(2-3), 371–398 (January 2010)
26. Van Deursen, D., Van Lancker, W., De Bruyne, S., De Neve, W., Mannens,
E., Van de Walle, R.: Format-independent and Metadata-driven Media Resource
Adaptation using Semantic Web Technologies. Multimedia Systems 16(2), 85–104
(2010)
27. Wang, Y.K.: Context awareness and adaptation in mobile learning. In: Wireless and
Mobile Technologies in Education, 2004. Proceedings. The 2nd IEEE International
Workshop on. pp. 154 – 158 (2004)
28. Wauters, K., van den Noortgate, W., Desmet, P.: The use of irt for adaptive item
selection in item-based learning environments. In: Proceeding of the 2009 confer-
ence on Artificial Intelligence in Education. pp. 785–786. IOS Press, Amsterdam,
The Netherlands, The Netherlands (2009)
29. Zhu, X.H., Yan, R.Q., Wu, L.P., Zhang, L.F.: An interoperable learning sequencing
model based on the flow control object. Computer Science and Software Engineer-
ing, International Conference on 5, 1217–1220 (2008)