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 ne-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 devices (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 experiences 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 pro le.
The last years, the use of e-learning environments has increased spectacularly, not only in formal educational settings, but also in working and private environments. 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 ne-grained feedback, and they can make use of audiovisual material. Moreover, while e-learning environments were traditionally o ered as applications on stand-alone computers, 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 environmental 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 tackled in the IBBT MAPLE project (Mobile, Adaptive & Personalized Learning Experience3), which aims to make adaptive mobile e-learning possible. Therefore, in this paper, we present a Web-enabled e-learning environment that is able to o er 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 capabilities and behaviour; { a media delivery platform taking into account usage environment characteristics 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
In order to o er 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 pro les. The learning items store is lled 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 pro le 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 pro le and environment and the available learning items. Detailed information 3 http://www.ibbt.be/en/projects/overview-projects/p/detail/maple-2 media ingest service learning item ingest service media store learning item
DB learner profile
DB media delivery platform selection
& packaging reasoner media delivery channels ADTE service learning endpoint 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
metadatadriven media adaptation and delivery platform [
The presented e-learning platform exposes its data (i.e., learning content and accompanying media resources) as linked data. More speci cally, 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 pro les 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.
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 pro le 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 ts 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 elearning platform; (6) the received answers and behaviour information are used to update the learner's pro le.
In the next sections, more detailed information regarding a number of components in the architecture is provided. 3
A number of di erent data models need to be developed in order to structure and de ne the content used on the e-learning platform. More speci cally, we need the following data models: { model for the learning items and their metadata (e.g., question, possible answers, di culty level); { model for the learning domain; { model for the metadata of the media resources (e.g., bit rate); { model for the learner pro le; { model for the logging.
In the following subsections, we provide more information regarding these different models and how they are populated. Note that all ontologies are modelled in OWL and published online. 3.1
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 10 15 20 25 30 35 40 <http :// ninsuna . elis . ugent . be / rdf / resource / maple / blcc_47363 > a llomp : Exercise ; dc : title "47363" ; mplc : exerciseType mplc : MultipleChoice ; 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 [ a mplc : Input ; mplc : answer [ a mplc : Choice ; mplc : isCorrect " false "^^ xsd : boolean ; mplc : scoreCorrect "0"^^ xsd : int ; mplc : scoreFalse "0"^^ xsd : int ; mplc : text " des frites " @fr . ] ; mplc : answer [ a mplc : Choice ; mplc : isCorrect " true "^^ xsd : boolean ; mplc : scoreCorrect "1"^^ xsd : int ; mplc : scoreFalse "0"^^ xsd : int ; mplc : text " de la glace " @fr .
] .
] ;
mplc : maxScore "1" ;
mplc : minScore "0" ;
llomp : educational [
a llomp : Educational ;
llomp : difficulty llomp : medium ;
llomp : level llomp : A2 ;
llomp : learningComponent : learningComponent_44854 .
] ;
llomp : lifeCycle : lifeCycle_47363 .
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, [
We describe not only the metadata of learning items, but also the exercises themselves. This way, they are formally represented, independent of any rendering. Moreover, they can be easily integrated with their metadata and corresponding 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 ll in missing text in text boxes; { Dropdown: same as Fill Gaps, but instead of free text elds, the learner can choose between a number of prede ned 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 rst ve 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 [
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 description (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 answer 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-speci c 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 speci c support for language learning. The learning component is split up into three separate subcomponents: target language, theme and language component. 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 de ned 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-speci c elements. As the
Listing 1.2. Representing a learning component in RDF (in Turtle). 1 @prefix lang : <http :// kuleuven - kortrijk . be / itec / ext / ontologies / itec_elearning_ontology / languagecomponent /# >. @prefix llomp : <http :// multimedialab . elis . ugent . be / organon / ontologies / maple / llomp # >. 5 10 <http :// ninsuna . elis . ugent . be / rdf / resource / maple / learningComponent_40001 > a llomp : LearningComponent ; llomp : theme " agriculture " ; llomp : targetLanguage "en - UK " ; llomp : languageComponent [ a llomp : LanguageComponent ; llomp : knowledge <http :// kuleuven - kortrijk . be / itec / ext / ontologies / itec_elearning_ontology / languagecomponent / 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 metadata 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 producing RDF learning items according to the above described model. 3.2
The learning items are not physically arranged into courses. Which learning objects belong together is determined by the metadata, namely the learning component 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 rst 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 language 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
To model media resources, we rely on the W3C Media Annotations ontology [
Listing 1.3. Representing a learner pro le in RDF (in Turtle). 1 @prefix itec : <http :// kuleuven - kortrijk . be / itec / ext / ontologies / itec_elearning_ontology # >. @prefix foaf : <http :// xmlns . com / foaf /0.1/ >. @prefix mplc : <http :// multimedialab . elis . ugent . be / organon / ontologies / maple / content # >. 10 15 20 25 already contains mappings to many other existing metadata formats. Further, the ontology also provides support for Media Fragment URIs. 3.4
In order to steer the decision making of the reasoner, an up-to-date learner
pro le is required for each of the learners in the learning system. This pro le
holds pro ciency 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 pro ciency scores
are expressed on a continuous scale based on the discrete European Language
Levels [
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 pro ciency 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 / itec_elearning_ontology # >. @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 10 15 20 25 log : learnerSession12452 a itec : LearnerSession ; itec : hasSessionStart "2010 -10 -26 T21 :32:52.126"^^ xsd : dateTime ; itec : hasSessionStop "2010 -10 -26 T21 :38:52.526"^^ xsd : dateTime ; itec : hasLearner learners : blcc_piet_lambrecht ; itec : hasSubSession [ a itec : LearningSession ; itec : hasSessionStart "2010 -10 -26 T21 :32:52.229"^^ xsd : dateTime ; itec : hasSessionStop "2010 -10 -26 T21 :38:52.501"^^ xsd : dateTime ; itec : hasItemObjectSession [ a itec : ItemObjectSession ; itec : hasItemObject maple : blcc_47363 ; itec : hasSessionStart "2010 -10 -26 T21 :32:56.233"^^ xsd : dateTime ; itec : hasSessionStop "2010 -10 -26 T21 :32:59.999"^^ xsd : dateTime ; itec : hasAnswerSubmittedEvent [ itec : hasInputObject maple : inputObject_57495 ; itec : hasGivenAnswer maple : answer_57495 ; itec : dateTime "2010 -10 -26 T21 :32:59.526"^^ xsd : dateTime .
] .
] .
] . 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
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 learning 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 information 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 a ect the pro ciency score of a learner's pro le through the functionality of the reasoner's pro ciency manager. Secondly, after runtime, the logged information 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 example 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 speci c exercise are generated at the client and sent back to the reasoner which processes the logging and stores it in the learner pro le RDF store. 4
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 rst 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 pro le 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 pro le as well as some real time
environmental properties. For the environmental adaptivity, both the screen
capacity 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 pro le adaptivity, there are two main policies which can steer
the decision process. The rst one is based on a theory stating that the
exercise di culty 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 [
To ful l 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, Logging manager and Pro ciency manager. We elucidate the functionality of these modules by means of the following example.
Learner profile
DB Learning item
DB
Environment manager Sequence manager Learner manager
Learning task decision manager Proficiency manager
Logging mananger
Reasoner
Suppose a learner's initial pro le 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 rst selects `French' followed by the theme `General' and nally 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 rst 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 properties. 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 successively 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 nishes 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 speci c logging object to a couple of observer objects which all have di erent functionalities. For instance, there is an observer writing these logs to the learner pro le 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 Pro ciency manager together with the level, di culty and the learning subject of the answered exercise. The Pro ciency manager keeps the pro ciency scores up to date. Prior to every decision of the sequencer, the stop criterion is tested based on a pro ciency that is retrieved from the Pro ciency 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
The architecture of the reasoner builds further on existing proposals for generic
learning system architectures such as in [
The ontology for the learner pro le is a compact non-exhaustive synopsis of
the most common learner characteristics found in literature [
Our realizations in this project largely replace the functionality of the
restrictive SCORM standard [
Gang et al. proposed a framework for mobile learning in [
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 pro le.
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 e orts (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-speci c feedback could be generated (e.g., link frequently occurring errors to answers).
The research activities as described in this paper were funded by Ghent University, 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 Scienti c ResearchFlanders (FWO-Flanders), and the European Union.