We argue that quality issues and didactical concerns of MOOCs may be overcome by relying on small Open Educational Resources, joining them into concise courses by gluing them together along predefined learning pathways with proper semantic annotations. This new approach to adaptive learning does not attempt to model the learner, but rather concentrates on the learning process and established models thereof. Such a new approach does not only require conceptual work and corresponding support tools, but also a new meta data format and an engine which may interpret the semantic annotations as well as measure a learner’s response to these. The EU FP7 project INTUITEL is introduced, which employs these technologies in a novel learning environment

Badii, Atta

Bock, Jürgen

Forstner, Alexandra

Fuchs, Kevin

Garcia Perales, Oscar

Henning, Peter A.

Steicher, Alexander

Swertz, Christian

Thiemert, Daniel

Zander, Stefan

Zielinski, Andrea

English

Fachlicher Dokumentenserver Paedagogik/Erziehungswissenschaften

Henning, Peter A.; Fuchs, Kevin; Bock, Jürgen; ...Personalized web learning by joining OERTrahasch, Stephan [Hrsg.]; Plötzner, Rolf [Hrsg.]; Schneider, Gerhard [Hrsg.]; Gayer, Claudia [Hrsg.]; Sassiat,Daniel [Hrsg.]; Wöhrle, Nicole [Hrsg.]: DeLFI 2014. Die 12. e-Learning Fachtagung Informatik der Gesellschaft fürInformatik e.V., 15.-17. September 2014 in Freiburg. Bonn : Gesellschaft für Informatik e.V. (GI) 2014, S. 127-132. -(Lecture Notes in Informatics (LNI) - Proceedings; P-233)Empfohlene Zitierung/ Suggested Citation:Henning, Peter A.; Fuchs, Kevin; Bock, Jürgen; Zander, Stefan; Steicher, Alexander; Zielinski, Andrea;Swertz, Christian; Forstner, Alexandra; Badii, Atta; Thiemert, Daniel; Garcia Perales, Oscar:Personalized web learning by joining OER - In: Trahasch, Stephan [Hrsg.]; Plötzner, Rolf [Hrsg.];Schneider, Gerhard [Hrsg.]; Gayer, Claudia [Hrsg.]; Sassiat, Daniel [Hrsg.]; Wöhrle, Nicole [Hrsg.]:DeLFI 2014. Die 12. e-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V., 15.-17.September 2014 in Freiburg. Bonn : Gesellschaft für Informatik e.V. (GI) 2014, S. 127-132 - URN:urn:nbn:de:0111-pedocs-159826http://nbn-resolving.de/urn:nbn:de:0111-pedocs-159826Nutzungsbedingungen Terms of useMit der Verwendung dieses Dokuments erkennen Sie dieNutzungsbedingungen an.By using this particular document, you accept the above-stated conditions ofuse.Kontakt / Contact:peDOCSDIPF | Leibniz-Institut für Bildungsforschung und BildungsinformationInformationszentrum (IZ) BildungE-Mail: pedocs@dipf.deInternet: www.pedocs.dePersonalized Web Learning by joining OERPeter A. Henning1, Kevin Fuchs1, Jürgen Bock2, Stefan Zander2, AlexanderStreicher3, Andrea Zielinski3, Christian Swertz4, Alexandra Forstner4, AttaBadii5, Daniel Thiemert5, and Oscar Garcia Perales6 and the INTUITELcollaboration1 Institute for Computers in Education, Karlsruhe U of Applied Sciences, Germany2 FZI Research Center for Information Technology, Karlsruhe, Germany3 Fraunhofer IOSB, Karlsruhe, Germany4 University of Vienna, Austria5 University of Reading, UK6 TIE Kinetix N.V., Breukelen, NetherlandsAbstract. We argue that quality issues and didactical concerns of MOOCsmay be overcome by relying on small Open Educational Resources, join-ing them into concise courses by gluing them together along predefinedlearning pathways with proper semantic annotations. This new approachto adaptive learning does not attempt to model the learner, but ratherconcentrates on the learning process and established models thereof. Sucha new approach does not only require conceptual work and correspond-ing support tools, but also a new meta data format and an engine whichmay interpret the semantic annotations as well as measure a learner’s re-sponse to these. The EU FP7 project INTUITEL7 is introduced, whichemploys these technologies in a novel learning environment.1 IntroductionFor the past few years, one of the most vigorously discussed topics in technol-ogy enhanced learning (TEL) is the possibility to distribute free knowledge tolarge audiences via internet in Massive(ly) Open Online Courses (MOOCs). Thisspecial case of Open Educational Resources (OER) has gained ground due to in-creased bandwidth and the spread of mobile digital devices even in remote areasof the world. The UNESCO has given this field of TEL a tremendous push byits 2012 OER conference, and enthusiastically claims that OER could provide asolution to the world’s educational problems [U12]. With many other TEL prac-titioners, we share a more differentiated view on this, because broadcasted videolectures have a long history and are not really considered the state of the art inTEL. Particular technical and didactical challenges that concern fundamentalaspects of MOOC learning are language, cultural background of learners, as well7 INTUITEL = Intelligent Tutorial Interfaces for Technology Enhanced Learning,http://www.intuitel.eu, is funded in the 7th framework programme of the EuropeanUnion (FP7-ICT-2011.8, Challenge 8.1) under grant no. 318496127as individual learning habits and learning discipline—to mention some of themost prominent ones.Moreover, the anonymity of MOOC learning and its lack of individual tutor-ing or coaching may be responsible for the high dropout rates of 90% that arecommonly observed during a MOOC [Ri13]. In this paper, we therefore inves-tigate how MOOC learning can be made more individual, human-centered andinteractive by a technologically enhanced Learning Management System (LMS).A second aspect is concerned with the simplification of MOOC production. Ifone really wishes to produce a high quality video tape of a full lecture, productioncosts could be as high as 50.000 Euro [MWK14], making it almost impossible tokeep these courses up to date by frequent updates and immensely complicatingtheir introduction in the educational system. Fortunately, the rapid developmentof technology has resulted in an increased availability of OER which are inex-pensively produced and small in length or learning effort: Public video platformscontain large numbers of small clips about almost any subject, also texts, pic-tures applets (or ”apps”) are available for free covering almost anything thatone could think of teaching or learning. For the purpose of the present paper welabel these media Small OER. We then elaborate on the question of how suchSmall OERs can be effectively collected and stitched together in order to createa MOOC-like course.2 Technical and didactical approach of INTUITELThe EU FP7 project INTUITEL provides a new approach to adaptive learn-ing. Current Adaptive Learning Environments (ALE) are either test-driven orcurriculum-driven, performing learner modeling or learner typization, i.e. theyeither follow a behaviouristic or cognitivistic learner model [KT13]. INTUITELin contrast follows a constructivistic approach by leaving full freedom of choiceto the learner while non-intrusively guiding him through a sequence of learningsteps.Each of these learning steps consists of learning one knowledge object (KO)of 3 - 10 minutes, and the concrete sequence of these objects is called a learningpathway. The desired personalization then consists of selecting an order for theknowledge objects based on considering all the aforementioned aspects for an in-dividual learner—and possibly very different from one of the predefined learningpathways. While the didactical and more theoretical aspects of this approach arediscussed in another paper at this conference [He14], the present contributionputs a focus on technology and its application to the MOOC problems outlinedabove.INTUITEL is implemented for five different leading eLearning platforms (eX-act LCMS, Clix, Crayons, ILIAS and Moodle). Each of those enhanced LMS thenconsists of six main components:1. a lightweight extension of the LMS giving access to its data and user inter-face, in terms of user score extraction (USE), tutorial guidance (TUG) andlearning object recommendation (LORE). Its specification is open and can128be applied to every type of LMS. Furthermore the concrete implementationsfor ILIAS and Moodle are open source and usable as blueprints for othersystems, see INTUITEL web site.2. a pedagogical ontology (PO) that is based on Meder’s web didactics [Me06]and insights gained from the L3 project [L01]. It contains the vocabularyand relations necessary for enhancing the learning content with didacticaland technical metadata [Sw13].3. the Semantic Learning Object Model (SLOM) which describes how learningmaterial needs to be annotated in order to be interpretable by the INTUI-TEL system.4. the INTUITEL back-end, which aggregates the required information anduses it to create learning recommendations and feedback in a LearningProgress Model (LPM) and the INTUITEL Engine.5. the INTUITEL communication layer (CL) interconnects the previously de-scribed components and manages message distribution.6. a tool suite comprised of INTUITEL Editor and Merger tailored to processexisting learning content most easily to transform it into the INTUITELformat.Within the INTUITEL project, the learning process is analyzed pragmati-cally by considering the learning pathway of a learner through a course and bygathering additional data. The system may draw these data from four differentsources: (i) the learning content, i.e. what has to be learned? (ii) the learnerhistory, i.e. what has already been learned? (iii) the learning environment, i.e.what are the temporal, spatial and physical parameters? (iv) the learner, i.e.what are the characteristics of this person?In the context of INTUITEL, we extract from these sources some 40 so calleddidactic factors (DF) that are symbolic statements with each of them having adistinct meaning for the learning process. Examples for these DF are:– How does the learner compare to his peers in learning speed ?– Which media types does the learner prefer ?By combining them with the learning pathway information, it is possible todeduce that a certain knowledge object is better suited for the learner thanothers. Moreover, it is also possible to state why this is the case (e.g. because itis age-appropriate, has a suitable difficulty level, etc). This enables self- reflectionof the learner and thus increases his metacognitive skills.The basic definitions of the didactic factors and their value ranges are presentas a separate ontology, which is interpreted by the LPM. This allows it to in-corporate various soft aspects into eLearning, like e.g. motivation or other emo-tions [EG08]. One may also add the actuality of a KO to the set of didacticalfactors and will then receive recommendations to use more recent learning con-tent with higher priority. At the same time, this creates an innovative learningpathway: adding new learning content while keeping the old one also allowslearning about the history of a knowledge domain.129The collected data is then merged with the original SLOM data into a singleontology and forwarded to the INTUITEL Engine. This component is a combi-nation of a set of Java modules and standard OWL-reasoners (like e.g. FaCT++or HermiT). Its task is to analyze the provided individualized ontology in orderto identify the most suitable knowledge objects with regard to the most suitablelearning pathways and the current situation as expressed by the didactic factors.It therefore generates semantic queries and starts the most efficient reasoners forthe specific query. INTUITEL thereby builds on the results and insights of theTHESEUS project and in particular the HERAKLES Reasoning Broker [Bo09].The output of this iterative procedure is then interpreted in order to create thefinal learning recommendations and also generates natural language messagesfor the learner, if appropriate.3 Joining OERLet us now outline how the INTUITEL technology is used to overcome theMOOC problems stated in the introductory text. Course authors are not re-stricted in their choice of what learning material they provide and in which stylethey do it. They just need to add further information to it in a following step -and those annotations, created with the specialized INTUITEL Editor, can bestored persistently with the learning content.The Semantic Learning Object Model SLOM specifies a file format thatcontains all relevant data to exchange and store complete INTUITEL-enabledcourses (i.e. courses from the LMS that have a (semi-)complete set of INTUITELmetadata). It contains eLearning course material in a form similar to IMS-CPand to the packaging of SCORM, but with additional annotations according tothe pedagogical and domain-specific ontologies. The SLOM container format al-lows for a compact side-by-side storage of learning content and metadata, permit-ting the usage of INTUITEL-enhanced course material also in Non-INTUITELsystems (albeit without the functionality provided by INTUITEL). It is there-fore easy to transform other semantically annotated formats for learning contentinto SLOM.A particular source for this process is the extraction from a Semantic Me-dia Wiki (SMW) which was created as an extension to the popular MediaWikisoftware. MediaWiki is the basis for numerous collaborative information stores,with the free encyclopaedia Wikipedia as the most prominent example. Wikisin general are well known for their capabilities to collect and share knowledgewithin and across communities. The SMW extends the MediaWiki software withsemantic features that allow for an annotation of wiki content with machine- pro-cessable semantic information. Since other tools allow to convert a selection ofHTML web pages into MediaWiki pages, a complete tool chain exists to createINTUITEL content by importing it from anywhere on the internet.The important aspect for our MOOC problem is, that the INTUITEL meta-data treats the concrete learning content as resources of the RDF/RDFS/OWLsyntax, i.e., it is only linked to the content. Therefore, the resources may reside130anywhere on the global internet, they only need to be accessible by the learner’sweb browser. In such an extreme example, the corresponding SLOM file containsonly metadata, annotating the distributed learning content according to the IN-TUITEL ontological scheme (see [He14] for details on this). To the learner, sucha distributed INTUITEL course will appear as an adaptive sequence of pagesand media that are not directly linked to each other - but appear as subsequentrecommendations in the TUG interface component of an LMS in considerationof the predefined learning pathway(s) as intended by the teacher.Consider, as an example, a course on Beethoven: It could start with an in-troductory text from Wikipedia, then offer to the learner a virtual tour throughthe Beethoven house in Bonn/Germany, proceeding with an audio file residingon a US server and produced by the Chicago Symphony Orchestra, etc. All ofthese pages and media are presented as adaptive recommendation, taking intoaccount the learner’s history as well as his current situation and behaviour inthe framework of the didactical model. Every learner experiences individual tu-toring, even dialogues with the INTUITEL system - and this course does notrequire a large monolithic production effort.The INTUITEL approach may also affect the content production in general.Not only does it preserve the high level of freedom for course creation currentlydemanded by authors, but also allows novel ways of collaboration in teaching.Authors from all over the world can link their Small OER via URIs and providetheir learners with a huge knowledge space. It is conceivable that such a knowl-edge space can attract as much learners as one of the current MOOCs—but moreflexibly and with an almost unlimited individuality.A course designer—or many of them—can contribute to this knowledge spacenot only by adding new learning content. They can also contribute a new Cog-nitive Content Map (CCM), which defines new learning pathways through thisknowledge space. Easier cultural adaptation is only one of the many possibilitiesoffered by this approach.4 Summary and OutlookIn this paper, we outlined a way to make OER more suitable for a greater vari-ability of learning needs, by semantically annotating Small OER and runningthem in a semantically enhanced LMS. INTUITEL therefore contributes to keyaspects of OER, e.g. how to create online courses in a didactically meaningfulway, how to add semantic interoperability, and how LMSs can assist in that. Inour estimate, this could also be used for a semantic reconstruction of currentMOOCs which will resolve some of their problems of maintainability and adapt-ability. The INTUITEL system here serves as the “glue” integrating a variety oflearning content into a greater knowledge space.By providing the information on the learning process in a suitable format,INTUITEL also opens the doors for other technologies such as learning analyticsand data mining in the educational sector. With the insights that can be gained131from a data driven perspective, this could result in new didactical approachesand thus enhance education in general.The research reported here is more than ”work in progress”. The USE/TUG/LOREinterfaces are in working condition for the five LMSs mentioned above, and thespecifications for these technical parts as well as specifications, guidelines andexamples for the semantic annotation are available from the INTUITEL web site.Not only does this allow for independent testing of the concepts, it also enablesintegration of the USE/TUG/LORE interfaces into other LMS and thereforeopens a new development line for TEL. It is also inline with the goals of INTU-ITEL not just to produce research results, but concrete innovation for TEL.References[Bo09] Bock, J., Tserendorj, T., Xu, Y., Wissmann, J., Grimm, S.: A Reasoning BrokerFramework for OWL. In: Hoekstra, R., Patel-Schneider, P.F. (eds): Proceed-ings of the 6th International Workshop on OWL: Experiences and Directions(OWLED 2009),CEUR WS proceedings, Chantilly (2009)[Da06] Davis, D. A., Mazmanian, P. E., Fordis, M., Van Harrison, R., Thorpe, K.E.,Perrier, L.: Accuracy of Physician Self-assessment Compared With ObservedMeasures of Competence. JAMA The Journal of the American Medical Asso-ciation, 296(9) (2006) 1094-1102[EG08] Erriquez, E., Grasso, F.: Generation of Personalized Advisory Messages: anOntology Based Approach. In: Puuronen, S., Pechenizkiy, M., Tsymbal, A.,Lee, D.J. (eds): Proc. 21st IEEE International Symposium on Computer-BasedMedical Systems, IEEE Los Alamitos (2008), 437-442[He14] Henning, P. A., Forstner, A., Heberle, F., Swertz C., Schmölz, A., Barberi, A.,Verdu, E., Regueras, L. M., Verdu, M., de Castro, J.P., Burgos, D., de la FuenteValentin, L., Gal, E., Parodi, E., Schwertel, U., Steudter, S.: Learning PathwayRecommendation based on a Pedagogical Ontology and its Implementation inMoodle, accepted for publication the DeLFI 2014 conference[KT13] Koedinger, K., Tanner, M.: 7 Things You Should Know About In-telligent Tutoring Systems, (July 2013) Retrieved 16 Dec 2013, fromhttps://net.educause.edu/ir/library/pdf/ELI7098.pdf[L01] Leidig, T.: L3 -Towards and Open Learning Environment. ACM Journal ofEducational Resources in Computing, Vol. 1 No. 1 (2001)[Me06] Meder, N.: Web-Didaktik: Eine neue Didaktik webbasierten, vernetzten Ler-nens, (Bertelsmann, Bielefeld 2006)[MWK14] Consensual figure given at the 2014 conference ”MOOCs or POOcs”, Min-istry of Science and Art, State of Baden-Wuerttemberg; Stuttgart April 2014[Ri13] Rivard, R.: Measuring the MOOC Dropout Rate (March 8 2013), Retrieved 20Dec 2013 from http://www.insidehighered.com/news/2013/03/08/researchers-explore-who-taking-moocs-and-why-so-many-drop-out[Sw13] Swertz,C., Schmölz, A., Forstner, A., Heberle, F., Henning, P.A., Streicher, A.,Bargel, B.A., Bock, J., Zander, S.: A Pedagogical Ontology as a Playgroundin Adaptive Elearning Environments. In: Horbach, M. (ed): INFORMATIK2013: Informatik angepasst an Mensch, Organisation und Umwelt, GI-EditionLecture Notes in Informatics (LNI), P-220 (2013) 1955-1960[U12] UNESCO World OER Congress releases 2012 Paris OER Declaration (UN-ESCO, Paris 2012)132

Personalized web learning by joining OER

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Personalized web learning by joining OER

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