1. September 15 -17, 2010 Hasselt, Belgium Defining Ontology Specification for Personal Learning Environment Forming MalinkaIvanova, Technical University – Sofia Mohamed Amine Chatti, RWTH Aachen University

2. Aim Creation of a tool for conceptual understanding of Personal Learning Environment before its bulding, based on exsting experience and exploration of the students‘ opinions, aspects and viewpoints

3. Outline existing PLE approaches needs analysis ontology presentation PLEF specification conclusion

4. PLE approaches The vision of PLE is still forming Aim: supporting deep and meaningful engagement of students in a learning process and attempting to enhance self-organized learning several solutions exist, especially created for educational purposes

5. PLEFRWTH Aachen University Conceptual view

6. PLEFRWTH Aachen University Prototype

7. GraaspEPFL Lausanne Conceptual view

8. GraaspEPFL Lausanne Prototype

9. Microblog cirip.euTimsoft, Romania

10. Microblog cirip.euTimsoft, Romania Prototype

11. Graz University of Technology PLE integration

12. Graz University of Technology Prototype

13. PLExusNorwegian University of Science andTechnology Conceptual model is built around the use of topic maps topic maps are suitable as the core of a powerful PLE with information administration, search and navigation

14. PLExusNorwegian University of Science andTechnology Prototype

15. Ontology presentation The concepts related to the logical and technical functionality of PLE building can be presented through ontologies Such presentation of information is chosen because it proposes a powerful method for organizing, retrieving and interacting with the included data a model for conceptual understanding of PLE and conceptual self-understanding of students’ needs using ontology apparatus is proposed

16. Related work ontology usage for modelling of personalization in an eLearning environment: for modelling of learner profile in support of course structure design, for monitoring and evaluating of learner behaviour HadjM'tir, R.; Jeribi, L.; Rumpler, B.: Ontology-based Modeling for Personalized E-Learning, 2007

17. Related work for describing the knowledge about student learning styles, student performance, and student data in context of the personalization in eLearning system Pramitasari, Hidayanto, Aminah, Krisnadhi, Ramadhani, 2009

18. Related work for describing the features of domains, users, and observations in support of dynamically generating personalized hypertext relations Personal reader Henze, Dolog ,Nejdl, 2004

19. Phases of PLE Creation  there is no approach reflecting on exploration and analysis of students’ preferences, learning styles and needs in the point of view of their preliminary preparation and conceptually understanding of technical and pedagogical aspects of PLE building

20. Students ask? Why do I have to possess a Personal Learning space? What does PLE mean? Which technologies are suitable? How to organize available tools? How will it support my interests and learning?

21. Why is the phase “PLE conceptual understanding” important when PLE is examined as “bottom up approach”?

22. PLE conceptual understanding Self-cognition Self-answering of questions about learning needs and goals preferred media content format for learning preferred communication channels lead to clearing of methods for information and knowledge absorbing and remembering

23. PLE conceptual understanding Self-organization increased individual control over learning and self-management through a process that involves choice of scenario learning recourses selection recording thoughts reflecting on thoughts engaging in learning conversations with others about one's own learning

24. PLE conceptual understanding Self-planning of personal development personal change , progress personal development a self improvement plan (strategy) based on understanding about the current and future professional positions, readiness for actions and awareness of potentials for realization  successful personal development

25. PLE conceptual understanding Self-competence realization Self-competence - sense of a student to be capable, effective and in self-control It is result from successful management of the learning environment and from achievement of needs and goals

26. PLE conceptual understanding a tool for modeling the understanding of PLE is useful: for students who for first time will be introduced with the PLE concept for self-organized learners and life-long learners who wish to improve their competences it is designed via ontology apparatus and used to support the PLE conceptual specification understanding of a student group during one semester

27. Ontologies overview ontology in the field of computer science is defined as a model for describing the world that consists of a set of classes, properties, and relationship types ontology as an explicit specification of a conceptualization a conceptualization is an abstract and simplified view of the concepts and their relationships

28. Ontologies overview may vary in their content, structure and implementation can be more complex including distinguished properties or properties that can define new concepts differ in respect to the scope and purpose of their content consist of a terminological component (XML document) and an assertional component can be realized in a number of languages - DAML+OIL (DARPA Agent Markup Language), OWL (Web Ontology Language) the PLE ontology specification has been developed using the software platform of AltovaSemanticWorks

29. Methodology Steps for PLE modeling based on ontologies: (1) Domain knowledge building – collecting of suitable information to define the terms used formally to describe the PLE (2) Design the ontologies’ structure – identifying the concepts, instances and their properties in OWL language (3) Further detailization - adding the concepts, relations, and instances to the level of detail clearing the ontology (4) Checking of ontology correctness - ensuring syntactic, logical, and semantic inconsistencies among the ontology elements (5) Verification of the ontology

30. Domain Knowledge Building In this step: the information gathered by students via surveys is analyzed with aim to forming the terminology for PLE ontology specification

31. Domain Knowledge Building How do you self-rate the level of your computer literacy? Result : (1) The majority of male and female students say that their ICT knowledge and skills are good (75% female, 62,5% male); (2) There is no students’ self-rating with average and poor computer literacy level

32. Domain Knowledge Building How often do you use applications for document processing, media files processing, emails composing, news reading, information searching, participation in social networks? Result: 100% male students several times/day use search engines the more used applications by 87,5% male students (several times/day) are for reading/sending emails, files sharing and news reading and a small percentage of students have never participated in social networks The frequency of computer and Internet applications usage by male students

34. Domain Knowledge Building What motivates you to use information and computer technologies? Result: The main reason for self motivation of 62,5% males is “to understand the new technology and to study it” For 50% of female students, the main driving forces are: “to understand the new technology and to study it” and “want to advance my ICT skills and knowledge” 50% male students are motivated to use ICT technologies when they have to “improve their ICT skills and knowledge and when they “can produce their own artifact”

35. Domain Knowledge Building How do you sort by frequent usage the communication tools? Result: GSM, email, social networks, skype, web forums, messengers the search engines are the main applications for information gathering of 100% male, after this the free accessed web sites, created by users (Wikipedia, online dictionaries) are used by 50% male students The 12,5% males male students rated themselves at using social bookmarking sites Order of communication tools by male students

36. Domain Knowledge Building How do you sort by frequent usage the communication tools? Result: GSM, email, skype, social networks, web forums, and messengers the search engines are the main applications for information gathering of 75% female students, after this the free accessed web sites, created by users are used by 50% female The 50% female students rated themselves much better using social bookmarking sites putting them on the third position Order of communication tools by female students

37. Domain Knowledge Building Order of information gathering tools by male students

38. Domain Knowledge Building Order of information gathering tools by female students

39. Domain Knowledge Building Ways for better understanding the new information Result: 75% male students understand information better “when it is supported with graphs and pictures” and 62,5% male students prefer “information presented with video and animations”, “interactive activities” and when “somebody explains it” 50% females like “information with pictures and graphics” and when “the text is well formatted” The 75% male students learn better when “the text includes many pictures” and 50% of them point that combination of: “text with many images”, “lessons’ listening” “explain and discuss”, interactive activities performing” is a good solution for better learning

40. Domain Knowledge Building Activities performance at interesting information found Result: 87,5% male students and 50% female students “comment with friends and colleagues offline and online” the interesting information they found out; 37,5% male students think about this interesting information, send the link to somebody and add it in favorite bookmarks; 25% female students think about this interesting information and send the link to somebody

41. Domain Knowledge Building Preferable functions of a personal learning environment

42. Domain Knowledge Building Other functions of a personal learning environment

43. Survey - conclusion the male/female students possess good computer knowledge and skills they have affinity to new technologies they are self-motivated to advance skills and knowledge they are striving and working for personal development many of them perceive information in a visual way and others prefer a combination of methods for a better understanding of the new information  Before starting PLE building the students formed a conceptual vision about functionally and technologies

44. PLEF Ontology Specification Personal Learning Environment Framework is originally designed and developed in RWTH Aachen, Germany consists of back-end office, containing available for learners components front-end office giving access to PLEF and interface for creation of virtual learning space

45. PLEF Ontology Specification Back-End Office OpenID authorization the PLE might have a title Each PLE consists of one or more pages each page might consist of zero or more elements The elements can be specialized into feed, OPML, text, image, linklist and widget Every element can have zero or more tags while one tag might appear on one or more elements Each element can also have zero or more comments A comment has an author, content and date of creation

46. PLEF Ontology Specification Front-End Office Interface: consists of north, west, east and center panels north - login/logout, add new PLE pages, and show the element insertion panel in the west panel west panel - insert new elements into a PLE east panel - viewing and searching of PLE elements center panel shows the elements either organized in pages or grouped based on tags

47. PLEF Ontology Specification Drag-and-drop action supported in both center and east panels in the center panel, it enables the learner to change the position of elements within pages east panel - it enables to move elements between pages and to change the order of the pages within a PLE

48. PLEF Ontology Specification Authentication: OpenID for authentication to access/create a PLE or comment on a specific PLE element

49. PLEF Ontology Specification Social Tagging, Commenting and Sharing: Each element in PLEF can be associated with different tags The learners are able to give comments to each element, they can login as anonymous or via their OpenIDs PLE pages and elements can be shared via email

50. PLEF Ontology Specification Views: traditional page view tag view of all elements in a PLE

51. PLEF Ontology Specification Access Control: PLEF enables access control at both PLE page and element levels Newly inserted elements or pages are automatically set as private and can then be made as public

52. PLEF Ontology Specification Pages and Elements: a learner organizes the learning resources into pages a page panel consists of three parts: tab, toolbar and body a page encompasses several elements: feed, OPML, text, image, linklist, and widget. an element panel consists of three parts: header, body and footer

53. PLEF Ontology Specification Search: PLEF enables full-text and tag-based search of all elements in a PLE Search is performed in the page/tag view of the east panel If a sequence of characters is detected by the search field’s listener, deep first search will be performed on the tree in the page/tag view to find all pages and elements which contains the exact string

54. Experimentation with PLEF If the panels/components PLEF’ structure is a good strategy for learning spaces building? Result: (1) 75% of male and female students are categorical that panels/components structure is a good solution to satisfy their learning interests (2) 25% of them answered “it depends” that means their conceptual view is a little bit different than this

55. Experimentation with PLEF If the proposed functionality is enough for self-learning organizing? Both male/female students are agree that possibilities for links, RSS feeds, text, images, media files adding, and also hml/JavaScript embedding give huge opportunities for the PLE organization based on different learning styles

56. Experimentation with PLEF The PLE has to consist of how panels and how components on each panel to satisfy an effective information perception? Results: 50% female students and 75% male students prefer 4-7 panels/6 and more components on each panel to effectively organize their learning space 12,5% male students said that “3 panels/2 components on each panel” and other 12,5% male students prefer more than “8 panels/6 and more components on each panel” 25% female students rate for “3 panels/6 and more components on each panel” and other 25% for “8 panels/6 and more components on each panel”

57. Ontologies Design the classes, instances, properties are identifies the relationships are defined three main classes are created: Affinity to technology, Information understanding and PLE functions in three different namespaces: tech, info and ple several instances for each class are formed, the properties related to different instances are designed

58. Further detailization All needed class’ instances and properties are created to form the vision about the PLE building according to students’ answers and opinion The class tech: Affinity to technology

59. Further detailization The class info:Information understanding

60. Further detailization The class PLE functions

61. Checking of ontology correctness and ontology verification the syntax and semantics of the ontology is checked the relationships between properties and instances, and instances and classes is performed are checked in text view of AltovaSemanticWorks the documents in their RDF/XML notation are displayed and edited – when it is necessity the verification is especially important when connecting the proposed ontology structure with other ones that may enhance the PLE ontology specification

62. Conclusion The comparison between students’ expectations and PLEF specification is done Students have formed understanding about PLE technical and functional aspects Several of them possess a little bit different view comparing with PLEF

63. Conclusion The created PLE model could be applied to any functional and technical solution of PLE, because it gives possibilities for: self-cognition of preferred learning style, learning objectives, needs from competence development by students and for understanding the conceptual base of PLE Future work will be focused on: verification and updating of the PLE specification model working with new students’ groups applying the PLE specification to help students build their PLEs, using different PLE development tools using the PLE specification for the development of new PLE solutions

64. Thank you for your attention! For contacts: m_ivanova@tu-sofia.bg chatti@cs.rwth-aachen.de