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1 WP1 Personalized Adaptive Learning. Overview Introduction D1.10 A SECI-based framework for learning work D1.11 Integration of adaptive learning.

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Presentation on theme: "1 WP1 Personalized Adaptive Learning. Overview Introduction D1.10 A SECI-based framework for learning work D1.11 Integration of adaptive learning."— Presentation transcript:

1 1 WP1 Personalized Adaptive Learning

2 Overview Introduction D1.10 A SECI-based framework for learning work D1.11 Integration of adaptive learning processes with IMS Learning Design considering corporate requirements D1.13 Current and future perspectives for Personalized Adaptive Learning Summary

3 3 Introduction

4 4

5 WP1 Deliverables D1.4/D1.6 User interface requirements and solutions in corporate e-Learning / Specification of requirements and the state of the art in personalized adaptive learning especially regarding corporate e-Learning (Report) D1.1 Requirements and solutions for personalized adaptive learning and systematic description of personalized assessment tools (Project Grapple) D1.3 Learner models for web-based personalised adaptive learning: current solutions and open issues D1.7 Web portal for professional education D1.2 Interoperability of adaptive learning components (ET&S journal paper) D1.5 Privacy and data protection in corporate e-Learning D1.8 Specification and prototyping of personalized workplace learning (IJLT paper) D1.9 Interfacing adaptive solutions with corporate training systems (JIME paper) D1.10 A SECI-based framework for learning work D1.11 Integration of adaptive learning processes with IMS Learning Design considering corporate requirements (Online Showcase) D1.13 Current and Future Perspectives for Personalized Adaptive Learning

6 6 Deliverable 1.10 SECI-based Professional Learning Process Framework

7 KnowledgeExplicitTacit Combination Socialization Internalization Externalization The SECI spiral of knowledge creation Indiv. Group Organization

8 Knowledge Explicit Tacit Combination Socialization Internalization Externalization Dialoguing ba Exercising ba INPUT new individual understanding Systemizing ba Originating ba Community building tools Discussion supporting tools Conceptual modeling tools Reflective analysis tools Reflecting Embodying Connecting Deducing Experiencing Empathizing Articulating Conceptualizing OUTPUT new collective understanding INPUT new collective understanding OUTPUT increased collective understanding OUTPUT new individual understanding INPUT increased collective understanding OUTPUT increased individual understanding Indiv. Group Organization INPUT visions challenges activities

9 Intra SECI-based Communication Process I E I E Conscious Subconscious Explicit Tacit S CC S Formal Informal Inter I E C S

10 I E C S I E C S C S Notational Simplification

11 11 Some screenshots from the Conzilla model

12 Layers of the PLPF

13 The generic PLPF

14 Adding the Business Roles layer

15 The generic PLPF with Business Roles









24 Documenting Ambjörn’s learning about the project PROLEARN as related to the Aims, Controls, Input, Output, Support and Effects during the project at the formal and informal level A Learning Frame for Ambjörn in PROLEARN with Questions, Possible answers (“Theories”), Tests and Reflections

25 Pointing to the frame brings up information about it Pointing to the corner icons brings up information about them

26 Clicking on the frame brings out an icon of the underlying map Double-clicking on the frame opens this map


28 Generic PLPF - different perspective Individuals with Business Roles in Projects Teams in Projects


30 30 D1.11 Integration of adaptive learning processes with IMS LD considering corporate requirements

31 31 Taxonomy of Adaptive Methods What is adapted … Learning goal Content Teaching method Content Teaching style Media selection Sequence Time constraints Help Presentation Hiding Dimming Annotation … to what features… Learner Preferences Usage Previous knowledge, professional background Knowledge Interests, Goals Task Context Complexity Situational Context Position Setting “Ubiquitious Learning”, Learning on demand … and why? Didactical reasons (Salomon 75) Preference model Compensation of deficits Reduction of deficits Ergonomic reasons Efficiency Effectivness Acceptance

32 32 whatto whatwhyhow adaptive sequencing 1 sequencing learning activities tested knowledge, quiz compensation of deficits user tracking adaptive sequencing 2 introduction of interaction possibilities level of expertiseusability, focus on learning activity usage tracking adaptive presentation selection of media (DIVs) preferences, learning style compensation, acceptance user input adaptive navigation support hyperlink annotation knowledgeguidanceuser tracking adaptive navigation support 2 hyperlink annotation community activities social guidanceuser tracking, clustering... ADALE 07 workshops...

33 33 Main Elements of IMS-LD to model Adaptivity Local, Global, Group, Role Properties -> Adaptation to Knowledge, Preferences, Attributes, Group, Stereotypes Environment ->Adaptive User Interfaces and increasingly interactive learning environments Conditionals and Calculations ->Adaptive Content Presentation Roles, Monitoring Services, Notification -> Collaborative Distributed Learning, Adaptability 33

34 Three Levels of IMS LD

35 35 whatto whatwhyhow adaptive sequencing, jazz example predefined activity- structures preferences, knowledge quiz compensation of deficitsactivity structures, assessment LO, user dialogue adaptive user interface, interaction facilities introduction of environment LO, annotation possibilities, blog or wiki facilities level of expertise, number of contributions or interactions usability, focus on learning activity usage tracking, calculations, properties, environments adaptive content presentation selection of media (DIVs) preferences, learning style compensation, acceptance properties, usage tracking, condtionals, calculations tutorial navigation support hyperlink annotationteacher feedback,guidancelocal and global properties, roles, calculations social navigation supporthyperlink annotationaverage learning success of peers in same activity social guidancelocal and global properties, roles, calculations synchronized collaborative learning scaffolding activity structure peer success in learning activities blended collaborative learning local, gloabl properties, conditionals 35 Specht, M., Burgos, D. (2007). Modeling Adaptive Educational Methods with IMS Learning Design. Journal of Interactive Media in Education (Adaptation and IMS Learning Design. Special Issue, ed. Daniel Burgos), 2007/08. ISSN: X [].

36 IMS LD & Adaptation Interface based Learning flow based Content based Interactive problem solving support Adaptive information filtering Adaptive user grouping Adaptive evaluation Changes on-the-fly

37 37 Integration of Adaptation Services in heterogenous Environments

38 38 CopperCore Service Integration

39 39 Work related Scenarios

40 40

41 41 SCORM and IMS-LD

42 42 D1.13 Current and Future Perspectives for Personalized Adaptive Learning

43 D1.13 Overview Cross-relationships, Deliverables, Events, Activities, Publications Major contributions given by the PROLEARN network to Personalized Adaptive Learning Where we stand and where we are heading in Personalized Adaptive Learning Sustainability

44 Cross-relationships with other WPs WP4: Interoperability and reusability, Content Federation and PAL (MACE) WP6: Competence-driven learning, Supporting the LLL (TenCompetence) WP7: Process-oriented learning, SECI Model WP8: Survey on VCC portal (D1.8) WP9: Summer School, Master of Active Learning, Mini-Conference organized WP12: Roadmap Vision Statement 1 WP15: Social SW in PAL (Journal Paper)

45 Some WP1 Activities AH2004: PC Chair, Workshops PROLEARN Workshop on Personalized Adaptive Corporate Learning (2005) UM2005: PROLEARN Session Personalized Adaptive Learning on the Semantic Web AIED05: WS on Adaptive and Adaptable Authoring UNFOLD/PROLEARN WS on IMS Learning Design (2005) AH2006: organization, WS – SWEL, ADALE, A3H ICALT2006: ADALE & AWELS WS, Keynote, Tutorial Hypertext 2006: Adaptivity, Personalization & the Semantic Web WS UM2007: WS on Adaptive & Adaptable Authoring Hypertext 2007: Practical Hypertext track

46 Personalization: Other Projects Personal Competence Manager (TENCompetence) Contextual learning support at work (APOSDLE) Process Oriented Learning (PROLIX) Metadata for Content Enrichment (MACE, MELT) Self-organized Learning (iCamp) Project Centred Learning (COOPER) Semantic Web Learning Services (LUISA) Intelligent cognitive-based open learning (iClass) Adaptive learning spaces (ALS)

47 Key Associate Partners University of Leeds (Personalization on the Semantic Web) Simon Fraser University Surrey (Knowledge Representation) University of Nottingham (Authoring of Adaptive Hypermedia) University Belgrade (Capturing of Learner's Feedback) Vrije Universiteit Brussel (Adaptation Engineering) Salzburg Research (Emotional Intelligence in Adaptation) Athabasca University (Semantic Web in Adaptive Education) University of Cordoba (Personalized Recommendation) University of Jyväskylä (Adaptation of Feedback) Aalborg University (Semantic Web Technologies in UM)

48 Transfer of Tacit Knowledge Marcus Specht: FHG – OUNL Lora Aroyo: TU/e – Free University Amsterdam Geert-Jan Houben: TU/e – Free University Brussels Peter Dolog: L3S – Aalborg University Alexandra Cristea: TU/e – University of Warwick Milos Kravcik: FHG – OUNL Daniel Burgos: OUNL – ATOS Origin

49 49 Issues Identified

50 WP1 Issues and Challenges Standards (IMS-LD) can represent some adaptative methods, but has restrictions Context Dependent Instructional Designs and Reuse in Authoring Authoring of learning design and adaptation strategies? Interoperability demands – between systems & between different models/layers Learning standards are not harmonized – Semantic Web is used as mediator

51 Issues and Challenges (cont.) Open Corpus Adaptive Hypermedia System: operates on an open corpus of documents and LOR New LMS Architectures and PAL Service Oriented Architectures and PLE Orchestration of Services and Integration on Social Navigation Support and Personalization

52 52 where to go from here ?

53 Roadmap Vision Statement 1 Everyone (in the community of current, potential and future knowledge workers) should be able to learn anything at anytime at anyplace Goals: –Provide the right learning experiences at the right time for the target person –Everyone should have access to all public learning materials at any time at any place Actions: 1. Aggregation of learning resources 2. Production tools for learning resources 3. Contextual Delivery of Learning Resources 4. Harmonization of Learning Standards 5. Digital Identity Management 6. Business models for learning exchanges

54 54 MACE and MELT

55 GRAPPLE Project Generic Responsive Adaptive Personalized Learning Environment (3-year STREP FP7 project initiated by PROLEARN partners) The WP1 deliverables D1.1/2/3/4/6/9/11 provided most of the basis for the definition of GRAPPLE Objective: Delivering to learners a TEL environment that guides them through a life-long learning experience, automatically adapting to personal preferences, prior knowledge, skills and competences, learning goals and the personal or social context in which the learning takes place

56 Sustainability/Impact Prolearn WP1 lives on in GRAPPLE GRAPPLE will ensure that adaptive TEL technology will actually be used world-wide: –integration into Moodle, Claroline and Sakai –architecture and interfaces as generic as possible to allow easy integration into other LMSs –training, documentation and demos for authors / educators –deployment and evaluation in higher education –deployment and evaluation in some industrial cases

57 Consortium adaptive e-learning user modeling architectures authoring metadata industrial learning technology open source learning management interaction standards learning design TU/e TCD IMC ATOS GILABS OUNL USI VUB LUH/L3S UCAM UCL DFKI Warwick UniGraz evaluation blue: Prolearn core partner red: Prolearn associate partner

58 58 WP1 Personalized Adaptive Learning

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