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USING MASHUPTECHNOLOGY TO IMPROVE     FINDABILITY           Sten Govaerts        Promotor: Erik Duval     Co-promotor: Kat...
OVERVIEW• Research   outline• Music• Technology    Enhanced Learning• Publication   list• Further   planning
FINDABILITYFindability is the ability of users to  identify an appropriate websiteand navigate the pages of the site to di...
MASHUPS
MASHUPS• mashups   in music: re-mixing multiple existing songs to create a new one.
MASHUPS• mashups   in music: re-mixing multiple existing songs to create a new one.•amashup is an application that combine...
MASHUPS• mashups     in music: re-mixing multiple existing songs to create a new one.•amashup is an application that combi...
THE ROCKANANGO PROJECT
SCOPE.
SCOPE.• roots   in HORECA.
SCOPE.• roots   in HORECA.  • how   does a bartender select his music?
SCOPE.• roots   in HORECA.  • how   does a bartender select his music?  • how   does an expert select his music?
SCOPE.• roots   in HORECA.  • how    does a bartender select his music?  • how    does an expert select his music?• making...
SCOPE.• roots   in HORECA.  • how    does a bartender select his music?  • how    does an expert select his music?• making...
SCOPE.• roots   in HORECA.  • how    does a bartender select his music?  • how    does an expert select his music?• making...
SCOPE.• roots   in HORECA.  • how    does a bartender select his music?  • how    does an expert select his music?• making...
METADATA SCHEMAS
Corthaut, Nik; Govaerts, Sten; Verbert, Katrien; Duval, Erik. Connecting the dots: music metadatageneration, schemas and a...
context              subcontext A                                      subcontext B                    songs with         ...
APPLICATIONS.
APPLICATIONS.• HORECA       (+1000 pubs & restaurants)• Retail   (Fun, Prémaman, Carrefour,...)• Banks    (Dexia & Fortis)...
GENERATE THE METADATA• from    different sources:  • the audio signal  • web sources  • the Aristo database  • attention m...
ORIGIN OF AN ARTIST
METADATA GENERATION:   COUNTRY & CONTINENT• why    is it useful?  • subgenres  • popularity  • recommendations• expensive ...
METADATA GENERATION:     COUNTRY & CONTINENT • why     is it useful?   • subgenres   • popularity   • recommendations • ex...
IN THE BACKGROUND...                                              google maps                                             ...
Coverage
http://www.cs.kuleuven.be/~sten/lastonamfm
LAST.FM MASHUPhttp://www.cs.kuleuven.be/~sten/lastonamfm
FUTURE• follow up research by Markus Schedl• want to test our algorithm on his data                       setGovaerts, Ste...
CLASSIFY BY SEARCH ENGINE
ONE APPROACH...• can    classify Genre and more!•   M. Schedl, T. Pohle, P. Knees, G. Widmer,    “Assigning and Visualizin...
CLASSIFICATION WITH  SEARCH ENGINES     using co-occurrence
CLASSIFICATION WITH  SEARCH ENGINES     using co-occurrence   Artist + Genre + Schema
CLASSIFICATION WITH  SEARCH ENGINES     using co-occurrence   Artist + Genre + Schema
Rock:      Jazz:Blues:      Pop:Country:   Metal:
Rock:              Jazz:           0,013            0,013Blues:              Pop:           0,009            0,015Country:...
RESULTS• 1st   results were much worse• what   happened?• re-run   the original experiment  • evaluate   on the same data ...
WHAT TO USE?• use   Google when it’s stable else rely on Yahoo!• when    is it stable? test with a small set  • some     a...
STUDENT ACTIVITY METER
PROBLEM
PROBLEM
PROBLEM
PROBLEM
PROBLEM
OBJECTIVES• self-monitoring   for learners• awareness    for teachers• time   tracking• learning       resource recommenda...
STUDENT ACTIVITY MONITOR
STUDENT ACTIVITY MONITOR
STUDENT ACTIVITY MONITOR
3 EVALUATIONS• with   CS students• withCGIAR courses and teachers• with     Learning and Knowledge Analytics course partic...
CS STUDENTS CASE STUDY• usability    and user satisfaction evaluation• 12   CS students•2   evaluation sessions:  • task  ...
USABILITY & USER                 SATISFACTION• in   general, people understand the visualizations well!• some    small iss...
USABILITY & USER SATISFACTION
USABILITY & USER SATISFACTION
USABILITY & USER SATISFACTION
USABILITY & USER SATISFACTION
CGIAR CASE STUDY
CGIAR CASE STUDY
CGIAR CASE STUDY  wants to                          more details search for       detect outliers    on student  students ...
LAK CASE STUDY• open     course on learning and knowledge analytics• visual   analytics enthousiasts + experts (who can al...
LAK CASE STUDY• open     course on learning and knowledge analytics• visual   analytics enthousiasts + experts (who can al...
LAK CASE STUDY  verify the status of the     more metrics    classroom activity                      chronological course ...
FEDERATED SEARCH ANDSOCIAL RECOMMENDATION         WIDGET
WHAT’S A WIDGET ?!?
WHAT’S A WIDGET ?!?
WHAT’S A WIDGET ?!?
WHAT’S A WIDGET ?!?
CONTEXT• Personal   Learning Environment:  • customizable  • re-use, creation   & mashup of tools, resources• enable   use...
CONTEXT• Personal   Learning Environment:  • customizable  • re-use, creation   & mashup of tools, resources• enable   use...
CONTEXT• Personal   Learning Environment:  • customizable  • re-use, creation   & mashup of tools, resources• enable   use...
ARCHITECTURE
WIDGET
WIDGET
WIDGET
EXTENDED PAGERANK                                                                   hare                                  ...
EVALUATION• 15   PhD students at K.U. Leuven and EPFL.•   What?    • usability    • user   satisfaction    • usefulness
FIRST PHASE• current   media search tool: Google & YouTube• understanding   recommendations: 6/15 from like/dislike
SECOND PHASE
SECOND PHASE• only   14 participants (one less)•   open questions• usefulness      of recommendations: 11/14 pro.• user   ...
SECOND PHASE• only   14 participants (one less)•   open questions• usefulness      of recommendations: 11/14 pro.• user   ...
WHY THE DIFFERENT SUS?• 1st phase by 2 interviewers• issues:  • distracts   of unrelated widget’s UI updates.  • layout   ...
DESIRABILITY...
DESIRABILITY...
DESIRABILITY...
FURTHER EVALUATION• evaluation   at a company and university
PUBLICATIONS: MUSIC•   Govaerts, Sten; Corthaut, Nik; Duval, Erik. Moody tunes: the rockanango project, Lemström, Kjell; T...
ISMIRI think ISMIR was motivated from two directions: the desire for a focus on music indexing, search, and retrieval, whi...
PUBLICATIONS: TEL•   Parra Chico, Gonzalo; Govaerts, Sten; Duval, Erik. More! a social discovery tool for researchers, DIR...
PUBLICATIONS IN THE                        PIPELINE•   Felix Mödritscher, Barbara Krumay, Sten Govaerts, Erik Duval, Sandy...
FUTURE PLANNING• Ph.D. on    papers• if   2 papers under review are accepted => FINISH.• potential   for writing (a) journ...
THANK YOU!                QUESTIONS?slides will appear on http://www.slideshare.net/stengovaerts
Using mashup technology to improve findability
Using mashup technology to improve findability
Using mashup technology to improve findability
Using mashup technology to improve findability
Using mashup technology to improve findability
Using mashup technology to improve findability
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Using mashup technology to improve findability

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  • musical atmosphere: is this computable?\n
  • musical atmosphere: is this computable?\n
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  • 1. MG is better than MS, a possible explanation is that style is a broader term than genre for music\n2. Google outperforms Yahoo! & Live!\n3. results fluctuate over time\n4. technical issues with Yahoo! only a fraction of the artists are retrieved\n
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  • correct: light\nincorrect: dark\n\n1. yahoo most stable\n2. google changes most often.\n3. changing from correct to incorrect occurs most, but no clear pattern\n4. Live seems to struggle with the same artists, one time they do it correctly, the next time wrong.\n
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  • Transcript of "Using mashup technology to improve findability"

    1. 1. USING MASHUPTECHNOLOGY TO IMPROVE FINDABILITY Sten Govaerts Promotor: Erik Duval Co-promotor: Katrien Verbert
    2. 2. OVERVIEW• Research outline• Music• Technology Enhanced Learning• Publication list• Further planning
    3. 3. FINDABILITYFindability is the ability of users to identify an appropriate websiteand navigate the pages of the site to discover and retrieve relevant information resources. Peter Morville (2005)
    4. 4. MASHUPS
    5. 5. MASHUPS• mashups in music: re-mixing multiple existing songs to create a new one.
    6. 6. MASHUPS• mashups in music: re-mixing multiple existing songs to create a new one.•amashup is an application that combines data from multiple online sources to create a new result which was not the original intend of the data.
    7. 7. MASHUPS• mashups in music: re-mixing multiple existing songs to create a new one.•amashup is an application that combines data from multiple online sources to create a new result which was not the original intend of the data.• data is key! • tweaking and enriching data is important • interesting data makes an interesting mashup
    8. 8. THE ROCKANANGO PROJECT
    9. 9. SCOPE.
    10. 10. SCOPE.• roots in HORECA.
    11. 11. SCOPE.• roots in HORECA. • how does a bartender select his music?
    12. 12. SCOPE.• roots in HORECA. • how does a bartender select his music? • how does an expert select his music?
    13. 13. SCOPE.• roots in HORECA. • how does a bartender select his music? • how does an expert select his music?• makingthe expert data accessible in a usable way for a bartender
    14. 14. SCOPE.• roots in HORECA. • how does a bartender select his music? • how does an expert select his music?• making the expert data accessible in a usable way for a bartenderA musical context is a musical description for situations based on atmospheres and musical properties.
    15. 15. SCOPE.• roots in HORECA. • how does a bartender select his music? • how does an expert select his music?• making the expert data accessible in a usable way for a bartenderA musical context is a musical description for situations based on atmospheres and musical properties.
    16. 16. SCOPE.• roots in HORECA. • how does a bartender select his music? • how does an expert select his music?• making the expert data accessible in a usable way for a bartenderA musical context is a musical description for situations based on atmospheres and musical properties.
    17. 17. METADATA SCHEMAS
    18. 18. Corthaut, Nik; Govaerts, Sten; Verbert, Katrien; Duval, Erik. Connecting the dots: music metadatageneration, schemas and applications, Bello, Juan Pablo; Chew, Elaine; Turnbull, Douglas (eds.), ISMIR,Philadelphia, USA, 14-18 September 2008, Proceedings of the 9th International Conference on MusicInformation Retrieval, pages 249-254
    19. 19. context subcontext A subcontext B songs with songs with subgenre(easy listening genre(pop) OR pop café) + + 75 25 songs with songs with mood(intimate OR mood(relax OR tasteful OR stylish) + romantic OR sensual) + + songs with songs with popularity(5 UNTIL 7) popularity(6 UNTIL 7)Govaerts, Sten; Corthaut, Nik; Duval, Erik. Moody tunes: the rockanango project, Lemström, Kjell;Tindale, Adam; Dannenberg, Roger (eds.), International Conference on Music Information Retrieval,ISMIR, Victoria, BC, 8-12 October 2006, pages 308-313, University of Victoria
    20. 20. APPLICATIONS.
    21. 21. APPLICATIONS.• HORECA (+1000 pubs & restaurants)• Retail (Fun, Prémaman, Carrefour,...)• Banks (Dexia & Fortis)• Webradio (HUMO, TMF, Mars, Overtoom, ...)
    22. 22. GENERATE THE METADATA• from different sources: • the audio signal • web sources • the Aristo database • attention metadata• using our metadata generation framework: SamgI
    23. 23. ORIGIN OF AN ARTIST
    24. 24. METADATA GENERATION: COUNTRY & CONTINENT• why is it useful? • subgenres • popularity • recommendations• expensive to annotate• very few existing research• very hard with signal processing
    25. 25. METADATA GENERATION: COUNTRY & CONTINENT • why is it useful? • subgenres • popularity • recommendations • expensive to annotate • very few existing research! • very hard with signal processing
    26. 26. IN THE BACKGROUND... google maps freebase last.fm google app enginetwitter last.fmlast.fm yahoo! pipes last on am/fmwebsite dapper youtube
    27. 27. Coverage
    28. 28. http://www.cs.kuleuven.be/~sten/lastonamfm
    29. 29. LAST.FM MASHUPhttp://www.cs.kuleuven.be/~sten/lastonamfm
    30. 30. FUTURE• follow up research by Markus Schedl• want to test our algorithm on his data setGovaerts, Sten; Duval, Erik. A Web-based approach to determine the origin of an artist, ISMIR, Kobe,Japan, 26-30 October 2009, Proceedings of ISMIR2009: 10th International Society for Music InformationRetrieval Conference, pages 261-266, ISMIR-The International Society for Music Information Retrieval
    31. 31. CLASSIFY BY SEARCH ENGINE
    32. 32. ONE APPROACH...• can classify Genre and more!• M. Schedl, T. Pohle, P. Knees, G. Widmer, “Assigning and Visualizing Music Genres by Web-based Co-occurrence Analysis”, Proceedings of the 7th International Conference on Music Information Retrieval, 2006, pp. 260-265.• G. Geleijnse, J. Korst, "Web-based Artist Categorization", Proceedings of the 7th International Conference on Music Information Retrieval, 2006, pp. 266 - 271.
    33. 33. CLASSIFICATION WITH SEARCH ENGINES using co-occurrence
    34. 34. CLASSIFICATION WITH SEARCH ENGINES using co-occurrence Artist + Genre + Schema
    35. 35. CLASSIFICATION WITH SEARCH ENGINES using co-occurrence Artist + Genre + Schema
    36. 36. Rock: Jazz:Blues: Pop:Country: Metal:
    37. 37. Rock: Jazz: 0,013 0,013Blues: Pop: 0,009 0,015Country: Metal: 0,009 0,005
    38. 38. RESULTS• 1st results were much worse• what happened?• re-run the original experiment • evaluate on the same data set: 1995 artists and 9 genres.• different search engines: Google,Yahoo! and Live! Search.• over time: 8 times over a period of 36 days.
    39. 39. WHAT TO USE?• use Google when it’s stable else rely on Yahoo!• when is it stable? test with a small set • some artists get classified incorrectly on bad days • compare the accuracy achieved with the test set to the average. Govaerts, Sten; Corthaut, Nik; Duval, Erik. Using search engine for classification: does it still work?, AdMIRe: International Workshop on Advances in Music Information Research 2009, San Diego, USA, 14-16 December 2009, Proceedings of AdMIRe: International Workshop on Advances in Music Information Research 2009, pages 483-488, IEEE
    40. 40. STUDENT ACTIVITY METER
    41. 41. PROBLEM
    42. 42. PROBLEM
    43. 43. PROBLEM
    44. 44. PROBLEM
    45. 45. PROBLEM
    46. 46. OBJECTIVES• self-monitoring for learners• awareness for teachers• time tracking• learning resource recommendation
    47. 47. STUDENT ACTIVITY MONITOR
    48. 48. STUDENT ACTIVITY MONITOR
    49. 49. STUDENT ACTIVITY MONITOR
    50. 50. 3 EVALUATIONS• with CS students• withCGIAR courses and teachers• with Learning and Knowledge Analytics course participants
    51. 51. CS STUDENTS CASE STUDY• usability and user satisfaction evaluation• 12 CS students•2 evaluation sessions: • task based interview with think aloud (after 1 week of tracking) • user satisfaction (SUS & MSDT) (after 1 month)Govaerts, Sten; Verbert, Katrien; Klerkx, Joris; Duval, Erik. Visualizing activities for self-reflectionand awareness, ICWL10: International Conference on Web based Learning, Shanghai, China, 7-11December 2010, Lecture Notes in Computer Science, volume 6483, pages 91-100, Springer
    52. 52. USABILITY & USER SATISFACTION• in general, people understand the visualizations well!• some small issues were uncovered...• average SUS score: 73% (stdv: 9,35)
    53. 53. USABILITY & USER SATISFACTION
    54. 54. USABILITY & USER SATISFACTION
    55. 55. USABILITY & USER SATISFACTION
    56. 56. USABILITY & USER SATISFACTION
    57. 57. CGIAR CASE STUDY
    58. 58. CGIAR CASE STUDY
    59. 59. CGIAR CASE STUDY wants to more details search for detect outliers on student students good indicator for effort understand the workloadmore metrics use for course design optimizationobtain course overview compare students increase awarenesswant better 1 to 1 progress evolution comparison tool
    60. 60. LAK CASE STUDY• open course on learning and knowledge analytics• visual analytics enthousiasts + experts (who can also teach)
    61. 61. LAK CASE STUDY• open course on learning and knowledge analytics• visual analytics enthousiasts + experts (who can also teach)
    62. 62. LAK CASE STUDY verify the status of the more metrics classroom activity chronological course dwell timeself-reflection to measure progress and increase motivation find students experiencing problems and low engagement more data
    63. 63. FEDERATED SEARCH ANDSOCIAL RECOMMENDATION WIDGET
    64. 64. WHAT’S A WIDGET ?!?
    65. 65. WHAT’S A WIDGET ?!?
    66. 66. WHAT’S A WIDGET ?!?
    67. 67. WHAT’S A WIDGET ?!?
    68. 68. CONTEXT• Personal Learning Environment: • customizable • re-use, creation & mashup of tools, resources• enable users to access content • in different contexts
    69. 69. CONTEXT• Personal Learning Environment: • customizable • re-use, creation & mashup of tools, resources• enable users to access content • in different contexts
    70. 70. CONTEXT• Personal Learning Environment: • customizable • re-use, creation & mashup of tools, resources• enable users to access content • in different contexts
    71. 71. ARCHITECTURE
    72. 72. WIDGET
    73. 73. WIDGET
    74. 74. WIDGET
    75. 75. EXTENDED PAGERANK hare d R1 d /s save saved/shared d en tion Sten R2 fri ec dis n like c on d lik ed R3 d n Sandy ien ctio fr e c on n lik ed R4Erik R5
    76. 76. EVALUATION• 15 PhD students at K.U. Leuven and EPFL.• What? • usability • user satisfaction • usefulness
    77. 77. FIRST PHASE• current media search tool: Google & YouTube• understanding recommendations: 6/15 from like/dislike
    78. 78. SECOND PHASE
    79. 79. SECOND PHASE• only 14 participants (one less)• open questions• usefulness of recommendations: 11/14 pro.• user satisfaction: System Usability Scale (SUS) & MS Desirability Toolkit• SUS score: 66,25% •2 groups
    80. 80. SECOND PHASE• only 14 participants (one less)• open questions• usefulness of recommendations: 11/14 pro.• user satisfaction: System Usability Scale (SUS) & MS Desirability Toolkit• SUS score: 66,25% •2 K.U. Leuven: high (75%) groups EPFL + one K.U.Leuven: low (50%)
    81. 81. WHY THE DIFFERENT SUS?• 1st phase by 2 interviewers• issues: • distracts of unrelated widget’s UI updates. • layout too dense • height of widgets too small• KULeuven student had prior experience with iGoogle.• not evaluating the widget but the whole experience...
    82. 82. DESIRABILITY...
    83. 83. DESIRABILITY...
    84. 84. DESIRABILITY...
    85. 85. FURTHER EVALUATION• evaluation at a company and university
    86. 86. PUBLICATIONS: MUSIC• Govaerts, Sten; Corthaut, Nik; Duval, Erik. Moody tunes: the rockanango project, Lemström, Kjell; Tindale, Adam; Dannenberg, Roger (eds.), International Conference on Music Information Retrieval, ISMIR, Victoria, BC, 8-12 October 2006, International Conference on Music Information Retrieval, ISMIR, pages 308-313, University of Victoria• Govaerts, Sten; Corthaut, Nik; Duval, Erik. Mood-ex-machina: towards automation of moody tunes, Dixon, Simon; Bainbridge, David; Typke, Rainer (eds.), International Conference on Music Information Retrieval, ISMIR, Vienna, Austria, 23-27 September 2007, Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007, pages 347-350, Österreichische Computer Gesellschaft• Corthaut, Nik; Govaerts, Sten; Verbert, Katrien; Duval, Erik. Connecting the dots: music metadata generation, schemas and applications, Bello, Juan Pablo; Chew, Elaine; Turnbull, Douglas (eds.), ISMIR, Philadelphia, USA, 14-18 September 2008, Proceedings of the 9th International Conference on Music Information Retrieval, pages 249-254• Corthaut, Nik; Lippens, Stefaan; Govaerts, Sten; Duval, Erik; Martens, Jean-Pierre. The integration of a metadata generation framework in a music annotation workflow, ISMIR, Kobe, Japan, 26-30 October 2009, Proceedings of ISMIR2009: 10th International Society for Music Information Retrieval Conference, ISMIR-The International Society for Music Information Retrieval• Govaerts, Sten; Duval, Erik. A Web-based approach to determine the origin of an artist, ISMIR, Kobe, Japan, 26-30 October 2009, Proceedings of ISMIR2009: 10th International Society for Music Information Retrieval Conference, pages 261-266, ISMIR-The International Society for Music Information Retrieval• Govaerts, Sten; Corthaut, Nik; Duval, Erik. Using search engine for classification: does it still work?, AdMIRe: International Workshop on Advances in Music Information Research 2009, San Diego, USA, 14-16 December 2009, Proceedings of AdMIRe: International Workshop on Advances in Music Information Research 2009, pages 483-488, IEEE
    87. 87. ISMIRI think ISMIR was motivated from two directions: the desire for a focus on music indexing, search, and retrieval, which cuts across many disciplines, and a desire for a focused technical and scientific forum for music research. -Roger Dannenberg It is not just statistics and computer science (as Wikipedia explains for "bioinformatics") but also many other aspects, including social, musicological, perceptual etc. ones. -Michael Fingerhut
    88. 88. PUBLICATIONS: TEL• Parra Chico, Gonzalo; Govaerts, Sten; Duval, Erik. More! a social discovery tool for researchers, DIR 2010: Dutch- Belgian Information Retrieval Workshop, Nijmegen, Nederland, 25 January 2010, DIR 2010: 10th Dutch-Belgian Information Retrieval Workshop• Renzel, D.; Hobelt, C.; Dahrendorf, D.; Friedrich, M.; Modritscher, F.; Verbert, Katrien; Govaerts, Sten; Palmer, M.; Bogdanov, E.. Collaborative development of a PLE for language learning, International Journal of Emerging Technologies in Learning, volume 5, 2010• Govaerts, Sten; Verbert, Katrien; Klerkx, Joris; Duval, Erik. Visualizing activities for self-reflection and awareness, ICWL10: International Conference on Web based Learning, Shanghai, China, 7-11 December 2010, Lecture Notes in Computer Science, volume 6483, pages 91-100, Springer• Govaerts, Sten; El Helou, Sandy; Duval, Erik; Gillet, Denis. A federated search and social recommendation widget, Proceedings of the 2nd International Workshop on Social Recommender Systems (SRS 2011) in conjunction with the 2011 ACM Conference on Computer Supported Cooperative Work (CSCW 2011), Hangzhou, China, 19-23 March 2011, pages 1-8.
    89. 89. PUBLICATIONS IN THE PIPELINE• Felix Mödritscher, Barbara Krumay, Sten Govaerts, Erik Duval, Sandy El Helou, Denis Gillet, Alexander Nussbaumer, Dietrich Albert, Carsten Ullrich. May I suggest? Three PLE recommender strategies in comparison, PLE Conference 2011, Southampthon, UK. => ACCEPTED, not a core part of my PhD.• Govaerts, Sten; Verbert, Katrien; Duval, Erik. Visualizing student activities for teachers, IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), Providence, USA => UNDER REVIEW, notification 8 June.• Sten Govaerts, Katrien Verbert, Daniel Dahrendorf, Carsten Ullrich, Manuel Schmidt, Michael Werkle, Arunangsu Chatterjee, Alexander Nussbaumer, Dominik Renzel, Maren Scheffel, Martin Friedrich, Jose Luis Santos, Effie L-C Law, Erik Duval. Towards reponsive open learning environments: the ROLE interoperability framework. The 6th European Conference on Technology Enhanced Learning Towards Ubiquitous Learning, Lecture Notes of Computer Science. => UNDER REVIEW, notification 31 May.
    90. 90. FUTURE PLANNING• Ph.D. on papers• if 2 papers under review are accepted => FINISH.• potential for writing (a) journal article(s): •0 articles: submit end September •1 article: submit end November •2 articles: submit Xmas.
    91. 91. THANK YOU! QUESTIONS?slides will appear on http://www.slideshare.net/stengovaerts
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