Learning Analytics at Large: the Lifelong Learning Network of 160, 000 European Teachers

TeLLNet EC-TEL EC TEL 2011 Learning Analytics at Large: the Lif l th Lifelong Learning Network L i N t k of 160, 000 European Teachers Ergang Song, Zinayida Petrushyna, Yiwei Cao, and Ralf Klamma Information Systems and Databases, RWTH Aachen University Palermo, Italy September 23 2011 23,Lehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-1

TeLLNet Motivations  How to support lifelong learning (LLL)? – New means for LLL with rapid development of ICT (Meta-) – Competence assessment methods for LLL in demand Competence management – Self-monitoring f LLL needed S lf it i for d d – Still lack of large data sets Self- monitoring – Tools are needed instead of a concept  Case study: eTwinning Network Learning analytics for lifelong learning – Continuous professional development for teachers p p – Aiming to promote collaborations among schools – Competence gap to recognize and to bridge – Meta-competence  Learning analytics is needed – Vi l analytics for self-monitoring Visual l ti f lf it iLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke – Multiple levels (individual, community, and network) I5-SPCK-0911-2

TeLLNet Learning Analytics Learning analytics is the measurement, collection, analysis and reporting of data about learners and their ea ning s e easu e e , co ec o , a a ys s a d epo g o da a abou ea e s a d e contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. (Siemens, 2011)  Visual analytics – It is easier for teachers to understand visualization than statistics (Breuer et al., 2009)  Data analysis  Learning context analysis (Cao et al., 2010)  Network analysis  The EC-TEL communitiesLehrstuhl Informatik 5 as an e ample example(Informationssysteme) Prof. Dr. M. Jarke (Pham et al., 2011) I5-SPCK-0911-3

TeLLNet Learning Analytics Contributions to EC TEL so far EC-TEL  2006 - Klamma, Spaniol, Cao, Jarke: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe – Media Bases as research tools for TEL – SNA as research methodology for TEL  2008 - Petrushyna, Klamma: No Guru, No Method, No Teacher: Self-Observation and Self-Modelling of E-Learning Communities – In-depth Analysis of a Media Base for TEL – Combination of SNA and content-based measures  2009 - Breuer, Klamma, Cao, Vuorikari: Social Network Analysis of 45.000 Schools: A Case Study of Technology Enhanced Learning in Europe – eTwinning database of European cooperation between schools – SNA as a tool for teachers – Visualization and Usability  2010 – Petrushyna: Self-modeling and Self-reflection of E-learning communitiesLehrstuhl Informatik 5 (Doctoral Consortium)(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-4

TeLLNet TeLLNet Project Teachers Lifelong Learning Networks eTwinning T i i TwinSpace T i S TeLLNet T LLN t • Founded in 2005 • Since 2008? •3-year-project within the EU • Coordinated by European • Subject to eTwinning Lifelong Learning Schoolnet S h l t • Web 2 0 for T i i W b 2.0 f eTwinning Programme (2009-2012) • Internet platform with • Blogs •Project obejctives: workspace and Competence development • Quality labels (communication) tools for teachers in learning • Desktop tools networks with social network • P j t must be done b Projects tb d by two or more partners from analysis and scenario different countries building based on eTwinning • Offline activities: • Partners Workshops across Europe • European Schoolnet • RWTH Aachen University • Open University of the Netherlands • Institute for Prospective Technological Studies (IPTS) –Joint Research Centre of the European CommissionLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-5

TeLLNet Competence and Meta-Competence  Developed in lots of areas: Author Definition Human resource management management, M Cl ll d McClelland Th k l d kill i i d The knowledge, skills, traits, attitudes, vocational education ... (1973) self‐concepts, values, or motives directly related to job performance or important  Different definitions in literatures life outcomes and shown to differentiate between superior and average b i d  Common points performers.  A set of human characteristics Brown and A meta‐competence is the overarching McCartney ability under which competence shelters. (knowledge, skills, abilities...) (knowledge skills abilities ) (1995) ( ) It embraces the higher order abilities b h h h d bl  The performances to enhance which have to do with being able to learn, adapt, anticipate and create. Meta‐  Categorized into different types competences are a prerequisite for the  Assessment methods development of capacities such as d l f h judgment, intuition and acumen upon  Explicit assessment (questionnaire, test) which competences are based and without which competences cannot  Implicit assessment flourish fl i h  Events to monitor Cheetham Meta‐competence is the competence that and Chivers is beyond other competences, and which  Algorithms to design (2005) enables individuals to monitor and/or  Competence to computer C t t t develop other competences d l hLehrstuhl Informatik 5(Informationssysteme)  Automated executable without participation Prof. Dr. M. Jarke I5-SPCK-0911-6 of questionnaires

TeLLNet Teachers’ Competence in eTwinning  eTwinning Network g  Our meta-competence (as of the end of 2010) – Higher order competence Teacher Amount % Sum 135,351 100% – Competence to monitor and Project with projects 26,365 19.4% develop other competences with QLs 2,093 1.55% – Depends on context with EQLs 616 0.46% – Ability to self-monitoring is y g with prizes 655 0.48% meta-competence in the context Wall post Wall posts sent 10,104 7.47% of LLL Meta Wall posts 18,986 14.03% competence received i d Self- monitoring Blog Posts written 4,508 3.33% ability etence Post comments 441 0.33% Language Wall-post writing written onal compe competence t ability bilit ompetence e Post comments 727 0.54% received Blog writing Project Comment Project comments 1,531 1.13% performance ability Professio Social co written ittLehrstuhl Informatik 5 Prize comments 354 0.26% Project efficiency Comment writing(Informationssysteme) Prof. Dr. M. Jarke written etc. ability, etc. I5-SPCK-0911-7

TeLLNet Data Analysis: Large-Scale Data Set of eTwinning  New tables generated Table Name Table Name Records Records Error Error  New data for Web 2.0 number Rate – Blogs (TwinBlogPost) Affectation 99886 0.00002 Institution 71988 0 – Comments (TwinBlogComment, MyContact 464780 0.000037 PrizeComment) Prize 892 0.0045 – Labels (QualityLabel) PrizeComment 441 0.0091 0 0091 – Tagging, etc. Project 17392 0 ProjectGuestBook 3460 0.009 – ProjectMember ProjectMember 66145 0 0045 0.0045 – ProjectGuestBook QualityLabel 4886 0  Data cleaning Teacher 133693 0 TeacherWall 34900 0.00014 0 00014  Data dumps TwinBlog 15235 0.00013 – 1st Dump (June, 2010) TwinBlogComment 2950 0.2783Lehrstuhl Informatik 5 – 2nd Dump (November, 2010) 2 ( TwinBlogPost T i Bl P t 31163 0.00064 0 00064 Sum 947811 0.0013(Informationssysteme) Prof. Dr. M. Jarke – 3nd Dump (May, 2011) I5-SPCK-0911-8

TeLLNet Competence Assessment  Indicator model in Entity Relationship Diagram  Performance Indicator I   fF w f  Norm ( f )Lehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-9

TeLLNet System Architecture of Prototype CAfeLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-10

TeLLNet Self-monitoring of Teacher Network in CAfe  Target users – European teachers (teachers‘ workshops) – Administrators & policy-makersLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-11

TeLLNet Self-Monitoring of Competence ManagementLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-12

TeLLNet Self-Monitoring of Competence Management  Community level ->  Teacher levelLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-13

TeLLNet Dynamic Network Analysis in Progress  The Development Model (Pham et al. 2011 )  Applied to collaboration (project, email) and social media(blog) networks pp ed o co abo a o (p ojec , e a ) a d soc a ed a(b og) e o sLehrstuhl Informatik 5(Informationssysteme) - To detect the development pattern of project partner community - To compare different networks Prof. Dr. M. Jarke I5-SPCK-0911-14

TeLLNet Learning Analytics: EC-TEL Community among TEL Communities  ICALT, ICWL, EC-TEL, IST, AIED (Pham,, Derntl and Klamma 2011) , , , , ( ) 4 (a) Densification law (b) Clustering Coefficient 10 1 0.95 Clustering coefficient Number of edges 3 10 0.9 1.1976 ICALT: 0.34889*x g r 1.0544 ICALT ICWL: 1.1149*x 0.85 10 2 ICWL 1.2415 ECTEL: 0.40338*x ECTEL 1.3817 0.8 ITS: 0.15818*x ITS 1.1197 AIED: 1.0128*x AIED 1 10 0.75 10 1 10 2 10 3 10 4 1 2 3 4 5 6 7 8 9 Number of nodes Age (c) Maximum Betweenness (d) Largest connected component 0.08 0 08 0.7 07 ICALT ICALT Largest connected component ICWL 0.6 ICWL mum betweenness 0.06 ECTEL ECTEL 0.5 ITS ITS AIED 0.4 AIED 0.04 0.3 Maxim c 0.2 02 0.02 0.1 0 0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Age Age (e) Diameter (f) Average Path Length 20 8 ICALT ICALT ICWL ICWL Average path length 15 6 ECTEL ECTEL Diameter ITS ITS 10 AIED 4 AIED 5 2 0 0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9Lehrstuhl Informatik 5 Age Age(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-15

TeLLNet Node Level Analysis: Structural Holes, Closure and Social Capital  Structural holes (Burt, 1992) - Nodes are positioned at the interface between groups (gatekeepers, e.g. node B) - Informational nodes: access to information from different parts of networks - Novel ideas by combining information from different groups - Control the communication between groups  Closure: Cl - Nodes with high clustering coefficient (e.g. node A): embedded in tightly-knit groups - More trust and security within coherent communities  Social capital (Coleman,, 1990) p ( )Lehrstuhl Informatik 5(Informationssysteme) - Individuals and groups deriving benefits from social relationships Prof. Dr. M. Jarke I5-SPCK-0911-16 - Network structural property: can be either structural hole or closure

TeLLNet Conclusions  SNA & visualization as tools for competence development in learning networks – Competence assessment is still limited in performance indication  eTwinning case study – Complex data management issues – Visual complexity of networks vs. teachers’ competence – Experimenting with web based tools web-based  Learning analytics is the solution for large scale network Data Visual Context Network Learning analysis analytics analytics analysis analyticsLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-17

TeLLNet Learning Analytics for Conference Participants  At academic conferences/workshops – Whi h t lk t attend? Which talk to tt d? – To whom to talk to?  CAMRS – Mobile Context-aware Recom- mendation Services for Conference Participants ? ? ? ? ? Auditorium: keynote Room 342: workshopLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-SPCK-0911-18 Room 204: paper session Hall: poster session Room 048: round table

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Learning Analytics at Large: the Lifelong Learning Network of 160, 000 European Teachers

by Ralf Klamma on Sep 26, 2011

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Learning Analytics at Large: the Lifelong Learning Network of 160, 000 European Teachers — Presentation Transcript