MOOCs: the power of collaborative learning and communities of knowledge
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This document discusses Marta Cáceres-Piñuel's PhD research on MOOCs and their role in creating open communities of knowledge. Her hypothesis is that MOOCs enable more than just massive online courses - they create new online spaces for knowledge sharing. She analyzes data from surveys and learning analytics from two MOOC platforms to study how students interact and form knowledge networks. A case study examines a MOOC on transversal competencies for entrepreneurship with over 260,000 total students and 5,602 students in the course. The document provides background on the theoretical frameworks of networking analysis and connectivism that inform the research.
MOOCs: the power of collaborative learning and communities of knowledge
- 1. MOOCS: THE POWER OF COLLABORATIVE LEARNING AND THE COMMUNITIES OF KNOWLEDGE Marta Cáceres-‐Piñuel UNED PhD Researcher GO-‐GN Member OCWC Lujbljana 23th April 2014
- 2. PhD AIM & HYPOTHESIS • AIM: • To analize MOOCs and Beyond (MOOCs as New online spaces for Open Knowledge; New Methodologies, innovaYve social dynamics, and renewed insYtuYonals designs associated for creaYng Open CommuniYes of Knowledge) • HYPOTHESIS 1. MOOCs enable much more than massive spaces for scalable online courses.-‐ MOOCs create new online spaces for CommuniYes of Knowledge. 2. MOOCs can permeate remove borders in (formal/non formal)EducaYon and enlarge open knowledge sharing in two direcYons. – They offer soluYons for EducaYonal InsYtuYons (Secondary Schools/UniversiYes/Colleges…) to offer Open and Massive educaYon (formal/non formal). – They offer Open Knowledge Networks to different stakeholders (companies, insYtuYons/ individuals…). Networks to access knowledge, to organise knowledge, to share/exchange knowledge with others. 3. MOOCs are a powerful tool in combinaYon with other social tools (P2P acYviYes, fora, Q&A…) for Global Open Knowledge Networks. Knowledge networks (academic, professional, communiYes of pracYce) can be connected in a region or even globally.
- 3. THEORETICAL FRAMEWORK • Networking Analisys Theory (Sociological ProspecJve) • Networkig analysis is based on an assumpYon of the importance of relaYonships among interacYng units. The social network perspecYve encompasses theories, models, and applicaYons that are expressed in terms of relaYonal concepts or processes. Actors and their acYons are viewed as interdependent rather than independent, autonomous units(…)(1).(Wasserman, S. and K. Faust, 1994, Social Network Analysis.Cambridge: Cambridge University Press. • ConnecJvism ( EducaJonal ProspecJve) • ConnecYvism is a hypothesis of learning which emphasizes the role of social and cultural context(1). The relaYonship between work experience, learning, and knowledge, as expressed in the concept of ‘connecYvity, is central to connecYvism, moYvaYng the theory's name.The phrase "a learning theory for the digital age”(2) indicates the emphasis that connecYvism gives to technology's effect on how people live, communicate and learn.( George Siemens. ConnecYvism: A Learning Theory for the Digital Age) • (1)ConecYvism and Previous Theories: ConnecYvism is oien associated with and proposes a perspecYve similar to Vygotsky's 'zone of proximal development' (ZPD), an idea later transposed into Engeström's (2001) AcYvity theory. It is somewhat similar to Bandura's Social Learning Theory that proposes that people learn through contact.
- 4. DATA ANALYSIS MOOC survey to Students and Learning AnalyYcs TOOLS: Survey to Students and Learning AnalyYcs from 2 different MOOC Plamorms UNED COMA: The MOOC strategy of the NaYonal Distance University. Spain.Removing borders of Distance EducaYon in Spanish UNX: The open LaYnamerican Plaoorm for Entrepreneurship Training and CreaYng Networworking for new Entrepreneurs RATIONAL OF THE MOOC DATA ANALYSIS • MOOCs collect valuable data on student learning behavior; essenYally complete records of all student interacYons in a self-‐contained learning environment, and analize their opinions and moYvaYon with the benefit of large sample sizes. • Our main objecYve here is to show how the huge amount of data available in MOOCs offers a unique research opportunity, permirng not only to study detailed student profile , interest and behavior, but also analize how these students interact with other peers in a new online environment creaYng a new space for Knowledge ang Networking.
- 5. CASE STUDY: MOOC: Transversal Competencies for Entrepreneurship. (Course offered in both plaoorms) • Total Students:261.480 • In the Course: 5602 • Sample:210 surveys • DescripYon: Clasic xMOOC Academic Plamorm (Language,Spainish) • Features: (OpenMOOC) – (Video, test, Self-‐assessment, online and presenYal cerYficaYon) – Some social tools (Fora, Curator, Facilitator P2P acYviYes, P2P Assesment, open badges) • Total Studens:38.881 • In the Course 2262 • Sample: 82 surveys • DescripYon: ThemaYc cMOOC Plamorm focused on Entrepreneurship (MOOC layar +Community layar) (Languages Spanish &Portuguese) • Features: (WeMOOC) • MOOC Plamorm (video,test, self-‐assessment, online cerYficaYon) • Social Layar: (Karma, system of voYng, fora, Q&A, Tool for friendship, Content upload by users , Supply & demand ads, events, Facebook, twiuer,…)
- 6. QUESTIONS FOR ANALYSIS 1. Who is behind the MOOC? Sociodemographic Profile of students 2. Why do they register in a MOOC? MoJvaJon 3. What is the level of Drop out? CompleJon Rate and CerJficaJon availability 4. What is the level of Social parJcipaJon in the online environment?.InteracJon with peers. 5. How do students learn in a MOOC environment? Tools Used and learning style 6. How to measure MOOC success? Beyond the scalability of the online learning experience; Level of saJsfacJon and outputs (For students and InsJtuJons) 7. Where are the MOOC borders? Language and internaJonalizaJon 8. Lessons Learned
- 7. SOCIODEMOGRAPHIC PROFILE ¿What is the profile of the students in the Case Study? (I) • Note: NO significant sociodemographic differences between plamorms, or with other MOOC. 0% 35,37% 46,34% 13,41% 4,88% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% <15 years 15-‐30 31-‐45 46-‐55 >55 years Age of MOOC students (%) Age of MOOC students (%) 36,59% 63,41% Sex of MOOC Students % Women Men AGE GENRE
- 8. ¿What is the profile of the students in the Case Study? (II) 11,11% 88,89% Place of residence % Rural Urban 0% 7,32% 14,63% 37,80% 40,24% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% EducaJon % EducaYon % SOCIODEMOGRAPHIC PROFILE
- 9. SOCIODEMOGRAPHIC PROFILE ¿What is the profile of the students in the Case Study? (III) 10,98 32,93 37,8 12,2 6,1 0 5 10 15 20 25 30 35 40 Job SituaJon (%) Job SituaYon (%) 10,98 70,33 12,2 6,1 Student Worker/Self-‐employed Unemployed Other situaYon
- 10. MOTIVATION (To Register in the MOOC) 92,68 91,46 53,66 48,78 0 10 20 30 40 50 60 70 80 90 100 InteresYng Subject To Entrepreneur To find a job To Improve my current job MoJvaJon % 78 4 6 1 10 0 50 100 IntesYng Subject Profesional(to find a job) Curiosity CerYficaYon Other moYvaYon (to entrepreneur) MoJvaJon %
- 11. RATE OF COMPLETION ¿ PotenYal link with cerYficaYon? • 37´8% complete the course • 1,23% cerYficaYon apply (cerYficaYon fee for ECTs credits) • 23´5% Complete the course • No cerYficaYon • DROP OUT PATH 0 500 1000 1500 2000 2500 CerYficaYon
- 12. PARTICIPATION (InteracYon with peers) • Level of ParJcipaJon (Fora) 78,51% 0,65% 20,81% Level of ParJcipaJon (Karma) Low ParYcipaYon High ParYcipaYon No parYcipaYon (observers) 4,60 8,25 87,85 ParJcipaJon % Low ParYcipaYon High ParYcipaYon No parYcipaYon (Observer)
- 13. USE OF TOOLS FOR LEARNING 0 10 20 30 40 50 60 70 80 Videos Contents Test Selfassesments P2P Asessment Facilitator Support Curator Support Peer Support Use of Social Tools 0 20 40 60 80 100 120 Videos Test Selfassesment Contents P2P AcYviYes Facebook (followers) Twiuer (followers) Supply/demands ads Q&A regarding MOOCs P2P Direct Contact Fora comments VoYng tool Wrirng in personal walls Sharing contents Use of Social Tools
- 14. LEVEL OF SUCCESS SaYsfacYon and Outputs • Percentage of SaJsfacJon 95% 0 25,61 29,27 45,12 0 10 20 30 40 50 No ParYally capable Very capable Totally Capable Do you feel capable to entrepreneur aeer the MOOC (%)? Percentage of SaJsfacJon 98% OUTPUT OUTPUT 0 10 20 30 40 50 60 70 80 I konw beuer UNED University I would like to parYcipate in other UNED MOOC I woull like to parYcipate in Formal Courses in UNED I will not parYcipate in formal course in UNED Aeer the MOOC Experience…
- 15. 13.640 4.080 1.632 1.089 711 518 372 328 262 214 Visits per Country Spain Brazil Mexico Colombia ArgenYna Perú Student per Country Spain Colombia Mexico ArgenYna Peru INTERNATIONALIZATION Removing Borders with Languages
- 16. PRELIMINARY FINDINGS 1. Typical sociodemographic Profile: man, 25-‐35 years old,high educaYon, worker and urban. 2. MoJvaJon: Interest in academic subject and job purposes (LLL) 3. The level of drop out in a MOOC could be linked with cerYficaYon availability; More CompleYon Rate if there is a CerJficaJon (specifically in academic MOOC environments) 4. The tradiYonal MOOC environment (xMOOCs) promotes less interacJon between peers than communiYes of knowledge (cMOOCs) . 5. Learning style in MOOCs is Autonomous but ICT and Social Tools can enable more collaboraJve learning style. 6. MOOC success must be measured beyond the scalability of the online learning experience; Level of saJsfacJon and outputs for students and InsJtuJons must be take in account. 7. Language is the main enabler for internaJonalizaJon of MOOC 8. Lessons Learned : More Learning AnalyYcs are needed and a standardized framework for assessment.
- 17. “IntuiYons without data are just opinions” THANK YOU!!!