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Les possibilitats del machine learning, big data i blockchain en la docència universitària

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Una de les tendències en entorns educatius que està creixent més és l’aplicació del Big Data, el Machine Learning, i la tecnologia Blockchain.
Es tracta de diferents tecnologies que permeten per una banda, aprofitar la immensa quantitat de dades que es generen en els processos d’aprenentatge, per extreure’n conclusions i introduir millores al procés, personalitzar itineraris, etc.
I per altra banda, generar estructures d’aprenentatge descentralitzades o sistemes de certificació basats en la tecnologia blockchain, per exemple.
Si bé són tecnologies que no han nascut pròpiament en entorns educatius i que ja s’utilitzen en banca, comerços o transports, entre d’altres, l’objectiu final d’aplicar-les en entorns educatius hauria de ser sempre la millora de l’experiència d’aprenentatge, personalitzant-la i fent-la el més significativa possible per l’alumne, així com oferint el millor entorn educatiu possible per a desenvolupar-la.

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Les possibilitats del machine learning, big data i blockchain en la docència universitària

  1. 1. Trobada dels Serveis Informàtics de les Universitats de Catalunya 2017 FRANCESC BALAGUÉ http://akoranga.org Les possibilitats del Machine Learning, Big Data i Blockchain en la docència universitària
  2. 2. 1. Context actual 2. Exemples i casos globals 3. Context educatiu 4. Exemples i casos 5. Reptes de futur Agenda Expectatives
  3. 3. World Economic Forum, 2016 https://www.weforum.org/agenda/2016/01/the-fourth-industrial- revolution-what-it-means-and-how-to-respond/ i https://www.youtube.com/watch?v=-OiaE6l8ysg 4ª Revolució Industrial
  4. 4. World Economic Forum, 2016 https://www.weforum.org/agenda/2016/01/the-fourth-industrial- revolution-what-it-means-and-how-to-respond/ i https://www.youtube.com/watch?v=-OiaE6l8ysg “Una combinació de sistemes digitals, físics i biològics” 4ª Revolució Industrial
  5. 5. The Economist, 2017 https://www.economist.com/news/briefing/21721634-how-it-shaping-up-data- giving-rise-new-economy Data are to this century what oil was to the last one: a driver of growth and change
  6. 6. 90% 2ANYS EL 90% DE LA INFORMACIÓ MUNDIAL S’HA GENERAT EN ELS DARRERS 2 ANYS Science Daily 2013 - https://www.sciencedaily.com/releases/2013/05/130522085217.htm
  7. 7. Moore’s Law http://www.mooreslaw.org i http://www.wired.co.uk/article/wired-explains-moores-law 2000 2015 37,5 M de transistors 1400 M de transistors Llei de Moore
  8. 8. Big Data Font: https://ca.wikipedia.org/wiki/Dades_massives Dades massives (o Big Data) és el nom que reben els conjunts de dades, els procediments i les aplicacions informàtiques, que, pel seu volum, la seva naturalesa diversa i la velocitat a què han de ser processades, ultrapassen la capacitat dels sistemes informàtics habituals.
  9. 9. Mineria de dades Font: https://ca.wikipedia.org/wiki/Mineria_de_dades La mineria de dades és un conjunt de tècniques encaminades a l’extracció de coneixement processable, per tal d’identificar informació vàlida, nova, potencialment útil i comprensible, que es troba oculta en les dades.
  10. 10. Machine Learning Font: https://ca.wikipedia.org/wiki/Aprenentatge_autom%C3%A0tic Per Machine learning ens referim a la branca de la informàtica relacionada amb la intel·ligència artificial, que estudia els sistemes que aprenen autònomament. Una de les diferències entre la manera d’aprendre dels humans i de les màquines, és que aquestes poden utilitzar conjunts massius de dades, extreure’n patrons, i aprendre’n.
  11. 11. http://www.expansion.com/actualidadeconomica/analisis/2016/03/11/56e1936fe2704e6c588b4575.html Zara
  12. 12. https://www.forbes.com/sites/bernardmarr/2016/12/13/big-data-in-healthcare-paris-hospitals-predict-admission-rates-using-machine-learning Medicina
  13. 13. https://digit.hbs.org/submission/rolls-royce-value-creation-through-big-data/ Rolls Royce
  14. 14. https://www.theguardian.com/football/2014/oct/17/arsenal-place-trust-arsene-wenger-army-statdna-data- analysts i https://www.forbes.com/sites/bernardmarr/2015/03/25/big-data-the-winning-formula-in-sports/ Big Data The Winning Formula in Sports Esports
  15. 15. https://www.wired.com/insights/2014/03/big-data-lessons-netflix/ i https://www.uoc.edu/portal/es/news/actualitat/2015/130-netflix-big- data.html Netflix
  16. 16. https://techcrunch.com/2017/03/06/googles-smarter-a-i-powered-translation-system-expands-to-more-languages/ Google translate
  17. 17. https://www.theverge.com/2017/3/27/15077864/elon-musk-neuralink-brain-computer-interface-ai-cyborgs Neuralink Developing ultra high bandwidth brain-machine interfaces to connect humans and computers.
  18. 18. Expedition 50 Soyuz MS-02 Landing https://www.flickr.com/photos/nasahqphoto/33110046934
  19. 19. Font: http://www.swiftelearningservices.com/top-6-elearning-trends-in-2017/
  20. 20. 1. Millorar els resultats dels estudiants 2. Crear programes massius personalitzats 3. Millorar l’experiència d’usuari en temps real 4. Reduir l’abandonament 5. Equips d’alt rendiment d’aprenentatge 6. Avaluació Big Data en educació https://datafloq.com/read/big-data-will-revolutionize-learning/206
  21. 21. El fet de tenir dades en temps real de tot el procés permet fer intervencions i oferir suport just en el moment que els necessita, coneixent el seu perfil i els recursos que més s’hi poden ajustar. Millorar els resultats dels estudiants Big Data en educació https://datafloq.com/read/big-data-will-revolutionize-learning/206
  22. 22. El fet de tenir dades en temps real de tot el procés permet fer intervencions i oferir suport just en el moment que els necessita, coneixent el seu perfil i els recursos que més s’hi poden ajustar. Millorar els resultats dels estudiants Big Data en educació https://datafloq.com/read/big-data-will-revolutionize-learning/206
  23. 23. Crear programes massius personalitzats - Els MOOCs estan pensats per estandaritzar una mateixa proposta - Els MOOCs presenten moltes evidències d’abandonaments massiu (7-12%)* - Els MOOCs estan majoritàriament dissenyats com a processos individuals no interactius - Els MOOCs no faciliten un seguiment directe i/o interacció per part del professorat MOOCs vs. POOCs http://edtechreview.in/trends-insights/insights/2284-poocs-personalized-open-online-courses-not-moocs * http://www.mindingthecampus.org/2013/11/why_do_students_drop_out_of_mo/ Big Data en educació
  24. 24. Millorar l’experiència de l’usuari en temps real https://www.youtube.com/watch?v=0JUN9aDxVmI Advanced Algorithms (COMPSCI 224), Lecture 1 Big Data en educació 1.032.387 visualitzacions
  25. 25. - Abans de començar el curs: anàlisis de “requisits previs” i proposta d’itineraris personalitzats - Durant el curs: identificar en quins moments comença a presentar indicadors de perill d'abandonament i oferir recursos i suport complementari. - Un cop graduat, traçabilitat de la ocupabilitat per retroalimentar la definició dels perfils d’estudiants. Reduir l’abandonament Big Data en educació
  26. 26. A partir de les anàlisis dels perfils d’aprenentatge individuals, i dels resultats i processos de molts altres equips, els algoritmes poden determinar perfils de cada membre i analitzar punts forts i dèbils alhora de crear grups de treball. Equips més equilibrats i complementaris, amb unes corbes d’aprenentatge més profundes i millorar els resultats grupals. Equips d’alt rendiment d’aprenentatge Big Data en educació
  27. 27. - Eines per mesurar el rendiment dels estudiants més detalladament en funció de les tasques, obtenint més informació per avaluar les habilitats més complexes de forma més ràpida i contínua. - El Big Data també pot facilitar incloure elements de les discussions i interaccions online dels estudiants i combinar-los amb altres indicadors. - Permet un monitoratge i seguiment longitudinal molt més detallat de tot el procés, facilitant processos d’avaluació més globals. Avaluació Big Data en educació Dede, D. Andrew, H. I Mitros, P. (2016) Big Data Analysis in Higher Education: Promises and Pitfalls http://er.educause.edu/articles/2016/8/big-data-analysis-in-higher-education-promises-and-pitfalls
  28. 28. - Eines per mesurar el rendiment dels estudiants més detalladament en funció de les tasques, obtenint més informació per avaluar les habilitats més complexes de forma més ràpida i contínua. - El Big Data també pot facilitar incloure elements de les discussions i interaccions online dels estudiants i combinar-los amb altres indicadors. - Permet un monitoratge i seguiment longitudinal molt més detallat de tot el procés, facilitant processos d’avaluació més globals. Destaquen la tendència desproporcionada a pensar que les anàlisis predictives seran fiables i correctes, i que realment podran ser usades per professors i alumnes per millorar l'aprenentatge. Demanen estudis més profunds que contrastin aquest punt. Avaluació Big Data en educació ! Dede, D. Andrew, H. I Mitros, P. (2016) Big Data Analysis in Higher Education: Promises and Pitfalls http://er.educause.edu/articles/2016/8/big-data-analysis-in-higher-education-promises-and-pitfalls
  29. 29. “Beyond Badges: Why Personalized Learning Advocates Need to Care about Blockchain” i “10 Things To Know about the Future of Blockchain in Education” Blockchain en educació - Sistemes de certificació - Seguretat - Manetniment de registres i dades - Accés a registres en qualsevol moment - Validar credencials dels estudiants
  30. 30. Academic Analytics Academic analytics combines large data sets with statistical techniques and predictive modelling to improve decision making. Academic analytics provide data that administrators can use to support the strategic decision-making process as well as a method for benchmarking in comparison with other institutions. Daniel, B. Daniel, B. (2014) Big Data and analytics in higher education: Opportunities and challenges https://i.unisa.edu.au/siteassets/staff/tiu/documents/big-data-and-analytics-in-higher-education--opportunities-and-challenges.pdf
  31. 31. Academic Analytics https://datafloq.com/read/big-data-will-revolutionize-learning/206 - Ús dels MOOCS - Satisfacció usuari - Reducció de l’abandonament
  32. 32. Academic Analytics https://www.cogbooks.com/2016/10/05/big-data-will-boost-learning-teaching-higher-education/ - Ús dels MOOCS - Satisfacció usuari - Reducció de l’abandonament ! - Començar pel final: a quin objectiu volem arribar? - Avançar pas a pas: començar identificant un element clau - Canvi de cultura: entendre i acceptar la cultura de tecnologies digitals.
  33. 33. EXEMPLES EDUCACIÓ
  34. 34. Domoscio connecta les ciències cognitives, el Big Data i la IA per crear solucions d’aprenentatge adaptatiu. Optimitzant l’assimilació i consolidació per millorar els impactes de l’aprenentatge. https://domoscio.com/
  35. 35. Knewton és una plataforma d’aprenentatge adaptatiu per a l’educació superior. https://www.knewton.com/
  36. 36. Zoomi ofereix una eina d’aplicació de la Intel·ligència Artificial als processos d’aprenentatge. http://zoomiinc.com/
  37. 37. CogBooks és una plataforma d’aprenentatge adaptatiu que personalitza el contingut amb les necessitats individuals dels estudiants. https://www.cogbooks.com/
  38. 38. La plataforma d’aprenentatge adaptatiu Area9, combina neurociència cognitiva i informàtica amb els continguts, per reduir el temps de formació a la meitat, garantir la competència i generar impactes duradors. https://area9learning.com
  39. 39. Wildfire és un programari d’intel·ligència artificial que transforma textos en continguts d’aprenentatge actiu. http://www.wildfirelearning.co.uk/
  40. 40. Blockcerts Sistema de certificació basada en la tecnologia Blockchain. http://www.blockcerts.org/ http://certificates.media.mit.edu/
  41. 41. edChain és una biblioteca descentralitzada i de codi obert que permet compartir continguts educatius a través de diferents aplicacions i organitzacions (basada en els protocols de web distribuïda IPFS). https://www.edchain.io/
  42. 42. La Open Source University es defineix com la primera universitat distribuïda que proporciona adquisició de coneixements i habilitats, verificació i validació, a través de tecnologies de blockchain. https://os.university/
  43. 43. La versió 3.4 de Moodle ja integra en l’estructura principal, les funcionalitats d’analítica (prediccions d’èxit o abandonament d’estudiants, etc.) https://moodle.com/2017/11/23/support-learners-using-inspire-analytics-moodle-3-4/
  44. 44. REPTES IMMINENTS
  45. 45. REPTES IMMINENTS - Grans volums de dades - Estandarització - Privacitat i propietat - Recol·lecció d’activitat presencial
  46. 46. Mark van Rijmaenam https://datafloq.com/read/big-data-will-revolutionize-learning/206 Uns estudiants més intel·ligents que optimitzin i facin més eficient el seu aprenentatge tindrà importants beneficis per a la societat” “
  47. 47. Francesc Balagué info@akoranga.org @fbalague

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