Analitica aprendizaje
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Analitica aprendizaje

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Presentación sobre Analítica del Aprendizaje en iSummit 2012 en Loja, Ecuador. Octubre 24, 2012

Presentación sobre Analítica del Aprendizaje en iSummit 2012 en Loja, Ecuador. Octubre 24, 2012

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  • SAS/Davenport
  • Key (to UMBC) is that CMS usage ALONE is established as an indicator of student success. To date, many academic analytics projects have focused on predictive data models that may have more to do with what students did or where they came from BEFORE stepping foot on campus.
  • Highly interactive online courses are predictive of student success, but variability among faculty course design means the LMS can never rise above “the course level” in terms of a “one size fits all” intervention solution.
  • A unified framework for multi-level analysis of distributed learninghttp://dl.acm.org/citation.cfm?id=2090124&CFID=82269174&CFTOKEN=35344405
  • Attention please!: learning analytics for visualization and recommendationhttp://dl.acm.org/citation.cfm?id=2090118&CFID=82269174&CFTOKEN=35344405
  • Learning networks, crowds and communitieshttp://dl.acm.org/citation.cfm?id=2090119&CFID=82269174&CFTOKEN=35344405
  • Discourse-centric learning analytics http://dl.acm.org/citation.cfm?id=2090120&CFID=82269174&CFTOKEN=35344405
  • Social Learning Analyticshttp://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf
  • iSpot analysed: participatory learning and reputation http://dl.acm.org/citation.cfm?id=2090121&CFID=82269174&CFTOKEN=35344405
  • Macfadyen, L.P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599.Campbell, J. P., Collins, W.B., Finnegan, C., & Gage, K. (2006). "Academic analytics: Using the CMS as an early warning system." WebCT Impact 2006. Chicago, IL

Analitica aprendizaje Analitica aprendizaje Presentation Transcript

  • Analítica del Aprendizaje TICs y Big Data para Investigación Educativa
  • http://www.slideshare.net/xaoch
  • Grupo de Investigación enTecnologías para la Enseñanza y el Aprendizaje• Suitable material at the appropriate time.• Automatically measure student progress.• Virtual instructor – student.
  • Contexto
  • Los datos son valiosos…
  • Los datos revelan:• Nuestros sentimientos• Nuestras actitudes• Nuestas conexiones sociales• Nuestras intenciones• Lo que hicimos• Lo que hacemos• Lo que haremos
  • Abundancia de Datos
  • Exa Up to 10,000 Times Peta larger Data Scale Tera Data at RestData Scale Giga Traditional Data Mega Warehouse and Business Intelligence Up to 10,000 Data in Motion times faster Kilo yr mo wk day hr min sec … ms s Occasional Frequent Real-time Decision Frequency
  • http://www.businessinsider.com/the-cia-just-put-a-ton-of-cash-into-a-software-firm-that-monitors-your-online-activity-2011-7
  • Los datos son valiosos… Si les podemos dar sentido
  • Sensemaking“Sensemaking is a motivated, continuous effortto understand connections . . . in order toanticipate their trajectories and act effectively” (Klein et al. 2006)
  • Analítica
  • Inteligencia de Negocio
  • BI• Prediction
  • Google Analytics
  • Google Analytics
  • Otros campos
  • E-Ciencia
  • Que pasa en Educación ¿Porqué los alumnos se retiran? ¿En que invertir los fondos? ¿Como van nuestros profesores? ¿Cómo va tu curso? ¿Están tus estudiantes aprendiendo? ¿Funcionan tus estrategias? ¿Estoy estudiando suficiente? ¿Que hago para mejorar? ¿Donde estoy fallando?
  • Definiciones
  • Analítica del Aprendizaje“La Analítica del Aprendizaje es lamedida, colección, análisis y reporte de datos delos aprendices y su contexto, con el propósito deentender y optimizar el aprendizaje y losambientes donde ocurre.” SoLAR http://www.solaresearch.org
  • Analítica del Aprendizaje“La Analítica del Aprendizaje es lamedida, colección, análisis y reporte de datos delos aprendices y su contexto, con el propósito deentender y optimizar el aprendizaje y losambientes donde ocurre.” SoLAR http://www.solaresearch.orgL. Johnson, R. Smith, H. Willis, A. Levine, and K. Haywood, The 2011 Horizon Report (Austin, TX: The NewMedia Consortium, 2011), http://www.nmc.org/pdf/2011-Horizon-Report.pdf 6
  • Raices de la Analítica del Aprendizaje Métodos Estadísticos Tutores EDM Inteligentes Big Data Personalización Analítica Business AprendizajeIntelligence del Adaptativo Aprendizaje
  • 39
  • Analítica de la Academia“La Analítica de la Academia ayuda asatisfacer el deseo público de hacer cuentassobre el éxito de los estudiantes, dadas laspreocupaciones a nivel mundial del costo dela educación y las dificultades económicas ypresupuestarias existentes.”http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazi neVolum/SignalsApplyingAcademicAnalyti/199385
  • Analítica del Aprendizaje Minería Ciencias de Datos EducacionalesEducacionales
  • Análisis del Aprendizaje• Ciencias Educacionales y Cognitivas – Motores• Analítica del Aprendizaje – Panel de Control (Dashboard)• Minería de Datos Educacional – Vehículos Automáticos
  • Creación de Datos Cada una de estas interacciones puede generar datosU.S. Department of Education, National Education Technology Strategy, 2010
  • Big Data en Educación EstudiantesProfesores Investigadores Institución 52
  • Casos y Ejemplos
  • http://www.skyrill.com/seatinghabits/
  • http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0023176
  • Approximate spatiotemporal trajectories of some classes. http://www.plosone.org/article/info%3Adoi%2 F10.1371%2Fjournal.pone.0023176
  • “Colleges Mine Data toPredict Dropouts”“At the University System ofGeorgia, researchers monitoredhow frequently students vieweddiscussion posts and content pageson course Web sites for threedifferent courses to findconnections between onlineengagement and academicsuccess. In the graph below,students who were "successful"received an A, B, or C in the class,and students who were"unsuccessful" received a D, F,or an incomplete.” - 5/30/08 Chronicle of Higher Ed.
  • http://www.itap.purdue.edu/studio/signals/
  • http://www.itap.purdue.edu/studio/signals/
  • http://www.itap.purdue.edu/studio/signals/
  • Bb Check My Activity (CMA)
  • Govaerts, S Verbert, Katrien, Duval, Erik; ten;Pardo,Abelardo, The S tudent Activity Meter forAwareness and S elf-reflection. Proceedings of CHIConference on Human Factors in ComputingSystems, 2012,Austin,Texas,ACM. 26
  • http://jlsantoso.blogspot.com/
  • http://research.uow.edu.au/learningnetworks/seeing/snapp/index.html
  • Cohere• Annotations or discussion as a network of rhetorical moves• Users must reflect on, and make explicit, the nature of their contribution Simon Buckingham Shum, Anna De Liddo
  • Open Mentor Denise Whitelock Analyse, visualise and compare quality of feedback
  • Distributed, multi-level analytics Suthers & Rosen (2011)
  • Attention metadata Duval (2011)
  • Learning networks, crowds, communities Haythornthwaite (2011)
  • Discourse analysis (automated and manual) De Liddo & Buckingham Shum (2011)
  • Social learning analytics Buckingham Shum & Ferguson (2011)
  • Participatory learning and reputation Clow & Makriyannis (2011)
  • Early warning Macfayden & Dawson (2010) Campbell et al (2006)
  • Learning Analytics in the workplace: Detectingand Analyzing Informal Workplace Learning Schreurs & De Laat, 2012
  • Herramientas
  • Algoritmos
  • http://www.solaresearch.org/
  • http://www.lakconference.org
  • Reconocimiento• Esta presentación está basada trabajo CC de: – George Siemens – John Fritz – Erik Duval – Simon Buckingham Shum – Matin Jouzdani – Jiawan Zhang – Louis Soares
  • Gracias / Thank you / Merci Xavier Ochoa xavier@cti.espol.edu.ec http://ariadne.cti.espol.edu.ec/xavier Twitter: @xaoch