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Analíticas del aprendizaje:
una perspectiva crítica
Jordi Adell

Centre d’Educació i Noves Tecnologies
Dept. d’Educació

U...
Índice
• Definición. Marcos conceptuales. Presupuestos.
• Promesas.
• Tipos-Aplicaciones.
• Críticas: los peligros de “dati...
http://www.excelacom.com/resources/blog/2016-update-what-happens-in-one-internet-minute
https://www.flickr.com/photos/84593672@N05/9427663067
¿El fin de la teoría?
https://www.wired.com/2008/06/pb-theory/
https://www.theguardian.com/news/datablog/2012/mar/09/big-da...
Big Data como mito
danah boyd & Kate Crawford
CRITICAL QUESTIONS FOR BIG DATA
Provocations for a cultural,
technological, ...
XML Template (2014) [8.7.2014–1:50pm] [1–12]
//blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/BDSJ/Vol00000/140001/APPF...
Big data en la era post-Snowden
Las analíticas del aprendizaje son
la aplicación de las ideas,
tecnologías, procesos, etc. sobre
Big Data a la educación
Definición
La analítica del aprendizaje es la medida,
recolección, análisis y presentación de datos
sobre los estudiantes y...
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education.
EDUCAUSE review, 46(5), 30.
«La ...
Las promesas de
la analítica del
aprendizaje
Learning Analytics in
Higher Education
A review of UK and international pract...
1. As a tool for quality assurance and quality
improvement - with many teaching sta using data to
improve their own practi...
3. As a tool for assessing and acting upon
differential outcomes among the student population
– with analytics being used ...
Motivaciones del 

mini-seminario
Horizon Report > Edición Educación Superior 2016
Horizon Report > Edición Educación Superior 2016
38 NMC Horizon Report: Edición Educación Superior 2016
Analíticas de aprendizaje y aprendizaje adaptativo
Plazo estimado p...
Pero hace 6 años que
está a punto de ocurrir :-)
https://twitter.com/audreywatters/status/696057730126065666
Innovating
Pedagogy
2016
Exploring new forms
of teaching, learning
and assessment, to guide
educators and policy
makers
Mi...
Innovating
Pedagogy
2016
Exploring new forms
of teaching, learning
and assessment, to guide
educators and policy
makers
Mi...
Definición
La analítica del aprendizaje es la
medida, recolección, análisis y
presentación de datos sobre los
estudiantes y...
Más definiciones
Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics. Educause Quarterly, 1-20.
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in
Learning and Education. EDUCAUSE review, 46(5), 30.
Clow, D. (2012). The learning
analytics cycle: Closing the
loop effectively. Proceedings
of the 2nd international
conferen...
Tipos de analíticas del aprendizaje
Adarshsudhindra The different types of Learning Analytics https://commons.wikimedia.or...
Analíticas macro/meso/micro
Macro:
Meso:
Micro:
región/estado/nación/internacional
institución (Universidad)
depto./titula...
Learning Analytics Framework
(Greller & Drachsler, 2012)
Greller, W., & Drachsler, H. (2012).
Translating learning into nu...
Modelo de referencia
(Chatti et al., 2012)
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference mo...
¿Qué?
Datos y entornos
• Learning Management System (aula
virtual) & SIS (Studen Information
System).
• Activitity Data, A...
¿Quién?
“Stakeholders"
Estudiantes, profesores, tutores/
mentores (humanos o “inteligentes”),
instituciones y administraci...
¿Para qué?
Fines y objetivos de cada stakeholder
• Monitorización y análisis.
• Predicción e intervención.
• Tutorización ...
¿Cómo?
Métodos
• Estadística.
• Visualización de información (dashboards).
• Minería de datos:
• Clasificacion.
• Clusterin...
Arnold, “Signals: Applying Academic Analytics,” EDUCAUSE Quarterly 33, no. 1; 

Kimberly E. Arnold and Matthew D. Pistilli...
https://www.d2l.com/topics/learning-analytics/
D2L Brightspace Degree Compass (sic)
http://www.blackboard.com/Images/Bb_Predict_tcm21-38757.pdf
http://klassdata.com/es/smartklass-el-
plugin-de-learning-analytics/learning-
analytics-moodle-smartklass/
http://www.moodlenews.com/2016/the-learning-analytics-
roadmap-the-dalton-plan-moodlemoot-australia-2016-lar-
series-6/
Knewton: aprendizaje adaptativo

https://youtu.be/LldxxVRj4FU
Plataformas de
aprendizaje adaptativo
etc.
Futuro
Investigación
Hershkovitz, A., Knight, S.,
Dawson, S., Jovanović, J., &
Gašević, D. (2016). About"
Learning" and" Analytic...
Reino Unido: grandes planes
http://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics...
Learning Analytics in
Higher Education
A review of UK and international practice
Full report
April 2016
Authors
Niall Scla...
University of Maryland,
United States
Students who obtain low grades use the
VLE 40% less than those with C grades
or high...
Reflexiones
“Our Learning
Analytics are 

Our Pedagogy”
Simon Buckingham Shum
http://www.slideshare.net/sbs/our-learning-
analytics-ar...
Is education 

poised to become
a data-driven
enterprise

and science?
Simon Buckingham Shum
http://www.slideshare.net/sbs...
La enorme diferencia
entre conocer y medir
By W.v. Ravernstein @wiswijzer2, RT por Simon Buckingham-Shum
Medir
Conocer
Data entry: towards the critical study of digital data and education
Neil Selwyn∗
Faculty of Education, Monash University,...
Preguntas
• Los datos, ¿son neutros, objetivos, carentes de
presupuestos epistemológicos, ideológicos,
sociales, etc.)
• ¿...
Preguntas
• ¿Cómo transforma la enseñanza y el aprendizaje
universitarios el análisis sistemático y constante de las
“huel...
¿Son las analíticas
el Gran Hermano?
¿Caminamos hacia una
“universidad panóptica”?
https://worldwideweber2014.wordpress.com/2014/04/14/the-panopticon/
Muchas gracias
por su atención.Y ahora…
http://www.thebluediamondgallery.com/tablet/d/debate.html
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
Analíticas del aprendizaje: una perspectiva crítica
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Analíticas del aprendizaje: una perspectiva crítica

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Jordi Adell. Presentación para el seminario "Analíticas del aprendizaje: una perspectiva crítica". CENT, 13 de diciembre de 2016.

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Analíticas del aprendizaje: una perspectiva crítica

  1. 1. Analíticas del aprendizaje: una perspectiva crítica Jordi Adell
 Centre d’Educació i Noves Tecnologies Dept. d’Educació
 Universitat Jaume I
  2. 2. Índice • Definición. Marcos conceptuales. Presupuestos. • Promesas. • Tipos-Aplicaciones. • Críticas: los peligros de “datificar” la enseñanza y el aprendizaje. • Debate
  3. 3. http://www.excelacom.com/resources/blog/2016-update-what-happens-in-one-internet-minute
  4. 4. https://www.flickr.com/photos/84593672@N05/9427663067
  5. 5. ¿El fin de la teoría? https://www.wired.com/2008/06/pb-theory/ https://www.theguardian.com/news/datablog/2012/mar/09/big-data-theory
  6. 6. Big Data como mito danah boyd & Kate Crawford CRITICAL QUESTIONS FOR BIG DATA Provocations for a cultural, technological, and scholarly phenomenon The era of Big Data has begun. Computer scientists, physicists, economists, mathemati- cians,political scientists,bio-informaticists,sociologists,and other scholars areclamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of ana- lyzing genetic sequences, social media interactions, health records, phone logs, govern- ment records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data ana- lytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communi- cation and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, techno- logical, and scholarlyphenomenonthatrests ontheinterplayof technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric. ownloadedby[181.136.104.141]at14:0222July2014 Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.
  7. 7. XML Template (2014) [8.7.2014–1:50pm] [1–12] //blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/BDSJ/Vol00000/140001/APPFile/SG-BDSJ140001.3d (BDS) [PREPRINTER stage] Original Research Article Big Data, new epistemologies and paradigm shifts Rob Kitchin Abstract This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemol- ogies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering para- digm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, history, economy and society. It is argued that: (1) Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and (2) there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. Keywords Big Data, data analytics, epistemology, paradigms, end of theory, data-driven science, digital humanities, computational social sciences Introduction Revolutions in science have often been preceded by revolutions in measurement. Sinan Aral (cited in Cukier, 2010) Big Data creates a radical shift in how we think about research . . .. [It offers] a profound change at the levels of epistemology and ethics. Big Data reframes key questions about the constitution of knowledge, the pro- cesses of research, how we should engage with informa- tion, and the nature and the categorization of reality . . . Big Data stakes out new terrains of objects, methods of knowing, and definitions of social life. (boyd and Crawford, 2012) As with many rapidly emerging concepts, Big Data has been variously defined and operationalized, ranging from trite proclamations that Big Data consists of data- sets too large to fit in an Excel spreadsheet or be stored on a single machine (Strom, 2012) to more sophisticated ontological assessments that tease out its inherent characteristics (boyd and Crawford, 2012; Mayer-Schonberger and Cukier, 2013). Drawing on an extensive engagement with the literature, Kitchin (2013) details that Big Data is: . huge in volume, consisting of terabytes or petabytes of data; . high in velocity, being created in or near real-time; . diverse in variety, being structured and unstructured in nature; . exhaustive in scope, striving to capture entire popu- lations or systems (n ¼ all); National Institute for Regional and Spatial Analysis, National University of Ireland Maynooth, County Kildare, Ireland Corresponding author: Rob Kitchin, National Institute for Regional and Spatial Analysis, National University of Ireland Maynooth, County Kildare, Ireland. Email: Rob.Kitchin@nuim.ie Big Data & Society April–June 2014: 1–12 ! The Author(s) 2014 DOI: 10.1177/2053951714528481 bds.sagepub.com Creative Commons CC-BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (http:// www.uk.sagepub.com/aboutus/openaccess.htm). by guest on July 6, 2015Downloaded from Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1-12. «Hay pocas dudas de que el desarrollo de Big Data y la nueva analítica de datos ofrece la posibilidad de replantear la epistemología de la ciencia, las ciencias sociales y las humanidades, y tal replanteamiento ya se está llevando a cabo activamente a través de disciplinas».
  8. 8. Big data en la era post-Snowden
  9. 9. Las analíticas del aprendizaje son la aplicación de las ideas, tecnologías, procesos, etc. sobre Big Data a la educación
  10. 10. Definición La analítica del aprendizaje es la medida, recolección, análisis y presentación de datos sobre los estudiantes y sus contextos con el propósito de comprender y optimizar el aprendizaje y el entorno en que tiene lugar. Long, P., Siemens, G., Conole, G., and Gasevic, D. (2011). Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK11), Banff, AB, Canada, Feb 27-Mar 01, 2011. New York: ACM.
  11. 11. Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30. «La idea es simple pero potencialmente transformadora: las analíticas proporcionan un nuevo modelo para que los líderes universitarios mejoren la enseñanza, el aprendizaje, la eficiencia organizacional y la toma de decisiones y, como consecuencia, sirva de base para el cambio».
  12. 12. Las promesas de la analítica del aprendizaje Learning Analytics in Higher Education A review of UK and international practice Full report April 2016 Authors Niall Sclater Alice Peasgood Joel Mullan
  13. 13. 1. As a tool for quality assurance and quality improvement - with many teaching sta using data to improve their own practice, and many institutions proactively using learning analytics as a diagnostic tool on both an individual level (e.g. identifying issues) and a systematic level (e.g. informing the design of modules and degree programmes). 2. As a tool for boosting retention rates – with institutions using analytics to identify at risk students – and intervening with advice and support – at an earlier stage than would otherwise be possible.
  14. 14. 3. As a tool for assessing and acting upon differential outcomes among the student population – with analytics being used to closely monitor the engagement and progress of sub-groups of students. 4. As an enabler for the development and introduction of adaptive learning – i.e. personalised learning delivered at scale, whereby students are directed to learning materials on the basis of their previous interactions with, and understanding of, related content and tasks.
  15. 15. Motivaciones del 
 mini-seminario
  16. 16. Horizon Report > Edición Educación Superior 2016 Horizon Report > Edición Educación Superior 2016
  17. 17. 38 NMC Horizon Report: Edición Educación Superior 2016 Analíticas de aprendizaje y aprendizaje adaptativo Plazo estimado para su implementación: un año o menos L a analítica de aprendizaje es una aplicación educa- tiva de analítica web dirigida a un perfil de alumnos, un proceso de recopilación y análisis de datos sobre la interacción individual de los estudiantes con las actividades de aprendizaje online. El objetivo es crear nuevas pedagogías, fortalecer el aprendizaje acti- vo, reconocer la población en riesgo entre los estudiantes y evaluar los factores que afectan a la finalización de los estudios y al éxito de los estudiantes. Las tecnologías de aprendizaje adaptativo aplican las analíticas de apren- dizaje mediante software y plataformas online, adaptán- dolas a las necesidades individuales de los estudiantes. Un documento de Tyton Partners describe el aprendizaje adaptativo como un “enfoque sofisticado, basado en da- tos y, en algunos casos, no lineal aplicado a la formación y recuperación,queseajustaalasinteraccionesdelalumno y al nivel de rendimiento demostrado y, como consecuen- cia prevé qué tipo de contenido y recursos necesitan los alumnos en un momento específico para poder progre- sar.”252 En este sentido, las herramientas de educación contemporáneas son capaces, hoy en día, de aprender la manera en que las personas aprenden. Habilitadas por la tecnología de aprendizaje automático, pueden adaptarse de aprendizaje híbrido y en línea, donde las actividades de los estudiantes pueden ser monitorizadas por programas y aplicaciones de seguimiento. Muchos editores y empresas digitales de aprendizaje se centran en el aprendizaje adaptativoparareinventarsusserviciosbásicosdedesarrollo de libros de texto y material didáctico.256 Por ejemplo, Pearson se ha asociado con Knewton para desarrollar MyLab & Mastering,257 McGraw-Hill ha lanzado ALEKS,258 y Macmillan ofrece acceso a la tecnología adaptativa de PrepU.259 Los resultados iniciales son prometedores; en asociación con Knewton y Pearson, la nueva plataforma de aprendizaje adaptativo en matemáticas de desarrollo de la Arizona State University está dando lugar a un mejor rendimiento de los estudiantes que en la oferta de cursos tradicionales.260 Los líderes de opinión creen que el aprendizaje adaptativo continuará avanzando a medida que la educación superior adquiere conciencia de él, adopta las normas del plan de estudios, y hace un seguimiento sistemático de la marcha del alumno.261 Hay un número creciente de iniciativas que reúne a empresas privadas y a instituciones educativas para dar forma al futuro de aprendizaje adaptativo. Las iniciativas
  18. 18. Pero hace 6 años que está a punto de ocurrir :-) https://twitter.com/audreywatters/status/696057730126065666
  19. 19. Innovating Pedagogy 2016 Exploring new forms of teaching, learning and assessment, to guide educators and policy makers Mike Sharples, Roberto de Roock, Rebecca Ferguson, Mark Gaved, Christothea Herodotou, Elizabeth Koh, Agnes Kukulska- Hulme, Chee-Kit Looi, Patrick McAndrew, Bart Rienties, Martin Weller, Lung Hsiang Wong Open University Innovation Report 5 1 Contents Executive summary 3 Introduction 7 Learning through social media 12 Using social media to offer long-term learning opportunities Productive failure 16 Drawing on experience to gain deeper understanding Teachback 19 Learning by explaining what we have been taught Design thinking 22 Applying design methods in order to solve problems Learning from the crowd 25 Using the public as a source of knowledge and opinion Learning through video games 28 Making learning fun, interactive and stimulating Formative analytics 32 Developing anal tics that help learners to re ect and improve Learning for the future 35 Preparing students for work and life in an unpredictable future Translanguaging 38 Enriching learning through the use of multiple languages Blockchain for learning 41 Storing, validating and trading educational reputation 1 Contents Executive summary 3 Introduction 7 Learning through social media 12 Using social media to offer long-term learning opportunities Productive failure 16 Drawing on experience to gain deeper understanding Teachback 19 Learning by explaining what we have been taught Design thinking 22 Applying design methods in order to solve problems Learning from the crowd 25 Using the public as a source of knowledge and opinion Learning through video games 28 Making learning fun, interactive and stimulating Formative analytics 32 Developing anal tics that help learners to re ect and improve Learning for the future 35 Preparing students for work and life in an unpredictable future Translanguaging 38 Enriching learning through the use of multiple languages Blockchain for learning 41 Storing, validating and trading educational reputation
  20. 20. Innovating Pedagogy 2016 Exploring new forms of teaching, learning and assessment, to guide educators and policy makers Mike Sharples, Roberto de Roock, Rebecca Ferguson, Mark Gaved, Christothea Herodotou, Elizabeth Koh, Agnes Kukulska- Hulme, Chee-Kit Looi, Patrick McAndrew, Bart Rienties, Martin Weller, Lung Hsiang Wong Open University Innovation Report 5 1 Contents Executive summary 3 Introduction 7 Learning through social media 12 Using social media to offer long-term learning opportunities Productive failure 16 Drawing on experience to gain deeper understanding Teachback 19 Learning by explaining what we have been taught Design thinking 22 Applying design methods in order to solve problems Learning from the crowd 25 Using the public as a source of knowledge and opinion Learning through video games 28 Making learning fun, interactive and stimulating Formative analytics 32 Developing anal tics that help learners to re ect and improve Learning for the future 35 Preparing students for work and life in an unpredictable future Translanguaging 38 Enriching learning through the use of multiple languages Blockchain for learning 41 Storing, validating and trading educational reputation Design thinking Applying design methods in order to solve problems Learning from the crowd Using the public as a source of knowledge and opinion Learning through video games Making learning fun, interactive and stimulating Formative analytics Developing anal tics that help learners to re ect and improve Learning for the future Preparing students for work and life in an unpredictable future Translanguaging Enriching learning through the use of multiple languages Blockchain for learning
  21. 21. Definición La analítica del aprendizaje es la medida, recolección, análisis y presentación de datos sobre los estudiantes y sus contextos, con el propósito de comprender y optimizar el aprendizaje y el contexto en que tiene lugar. Long, P., Siemens, G., Conole, G., and Gasevic, D. (2011). Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK11), Banff, AB, Canada, Feb 27-Mar 01, 2011. New York: ACM.
  22. 22. Más definiciones Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics. Educause Quarterly, 1-20.
  23. 23. Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30.
  24. 24. Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134-138). El ciclo de la analítica del aprendizaje
  25. 25. Tipos de analíticas del aprendizaje Adarshsudhindra The different types of Learning Analytics https://commons.wikimedia.org/wiki/File:Types-of-Learning-Analytics.png
  26. 26. Analíticas macro/meso/micro Macro: Meso: Micro: región/estado/nación/internacional institución (Universidad) depto./titulación/asignatura/estudiante
  27. 27. Learning Analytics Framework (Greller & Drachsler, 2012) Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57
  28. 28. Modelo de referencia (Chatti et al., 2012) Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL), 4, 318-331. doi:10.1504/ IJTEL.2012.05181
  29. 29. ¿Qué? Datos y entornos • Learning Management System (aula virtual) & SIS (Studen Information System). • Activitity Data, Achievement Data, Static Data (ECAR). • Múltiples fuentes distribuidas.
  30. 30. ¿Quién? “Stakeholders" Estudiantes, profesores, tutores/ mentores (humanos o “inteligentes”), instituciones y administraciones educativas (administradores y gestores), investigadores y diseñadores de sistemas... (con diferentes perspectivas, intereses y expectativas).
  31. 31. ¿Para qué? Fines y objetivos de cada stakeholder • Monitorización y análisis. • Predicción e intervención. • Tutorización y mentorazgo. • Evaluación y retoralimentación. • Adaptación. • Personalización y recomendación • Reflexión. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL), 4, 318-331. doi:10.1504/ IJTEL.2012.05181
  32. 32. ¿Cómo? Métodos • Estadística. • Visualización de información (dashboards). • Minería de datos: • Clasificacion. • Clustering. • Association rule mining, etc. • Social Network Analysis.
  33. 33. Arnold, “Signals: Applying Academic Analytics,” EDUCAUSE Quarterly 33, no. 1; 
 Kimberly E. Arnold and Matthew D. Pistilli, “Course Signals at Purdue: Using Learning Analytics to Increase Student Success,” in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, ACM, April 2012, 267–270.
  34. 34. https://www.d2l.com/topics/learning-analytics/ D2L Brightspace Degree Compass (sic)
  35. 35. http://www.blackboard.com/Images/Bb_Predict_tcm21-38757.pdf
  36. 36. http://klassdata.com/es/smartklass-el- plugin-de-learning-analytics/learning- analytics-moodle-smartklass/
  37. 37. http://www.moodlenews.com/2016/the-learning-analytics- roadmap-the-dalton-plan-moodlemoot-australia-2016-lar- series-6/
  38. 38. Knewton: aprendizaje adaptativo
 https://youtu.be/LldxxVRj4FU
  39. 39. Plataformas de aprendizaje adaptativo
  40. 40. etc.
  41. 41. Futuro
  42. 42. Investigación Hershkovitz, A., Knight, S., Dawson, S., Jovanović, J., & Gašević, D. (2016). About" Learning" and" Analytics". Journal of Learning Analytics, 3(2), 1-5.
  43. 43. Reino Unido: grandes planes http://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics-higher-education
  44. 44. Learning Analytics in Higher Education A review of UK and international practice Full report April 2016 Authors Niall Sclater Alice Peasgood Joel Mullan Jisc is currently working with 50 universities in the UK to set up a national learning analytics service for higher and further education. This is the first time learning analytics has been deployed at a national level anywhere in the world, creating a unique opportunity for the UK to lead the world in the development of learning analytics.
  45. 45. University of Maryland, United States Students who obtain low grades use the VLE 40% less than those with C grades or higher. Used to identify effective teaching strategies which could be deployed on other modules. Purdue University, Indiana, United States Identifies potential problems as early as the second week of term. Users seek help earlier and more frequently. Led to 12% more B and C grades. 14% fewer D and F grades. California State University, Chico, United States Found that use of virtual learning environment can be used as a proxy for student effort. VLE use explained 25% of the variation in final grade – and was four times as strongly related to achievement as demographic factors. New York Institute of Technology, New York, United States 74% of students who dropped out had been predicted as at-risk by the data model. Marist College, New York, United States Predictive model provides students with earlier feedback - allowing them to address any issues before it is too late. 6% improvement in final grade by at-risk students who received a learning intervention. Edith Cowan University, Perth, Western Australia Created probability of retention scores for each undergraduate student – used to identify students most likely to need support. Nottingham Trent University, UK Strong link with retention- less than a quarter of students with a low average engagement progressed to the second year, whereas over 90% of students with good or high average engagement did so. Strong link with achievement - 81% of students with a high average engagement graduated with a 2:1 or first class degree, compared to only 42% of students with low average engagement. 27% of students reported changing their behaviour after using the system. Received a positive reception among students and staff. One third of tutors contacted students as a result of viewing their engagement data in the Dashboard. Open University, UK Analytics used to: » » inform strategic priorities to continually enhance the student experience, retention and progression drive interventions at student, module and qualification levels The Open Universities Australia Analytics used to: » » drive personalisation and adaptation of content recommended to individual students provide input and evidence for curriculum redesign Wollogong University, Australia SNAPP visualises participant relationships in online discussion forums in real time, as a network diagram. It helps facilitators to avoid dominating the conversation and encourage greater engagement with students who are less connected with their peers in the forum. University of New England, Australia Learning analytics is part of a wider ecosystem of engagement with students via social media to foster a sense of community amongst students who may be studying part time or at a distance as well as on campus.
  46. 46. Reflexiones
  47. 47. “Our Learning Analytics are 
 Our Pedagogy” Simon Buckingham Shum http://www.slideshare.net/sbs/our-learning- analytics-are-our-pedagogy
  48. 48. Is education 
 poised to become a data-driven enterprise
 and science? Simon Buckingham Shum http://www.slideshare.net/sbs/our-learning- analytics-are-our-pedagogy
  49. 49. La enorme diferencia entre conocer y medir
  50. 50. By W.v. Ravernstein @wiswijzer2, RT por Simon Buckingham-Shum
  51. 51. Medir
  52. 52. Conocer
  53. 53. Data entry: towards the critical study of digital data and education Neil Selwyn∗ Faculty of Education, Monash University, Melbourne, VIC, Australia (Received 13 March 2014; accepted 28 April 2014) The generation and processing of data through digital technologies is an integral element of contemporary society, as reflected in recent debates over online data privacy, ‘Big Data’ and the rise of data mining and ana- lytics in business, science and government. This paper outlines the signifi- cance of digital data within education, arguing for increased interest in the topic from educational researchers. Building on themes from the emerging sub-field of ‘digital sociology’, the paper outlines a number of ways in which digital data in education could be questioned along social lines. These include issues of data inequalities, the role of data in managerialist modes of organisation and control, the rise of so-called ‘dataveillance’ and the reductionist nature of data-based representation. The paper con- cludes with a set of suggestions for future research and discussion, thus out- lining the beginnings of a framework for the future critical study of digital data and education. Keywords: digital data; education; analytics; measurement Introduction The prominence of data as a social, political and cultural form has risen signifi- cantly in recent years. Of course, the process of collecting measurements, obser- vations and statistics together for reference and/or analysis has taken place for centuries. Yet the past 20 years or so have seen the increased recording, storage, manipulation and distribution of data in digital form (usually through compu- ters). In this sense, digital forms of data are now being generated and processed on an unprecedented scale. This shift is often described in terms of ‘three Vs’ of volume, velocity and variety – i.e., increases in the amount of data that is now being produced; the speed in which this data can be produced and processed and the range of data types and sources that now exist (Laney 2001). Yet digital data are also distinct from pre-digital forms by being exhaustive in scope, highly Learning, Media and Technology, 2014 http://dx.doi.org/10.1080/17439884.2014.921628 Selwyn, N. (2014). Data Entry: Towards the Critical Study of Digital data and Education. Learning, Media and Technology. http://dx.doi.org/ 10.1080/17439884.2014.921628
  54. 54. Preguntas • Los datos, ¿son neutros, objetivos, carentes de presupuestos epistemológicos, ideológicos, sociales, etc.) • ¿Analíticas de la enseñanza? ¿Por qué solo se habla de analíticas del aprendizaje y no de la vigilancia y control del profesorado? • ¿Qué visión del aprendizaje se da por aceptada en la implementación de las analíticas del aprendizaje?
  55. 55. Preguntas • ¿Cómo transforma la enseñanza y el aprendizaje universitarios el análisis sistemático y constante de las “huellas digitales” de los estudiantes y profesores? • ¿Cómo trasforma la AA los contenidos del currículum y la comunicación (online y offline) entre profesores y estudiantes y entre los propios estudiantes? • ¿Qué precauciones y garantías es necesario adoptar para el uso de datos personales? • ¿Cómo cambia la toma de decisiones y el gobierno de las universidades la analítica del aprendizaje?
  56. 56. ¿Son las analíticas el Gran Hermano?
  57. 57. ¿Caminamos hacia una “universidad panóptica”? https://worldwideweber2014.wordpress.com/2014/04/14/the-panopticon/
  58. 58. Muchas gracias por su atención.Y ahora…
  59. 59. http://www.thebluediamondgallery.com/tablet/d/debate.html

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