Analíticas del aprendizaje: una perspectiva crítica

Analíticas del aprendizaje: una perspectiva crítica

Jordi AdellCentre d’Educació i Noves Tecnologies

Dept. d’EducacióUniversitat Jaume I

Índice• Definición. Marcos conceptuales. Presupuestos.

• Promesas.

• Tipos-Aplicaciones.

• Críticas: los peligros de “datificar” la enseñanza y el aprendizaje.

• Debate

http://www.excelacom.com/resources/blog/2016-update-what-happens-in-one-internet-minute

http://www.excelacom.com/resources/blog/2016-update-what-happens-in-one-internet-minute

https://www.flickr.com/photos/84593672@N05/9427663067

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-data-theory

https://www.theguardian.com/news/datablog/2012/mar/09/big-data-theory

Big Data como mitodanah 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 are clamoringfor 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? Orwill 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 usedto 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 itis necessary to critically interrogate its assumptions and biases. In this article, we offersix provocations to spark conversations about the issues of Big Data: a cultural, techno-logical, and scholarly phenomenon that rests on the interplay of technology, analysis, andmythology that provokes extensive utopian and dystopian rhetoric.

Keywords Big Data; analytics; social media; communication studies;social network sites; philosophy of science; epistemology; ethics; Twitter

(Received 10 December 2011; final version received 20 March 2012)

Technology is neither good nor bad; nor is it neutral . . . technology’s inter-action with the social ecology is such that technical developments frequentlyhave environmental, social, and human consequences that go far beyond theimmediate purposes of the technical devices and practices themselves.(Kranzberg 1986, p. 545)

Information, Communication & Society Vol. 15, No. 5, June 2012, pp. 662–679

ISSN 1369-118X print/ISSN 1468-4462 online # 2012 Microsoft

http://www.tandfonline.com http://dx.doi.org/10.1080/1369118X.2012.678878

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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.

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 andparadigm shifts

Rob Kitchin

AbstractThis 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 oftheory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities andcomputational social sciences that propose radically different ways to make sense of culture, history, economy andsociety. It is argued that: (1) Big Data and new data analytics are disruptive innovations which are reconfiguring in manyinstances how research is conducted; and (2) there is an urgent need for wider critical reflection within the academy onthe epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite therapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, itis contended that a potentially fruitful approach would be the development of a situated, reflexive and contextuallynuanced epistemology.

KeywordsBig Data, data analytics, epistemology, paradigms, end of theory, data-driven science, digital humanities, computationalsocial 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 hasbeen variously defined and operationalized, rangingfrom trite proclamations that Big Data consists of data-sets too large to fit in an Excel spreadsheet or be storedon a single machine (Strom, 2012) to more

sophisticated ontological assessments that tease out itsinherent characteristics (boyd and Crawford, 2012;Mayer-Schonberger and Cukier, 2013). Drawing onan extensive engagement with the literature, Kitchin(2013) details that Big Data is:

. huge in volume, consisting of terabytes or petabytesof data;

. high in velocity, being created in or near real-time;

. diverse in variety, being structured and unstructuredin nature;

. exhaustive in scope, striving to capture entire popu-lations or systems (n¼ all);

National Institute for Regional and Spatial Analysis, National University ofIreland Maynooth, County Kildare, Ireland

Corresponding author:

Rob Kitchin, National Institute for Regional and Spatial Analysis, NationalUniversity of Ireland Maynooth, County Kildare, Ireland.Email: [email protected]

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-NonCommercial3.0 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».

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 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.

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».

Las promesas de la analítica del

aprendizaje

Learning Analytics in Higher Education A review of UK and international practice Full report

April 2016

AuthorsNiall SclaterAlice PeasgoodJoel Mullan

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.

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.

Motivaciones del mini-seminario

Horizon Report > Edición Educación Superior 2016Horizon Report > Edición Educación Superior 2016

38 NMC Horizon Report: Edición Educación Superior 2016

Analíticas de aprendizaje y aprendizaje adaptativoPlazo estimado para su implementación: un año o menos

La 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, que se ajusta a las interacciones del alumno 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 a cada estudiante en tiempo real.

Visión generalLas instituciones de todo el mundo han reconocido que el enfoque universal en la enseñanza aleja a los estudiantes que tienen dificultades con conceptos específicos, así como a aquellos que entienden los argumentos más rápidamente que sus compañeros.253 A medida que la analítica del aprendizaje madura, las universidades van teniendo un amplio acceso a herramientas y grandes conjuntos de datos necesarios para comenzar a personalizar la experiencia del aprendizaje.254 A través de soluciones basadas en datos que reducen el tiempo necesario para la obtención del título, mejoran los resultados de los estudiantes, y se dirigen a ellos para que se matriculen, la analítica de aprendizaje está beneficiando no sólo a los alumnos e instructores, sino a una gama de partes interesadas: los órganos de gobierno, investigadores e instituciones. El análisis del aprendizaje se ha desarrollado en tres etapas, pasando de un enfoque basado en la visión retrospectiva a la previsión; la primera etapa describía los resultados, la segunda etapa se basaba en el diagnóstico, y la tercera y actual etapa consiste en la predicción de lo que sucederá en el futuro. La creación de datos procesables es una característica de aprendizaje adaptativo, que es el último foco de experimentos y programas piloto en diferentes contextos educativos.255

El aprendizaje adaptativo es el más adecuado en ambientes

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 adaptativo para reinventar sus servicios básicos de desarrollo 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 de aprendizaje personalizados de la Bill & Melinda Gates Fundation, son algunas de las más activas en esta área. Su Adaptive Learning Market Acceleration Grant Program (ALMAP) es uno de los varios programas que pretenden impulsar el progreso en el campo; otorga subvenciones a los colleges y universidades para estudiar las plataformas de aprendizaje adaptativo en más de 20 cursos a través de diferentes enfoques pedagógicos.262 Del mismo modo, el IMS Global Learning Consortium es un consorcio de más de 300 proveedores y universidades que trabajan con un lenguaje común para el seguimiento y la presentación de informes de analítica de aprendizaje. Conocido como Caliper, estos perfiles métricos podrían convertirse en un estándar común sobre cómo se recoge el aprendizaje del estudiante más allá del consorcio.263

Relevancia en la enseñanza, el aprendizaje, o la investigación creativaLas instituciones están mejorando su capacidad de rendición de cuentas para mejorar las tasas universitarias e identificar a los estudiantes en riesgo de abandonar la escuela. La University of Tennessee en Chattanooga está usando la analítica para determinar las posibles áreas problemáticas. Al investigar las tasas de graduación de sus estudiantes de enfermería, por ejemplo, la universidad ha descubierto algo que no habían previsto; los estudiantes se veían obligados a elegir una especialidad diferente porque tenían dificultades con un curso específico de Inglés en lugar

Pero hace 6 años que está a punto de ocurrir :-)

https://twitter.com/audreywatters/status/696057730126065666

Innovating Pedagogy 2016Exploring 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

ContentsExecutive summary 3

Introduction 7

Learning through social media 12Using social media to offer long-term learning opportunities

Productive failure 16Drawing on experience to gain deeper understanding

Teachback 19Learning by explaining what we have been taught

Design thinking 22Applying design methods in order to solve problems

Learning from the crowd 25Using the public as a source of knowledge and opinion

Learning through video games 28Making learning fun, interactive and stimulating

Formative analytics 32Developing anal tics that help learners to re ect and improve

Learning for the future 35Preparing students for work and life in an unpredictable future

Translanguaging 38Enriching learning through the use of multiple languages

Blockchain for learning 41Storing, validating and trading educational reputation

1


Introduction 7











Innovating Pedagogy 2016Exploring 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


Introduction 7











1


Introduction 7











DefiniciónLa 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.

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 conference on learning

analytics and knowledge (pp. 134-138).

El ciclo de la analítica del aprendizaje

Tipos de analíticas del aprendizaje

Adarshsudhindra The different types of Learning Analytics https://commons.wikimedia.org/wiki/File:Types-of-Learning-Analytics.png

Analíticas macro/meso/micro

Macro:

Meso:

Micro:

región/estado/nación/internacional

institución (Universidad)

depto./titulación/asignatura/estudiante

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

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

¿Qué? Datos y entornos

• Learning Management System (aula virtual) & SIS (Studen Information System).

• Activitity Data, Achievement Data, Static Data (ECAR).

• Múltiples fuentes distribuidas.

¿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).

¿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

¿Cómo? Métodos

• Estadística.• Visualización de información (dashboards).• Minería de datos:

• Clasificacion.

• Clustering.

• Association rule mining, etc.

• Social Network Analysis.

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.

https://www.d2l.com/topics/learning-analytics/

D2L Brightspace Degree Compass (sic)

http://www.blackboard.com/Images/Bb_Predict_tcm21-38757.pdf

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 adaptativohttps://youtu.be/LldxxVRj4FU

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" Analytics". Journal of Learning Analytics, 3(2), 1-5.

Reino Unido: grandes planes

http://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics-higher-education

http://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics-higher-education

Learning Analytics in Higher Education A review of UK and international practice Full report

April 2016

AuthorsNiall SclaterAlice PeasgoodJoel 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.

6 Learning Analytics in Higher Education

Executive summary

University of Maryland,United StatesStudents 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 StatesIdentifies 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 StatesFound 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 States74% of students who dropped out had been predicted as at-risk by the data model.

Marist College,New York, United StatesPredictive 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.

7Learning Analytics in Higher Education

Executive summary

Edith Cowan University,Perth, Western AustraliaCreated probability of retention scores for each undergraduate student – used to identify students most likely to need support.

Nottingham Trent University, UKStrong 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, AustraliaSNAPP 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, AustraliaLearning 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.

Reflexiones

“Our Learning Analytics are

Our Pedagogy”

Simon Buckingham Shum http://www.slideshare.net/sbs/our-learning-

analytics-are-our-pedagogy

http://www.slideshare.net/sbs/our-learning-analytics-are-our-pedagogy


Is education poised to become

a data-drivenenterprise

and science?Simon Buckingham Shum




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, Melbourne, VIC, Australia

(Received 13 March 2014; accepted 28 April 2014)

The generation and processing of data through digital technologies is anintegral element of contemporary society, as reflected in recent debatesover 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 thetopic from educational researchers. Building on themes from the emergingsub-field of ‘digital sociology’, the paper outlines a number of ways inwhich digital data in education could be questioned along social lines.These include issues of data inequalities, the role of data in managerialistmodes 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 digitaldata 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 forcenturies. 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 processedon an unprecedented scale. This shift is often described in terms of ‘three Vs’ ofvolume, velocity and variety – i.e., increases in the amount of data that is nowbeing produced; the speed in which this data can be produced and processed andthe range of data types and sources that now exist (Laney 2001). Yet digital dataare also distinct from pre-digital forms by being exhaustive in scope, highlydetailed and flexible in the ways that it can be combined (Kitchin 2014).Indeed, the constant circulation and reconstitution of digital data that nowtakes place has prompted talk of a ‘data deluge’ (The Economist 2010) and

# 2014 Taylor & Francis

∗Email: [email protected]

Learning, Media and Technology, 2014http://dx.doi.org/10.1080/17439884.2014.921628

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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

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?

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?

¿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

http://www.thebluediamondgallery.com/tablet/d/debate.html