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

1. Analíticas del aprendizaje:
una perspectiva crítica
Jordi Adell
Centre d’Educació i Noves Tecnologies
Dept. d’Educació
Universitat Jaume I

2. Índice
• Definición. Marcos conceptuales. Presupuestos.
• Promesas.
• Tipos-Aplicaciones.
• Críticas: los peligros de “datificar” la enseñanza y
el aprendizaje.
• Debate

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

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

11. ¿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

12. 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.

13. 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».

14. Big data en la era post-Snowden

15. Las analíticas del aprendizaje son
la aplicación de las ideas,
tecnologías, procesos, etc. sobre
Big Data a la educación

16. 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.

17. 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».

18. 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

19. 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.

20. 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.

21. Motivaciones del
mini-seminario

22. Horizon Report > Edición Educación Superior 2016
Horizon Report > Edición Educación Superior 2016

24. 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

25. Pero hace 6 años que
está a punto de ocurrir :-)
https://twitter.com/audreywatters/status/696057730126065666

26. 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

27. 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

28. 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.

29. Más definiciones
Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics. Educause Quarterly, 1-20.

30. Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in
Learning and Education. EDUCAUSE review, 46(5), 30.

31. 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

33. Tipos de analíticas del aprendizaje
Adarshsudhindra The different types of Learning Analytics https://commons.wikimedia.org/wiki/File:Types-of-Learning-Analytics.png

34. Analíticas macro/meso/micro
Macro:
Meso:
Micro:
región/estado/nación/internacional
institución (Universidad)
depto./titulación/asignatura/estudiante

35. 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

36. 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

37. ¿Qué?
Datos y entornos
• Learning Management System (aula
virtual) & SIS (Studen Information
System).
• Activitity Data, Achievement Data,
Static Data (ECAR).
• Múltiples fuentes distribuidas.

38. ¿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).

39. ¿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

40. ¿Cómo?
Métodos
• Estadística.
• Visualización de información (dashboards).
• Minería de datos:
• Clasificacion.
• Clustering.
• Association rule mining, etc.
• Social Network Analysis.

42. 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.

43. https://www.d2l.com/topics/learning-analytics/
D2L Brightspace Degree Compass (sic)

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

45. http://klassdata.com/es/smartklass-el-
plugin-de-learning-analytics/learning-
analytics-moodle-smartklass/

46. http://www.moodlenews.com/2016/the-learning-analytics-
roadmap-the-dalton-plan-moodlemoot-australia-2016-lar-
series-6/

47. Knewton: aprendizaje adaptativo
https://youtu.be/LldxxVRj4FU

48. Plataformas de
aprendizaje adaptativo

49. etc.

50. Futuro

51. Investigación
Hershkovitz, A., Knight, S.,
Dawson, S., Jovanović, J., &
Gašević, D. (2016). About"
Learning" and" Analytics".
Journal of Learning Analytics,
3(2), 1-5.

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

54. 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.

55. 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.

57. Reflexiones

58. “Our Learning
Analytics are
Our Pedagogy”
Simon Buckingham Shum
http://www.slideshare.net/sbs/our-learning-
analytics-are-our-pedagogy

59. 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

60. La enorme diferencia
entre conocer y medir

61. By W.v. Ravernstein @wiswijzer2, RT por Simon Buckingham-Shum

62. Medir

63. Conocer

64. 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

65. 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?

66. 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?

67. ¿Son las analíticas
el Gran Hermano?

68. ¿Caminamos hacia una
“universidad panóptica”?
https://worldwideweber2014.wordpress.com/2014/04/14/the-panopticon/

69. Muchas gracias
por su atención.Y ahora…

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