SlideShare a Scribd company logo
1 of 23
Download to read offline
What is a MOOC?
Nathalie Villa-Vialaneix
nathalie.villa@toulouse.inra.fr
http://www.nathalievilla.org
Journée “MOOC et Formation Continue en Statistique”
IHP, Paris, 3 mars 2015
Nathalie Villa-Vialaneix | What is a MOOC? 1/9
Definition
Massive Online Open Course
On-line, MOOCs are generally released on a given platform which
gathers several courses. The registration to the platforme is often
free;
they often use several tools related to new web technologies (videos,
interactive pages, ergonomic navigation tools...);
the course is simultaneously given for several thousands learners at a
fixed period (e.g., “Data Analysis”, J. Leek, JHU : 134 431, “Aléatoire”, S. Méléard,
EP : 9 600, “Introduction à la programmation orientée objet en C++”, Chappelier et
al, EPFL : 14 140);
active cooperation between learners via forums;
wide range of topics: some are very general (“Statistics one ”), others
are highly specialized (“Data management for clinical research ”).
Nathalie Villa-Vialaneix | What is a MOOC? 2/9
Some MOOC platforms non exhaustive list, restricted to platforms I have already tested
coursera: https://www.coursera.org
• created in 2012, 107 partners
(mostly academics)
• more than 1000 courses and 21
specialization programs (that gather
courses on a common topic); 71 courses
in the topic “Statistics and Data
analysis”
• courses are given in 21 languages
(36 are given in French)
• uses its own platform program (not
open-source but very well made, simple,
easy to navigate and integrating many tools,
such as videos, quiz-in-video, quizzes, pages
to upload essays...)
• can provide verified certificates of
validation (this service is not free)
Nathalie Villa-Vialaneix | What is a MOOC? 3/9
Some MOOC platforms non exhaustive list, restricted to platforms I have already tested
Udacity: https://www.udacity.com
• created in 2012, 15 partners (mostly
firms, such as Google, Nvidia, facebook...)
• ∼ 45 courses, all oriented toward
IT and 5 “nanodegrees”; 15 in the
field of “Data Science” (some of them
are DB courses)
• courses are given in English
• uses its own platform (a bit less easy
to navigate between the different parts of the
course; comprises videos with quizzes inside
the video, quizzes and a forum; the non-free
version of the course also gives access to
reviewed projects)
• can provide verified certificates of
validation (this service is not free)
Nathalie Villa-Vialaneix | What is a MOOC? 3/9
Some MOOC platforms non exhaustive list, restricted to platforms I have already tested
edX: https://www.edx.org
• created in 2012, 38 partners (mostly
academics but including, e.g., the Linux
Foundation)
• 429 courses and 13 Xseries; 45
courses in the field “Statistics and
Data Analysis”
• mostly in English but some courses are
in French (at least 5), Chinese, ...?
• platform based on the open-source
program “Open edX”, partially
developed by Google and also used
by the French platform FUN (less
flexible and easy to navigate, integrates
videos, and quizzes but (as far as I know) no
quiz-in-video and no page to upload
essays...)
Nathalie Villa-Vialaneix | What is a MOOC? 3/9
Some MOOC platforms non exhaustive list, restricted to platforms I have already tested
FUN (French platform):
http://www.france-universite-numerique.fr
• created in 2013, 43 partners
• ∼ 100 courses; 29 courses in the
field “Sciences” (at least one is taken
from coursera)
• courses are given in French
• platform based on the open-source
program “Open edX” which has been
customized (e.g., uses DailyMotion
instead of YouTube for posting videos)
Nathalie Villa-Vialaneix | What is a MOOC? 3/9
Navigating inside a MOOC
Nathalie Villa-Vialaneix | What is a MOOC? 4/9
Navigating inside a MOOC
Nathalie Villa-Vialaneix | What is a MOOC? 4/9
Examples of course outlines
“Data Analysis” (J. Leek, John Hopkins, coursera)
pre-requisites: apparently, none...
week 1 overview on data analysis and R
week 2 data management and organizing a data analysis
week 3 graphics and PCA
week 4 statistical inference and linear regression
week 5 ANOVA, GLM, variable selection
week 6 statistical learning, cross validation, regression trees
week 7 smoothing, bootstrap, bagging
week 8 multiple test correction, validation by simulation, summary
Nathalie Villa-Vialaneix | What is a MOOC? 5/9
Examples of course outlines
“Data Analysis” (J. Leek, John Hopkins, coursera)
pre-requisites: apparently, none...
week 1 overview on data analysis and R
week 2 data management and organizing a data analysis
week 3 graphics and PCA
week 4 statistical inference and linear regression
week 5 ANOVA, GLM, variable selection
week 6 statistical learning, cross validation, regression trees
week 7 smoothing, bootstrap, bagging
week 8 multiple test correction, validation by simulation, summary
plus: quizzes every week and 2 projects with real life and complex data on
weeks 3 and 6 (2 weeks are given to give the project back) and evaluate
the projects of 3 other students at least
Nathalie Villa-Vialaneix | What is a MOOC? 5/9
Examples of course outlines
“Data Analysis” (J. Leek, John Hopkins, coursera)
pre-requisites: apparently, none...
week 1 overview on data analysis and R
week 2 data management and organizing a data analysis
week 3 graphics and PCA
week 4 statistical inference and linear regression
week 5 ANOVA, GLM, variable selection
week 6 statistical learning, cross validation, regression trees
week 7 smoothing, bootstrap, bagging
week 8 multiple test correction, validation by simulation, summary
plus: quizzes every week and 2 projects with real life and complex data on
weeks 3 and 6 (2 weeks are given to give the project back) and evaluate
the projects of 3 other students at least ⇒ Be honnest, this cannot be
done if you have no pre-requisites!!
Nathalie Villa-Vialaneix | What is a MOOC? 5/9
Material
It is usually composed of:
videos (short ∼ 10 minutes, ∼ 5/10 for each week) with the teacher,
slides, animation on slides, movie...
Nathalie Villa-Vialaneix | What is a MOOC? 6/9
Material
It is usually composed of:
videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes,
videos are interrupted by very basic quizzes very helpful to check that
you have well understood the main idea and keep you focused on the
video
Nathalie Villa-Vialaneix | What is a MOOC? 6/9
Material
It is usually composed of:
videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes,
videos are interrupted by very basic quizzes Usually, videos can be
downloaded (MP4 format) as well as the slides (HTML or PDF)
Nathalie Villa-Vialaneix | What is a MOOC? 6/9
Material
It is usually composed of:
videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes,
videos are interrupted by very basic quizzes
quizzes: generally every week, they are short and can be part of the
evaluation. They are also more difficult than the basic quizzes
included in the videos
Nathalie Villa-Vialaneix | What is a MOOC? 6/9
Material
It is usually composed of:
videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes,
videos are interrupted by very basic quizzes
quizzes: generally every week
sometimes one or several exercises are given that are most
frequently part of the evaluation. They are marked by i) the platform
itself (if it can be tested, as for programs or a cleaned data sets) or ii)
by several other students (for reports)
Nathalie Villa-Vialaneix | What is a MOOC? 6/9
Material
It is usually composed of:
videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes,
videos are interrupted by very basic quizzes
quizzes: generally every week
sometimes one or several exercises
additional material (for further concepts), a discussion forum (that I
haven’t been using much), a wiki (students’ material), live meeting,
subtitling teams for the videos...
Nathalie Villa-Vialaneix | What is a MOOC? 6/9
Evaluation
Most MOOCs deliver certificates of validation.
basic version: a given amount of quizzes have been answered
properly, sometimes several attempts are allowed (sometimes with a
penalty at each new attempt); sometimes, the final mark is a
combination of the results to quizzes and the results to projects
(partially evaluated by your pairs)
Nathalie Villa-Vialaneix | What is a MOOC? 7/9
Evaluation
Most MOOCs deliver certificates of validation.
basic version: a given amount of quizzes have been answered
properly, sometimes several attempts are allowed (sometimes with a
penalty at each new attempt); sometimes, the final mark is a
combination of the results to quizzes and the results to projects
(partially evaluated by your pairs)
verified certificates: part of the MOOC business model is based on
these certificates that are charged; they are sometimes corrected by
an instructor (or an assistant)
Nathalie Villa-Vialaneix | What is a MOOC? 7/9
Some remarks
Why do I finish a MOOC?
the program, pre-requisites, requested effort, ... must be designed
carefully
videos must be short (can be watched at spare time)
videos are preferably interactive (can be watched while doing
something else)
quizzes must force me to come back to the course (preferably to PDF
version of the slides)
frequent assignments with a realistic objective
synchronization and deadlines are mandatory
Nathalie Villa-Vialaneix | What is a MOOC? 8/9
Some remarks
Why do I finish a MOOC?
the program, pre-requisites, requested effort, ... must be designed
carefully
videos must be short (can be watched at spare time)
videos are preferably interactive (can be watched while doing
something else)
quizzes must force me to come back to the course (preferably to PDF
version of the slides)
frequent assignments with a realistic objective
synchronization and deadlines are mandatory
⇒ only about 8 certificates on 23 MOOCs for me...
Nathalie Villa-Vialaneix | What is a MOOC? 8/9
Some remarks
Why do I finish a MOOC?
the program, pre-requisites, requested effort, ... must be designed
carefully
videos must be short (can be watched at spare time)
videos are preferably interactive (can be watched while doing
something else)
quizzes must force me to come back to the course (preferably to PDF
version of the slides)
frequent assignments with a realistic objective
synchronization and deadlines are mandatory
⇒ only about 8 certificates on 23 MOOCs for me...
I am not that lazy: the ratio of registered people who have obtained a
certificate is about 2/3%...
Nathalie Villa-Vialaneix | What is a MOOC? 8/9
Thank you for your attention...
... questions?
Nathalie Villa-Vialaneix | What is a MOOC? 9/9

More Related Content

What's hot

Remote practical-focused tutorials
Remote practical-focused tutorialsRemote practical-focused tutorials
Remote practical-focused tutorials
Jon Rosewell
 
English Links-up webinar presentation
English Links-up webinar presentationEnglish Links-up webinar presentation
English Links-up webinar presentation
Links-up
 
Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]
Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]
Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]
eileen.luebcke
 

What's hot (18)

20160308 createyour opencoursedesign (final)
20160308 createyour opencoursedesign (final)20160308 createyour opencoursedesign (final)
20160308 createyour opencoursedesign (final)
 
Creating and Enhancing Student Centred Portfolios in VLEs
Creating and Enhancing Student Centred Portfolios in VLEsCreating and Enhancing Student Centred Portfolios in VLEs
Creating and Enhancing Student Centred Portfolios in VLEs
 
Research and Teaching with Remo: Student research projects and teaching for a...
Research and Teaching with Remo: Student research projects and teaching for a...Research and Teaching with Remo: Student research projects and teaching for a...
Research and Teaching with Remo: Student research projects and teaching for a...
 
TAO DAYS - GeoGebra and TAO 2.0 by Raynald Jadoul
TAO DAYS - GeoGebra and TAO 2.0 by Raynald JadoulTAO DAYS - GeoGebra and TAO 2.0 by Raynald Jadoul
TAO DAYS - GeoGebra and TAO 2.0 by Raynald Jadoul
 
Getting started with Open Education
Getting started with Open EducationGetting started with Open Education
Getting started with Open Education
 
Interaction and interactivity in technology-rich second language classrooms: ...
Interaction and interactivity in technology-rich second language classrooms: ...Interaction and interactivity in technology-rich second language classrooms: ...
Interaction and interactivity in technology-rich second language classrooms: ...
 
Student Engagement with WebLearn
Student Engagement with WebLearnStudent Engagement with WebLearn
Student Engagement with WebLearn
 
The Whys and Wherefores of Wookie
The Whys and Wherefores of WookieThe Whys and Wherefores of Wookie
The Whys and Wherefores of Wookie
 
Remote practical-focused tutorials
Remote practical-focused tutorialsRemote practical-focused tutorials
Remote practical-focused tutorials
 
Encouraging Metacognition & Relf-Regulation in MOOCs through Increased Learne...
Encouraging Metacognition & Relf-Regulation in MOOCs through Increased Learne...Encouraging Metacognition & Relf-Regulation in MOOCs through Increased Learne...
Encouraging Metacognition & Relf-Regulation in MOOCs through Increased Learne...
 
UCA: Data Gathering Techniques. Selection criteria
UCA: Data Gathering Techniques. Selection criteriaUCA: Data Gathering Techniques. Selection criteria
UCA: Data Gathering Techniques. Selection criteria
 
English Links-up webinar presentation
English Links-up webinar presentationEnglish Links-up webinar presentation
English Links-up webinar presentation
 
Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]
Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]
Innovet project presentation kostelec en vesbe jkl 101010 [kompatibilitätsmodus]
 
1 courseinfo-5g 2
1 courseinfo-5g 21 courseinfo-5g 2
1 courseinfo-5g 2
 
#OEGlobal Less commonly used languages and OER
#OEGlobal Less commonly used languages and OER#OEGlobal Less commonly used languages and OER
#OEGlobal Less commonly used languages and OER
 
EURO Conference 2015 - Automated Timetabling
EURO Conference 2015 - Automated TimetablingEURO Conference 2015 - Automated Timetabling
EURO Conference 2015 - Automated Timetabling
 
Learning while Working
Learning while WorkingLearning while Working
Learning while Working
 
TU Delft OpenCourseWare for OCW EU workshop
TU Delft OpenCourseWare for OCW EU workshopTU Delft OpenCourseWare for OCW EU workshop
TU Delft OpenCourseWare for OCW EU workshop
 

Viewers also liked

Viewers also liked (7)

A comparison of learning methods to predict N2O fluxes and N leaching
A comparison of learning methods to predict N2O fluxes and N leachingA comparison of learning methods to predict N2O fluxes and N leaching
A comparison of learning methods to predict N2O fluxes and N leaching
 
Interpretable Sparse Sliced Inverse Regression for digitized functional data
Interpretable Sparse Sliced Inverse Regression for digitized functional dataInterpretable Sparse Sliced Inverse Regression for digitized functional data
Interpretable Sparse Sliced Inverse Regression for digitized functional data
 
Graph mining 2: Statistical approaches for graph mining
Graph mining 2: Statistical approaches for graph miningGraph mining 2: Statistical approaches for graph mining
Graph mining 2: Statistical approaches for graph mining
 
Cartes auto-organisée de Kohonen et clustering
Cartes auto-organisée de Kohonen et clusteringCartes auto-organisée de Kohonen et clustering
Cartes auto-organisée de Kohonen et clustering
 
Random Forest for Big Data
Random Forest for Big DataRandom Forest for Big Data
Random Forest for Big Data
 
Integrating Tara Oceans datasets using unsupervised multiple kernel learning
Integrating Tara Oceans datasets using unsupervised multiple kernel learningIntegrating Tara Oceans datasets using unsupervised multiple kernel learning
Integrating Tara Oceans datasets using unsupervised multiple kernel learning
 
Multiple kernel learning applied to the integration of Tara oceans datasets
Multiple kernel learning applied to the integration of Tara oceans datasetsMultiple kernel learning applied to the integration of Tara oceans datasets
Multiple kernel learning applied to the integration of Tara oceans datasets
 

Similar to What is a MOOC?

Presentationchapter2
Presentationchapter2Presentationchapter2
Presentationchapter2
viniciusbsb
 

Similar to What is a MOOC? (20)

Webinar REC:all
Webinar REC:allWebinar REC:all
Webinar REC:all
 
Feeding and Captivating OU Students
Feeding and Captivating OU StudentsFeeding and Captivating OU Students
Feeding and Captivating OU Students
 
EMBED explained
EMBED explainedEMBED explained
EMBED explained
 
EDUC5199G Session 1 Presentation
EDUC5199G Session 1 PresentationEDUC5199G Session 1 Presentation
EDUC5199G Session 1 Presentation
 
Lttc showcase & graduate conference
Lttc showcase & graduate conferenceLttc showcase & graduate conference
Lttc showcase & graduate conference
 
PGDEtechnology2
PGDEtechnology2PGDEtechnology2
PGDEtechnology2
 
Presentationchapter2
Presentationchapter2Presentationchapter2
Presentationchapter2
 
Development of a mobile French language learning platform
Development of a mobile French language learning platformDevelopment of a mobile French language learning platform
Development of a mobile French language learning platform
 
How can we move beyond recorded lectures?
How can we move beyond recorded lectures?How can we move beyond recorded lectures?
How can we move beyond recorded lectures?
 
Cost and time efficient dynamic learning def
Cost and time efficient dynamic learning defCost and time efficient dynamic learning def
Cost and time efficient dynamic learning def
 
MOOC Presentation for EDDE 801
MOOC Presentation for EDDE 801MOOC Presentation for EDDE 801
MOOC Presentation for EDDE 801
 
Teaching FEM software in formal and non-formal environment with MOOCs
Teaching FEM software in formal and non-formal environment with MOOCsTeaching FEM software in formal and non-formal environment with MOOCs
Teaching FEM software in formal and non-formal environment with MOOCs
 
Final group presentation
Final group presentationFinal group presentation
Final group presentation
 
Digital initiatives dr.c.thanavathi
Digital initiatives dr.c.thanavathi Digital initiatives dr.c.thanavathi
Digital initiatives dr.c.thanavathi
 
Bbslg conference-2007-tim-wales2405
Bbslg conference-2007-tim-wales2405Bbslg conference-2007-tim-wales2405
Bbslg conference-2007-tim-wales2405
 
Nursing Professional Development on Mobile Learning and Microlearning
Nursing Professional Development on Mobile Learning and MicrolearningNursing Professional Development on Mobile Learning and Microlearning
Nursing Professional Development on Mobile Learning and Microlearning
 
Lecture capture in your toolkit: building digital media into course design
Lecture capture in your toolkit: building digital media into course design Lecture capture in your toolkit: building digital media into course design
Lecture capture in your toolkit: building digital media into course design
 
Collaborative learning presentation
Collaborative learning presentationCollaborative learning presentation
Collaborative learning presentation
 
National education show final presentation (dw) 20.03.2015
National education show   final presentation (dw) 20.03.2015National education show   final presentation (dw) 20.03.2015
National education show final presentation (dw) 20.03.2015
 
Across System Learning Environment and Dashboard Design for K12 Teachers and ...
Across System Learning Environment and Dashboard Design for K12 Teachers and ...Across System Learning Environment and Dashboard Design for K12 Teachers and ...
Across System Learning Environment and Dashboard Design for K12 Teachers and ...
 

More from tuxette

More from tuxette (20)

Racines en haut et feuilles en bas : les arbres en maths
Racines en haut et feuilles en bas : les arbres en mathsRacines en haut et feuilles en bas : les arbres en maths
Racines en haut et feuilles en bas : les arbres en maths
 
Méthodes à noyaux pour l’intégration de données hétérogènes
Méthodes à noyaux pour l’intégration de données hétérogènesMéthodes à noyaux pour l’intégration de données hétérogènes
Méthodes à noyaux pour l’intégration de données hétérogènes
 
Méthodologies d'intégration de données omiques
Méthodologies d'intégration de données omiquesMéthodologies d'intégration de données omiques
Méthodologies d'intégration de données omiques
 
Projets autour de l'Hi-C
Projets autour de l'Hi-CProjets autour de l'Hi-C
Projets autour de l'Hi-C
 
Can deep learning learn chromatin structure from sequence?
Can deep learning learn chromatin structure from sequence?Can deep learning learn chromatin structure from sequence?
Can deep learning learn chromatin structure from sequence?
 
Multi-omics data integration methods: kernel and other machine learning appro...
Multi-omics data integration methods: kernel and other machine learning appro...Multi-omics data integration methods: kernel and other machine learning appro...
Multi-omics data integration methods: kernel and other machine learning appro...
 
ASTERICS : une application pour intégrer des données omiques
ASTERICS : une application pour intégrer des données omiquesASTERICS : une application pour intégrer des données omiques
ASTERICS : une application pour intégrer des données omiques
 
Autour des projets Idefics et MetaboWean
Autour des projets Idefics et MetaboWeanAutour des projets Idefics et MetaboWean
Autour des projets Idefics et MetaboWean
 
Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...
Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...
Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...
 
Apprentissage pour la biologie moléculaire et l’analyse de données omiques
Apprentissage pour la biologie moléculaire et l’analyse de données omiquesApprentissage pour la biologie moléculaire et l’analyse de données omiques
Apprentissage pour la biologie moléculaire et l’analyse de données omiques
 
Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...
Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...
Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...
 
Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...
Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...
Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...
 
Journal club: Validation of cluster analysis results on validation data
Journal club: Validation of cluster analysis results on validation dataJournal club: Validation of cluster analysis results on validation data
Journal club: Validation of cluster analysis results on validation data
 
Overfitting or overparametrization?
Overfitting or overparametrization?Overfitting or overparametrization?
Overfitting or overparametrization?
 
Selective inference and single-cell differential analysis
Selective inference and single-cell differential analysisSelective inference and single-cell differential analysis
Selective inference and single-cell differential analysis
 
SOMbrero : un package R pour les cartes auto-organisatrices
SOMbrero : un package R pour les cartes auto-organisatricesSOMbrero : un package R pour les cartes auto-organisatrices
SOMbrero : un package R pour les cartes auto-organisatrices
 
Graph Neural Network for Phenotype Prediction
Graph Neural Network for Phenotype PredictionGraph Neural Network for Phenotype Prediction
Graph Neural Network for Phenotype Prediction
 
A short and naive introduction to using network in prediction models
A short and naive introduction to using network in prediction modelsA short and naive introduction to using network in prediction models
A short and naive introduction to using network in prediction models
 
Explanable models for time series with random forest
Explanable models for time series with random forestExplanable models for time series with random forest
Explanable models for time series with random forest
 
Présentation du projet ASTERICS
Présentation du projet ASTERICSPrésentation du projet ASTERICS
Présentation du projet ASTERICS
 

Recently uploaded

call Now 9811711561 Cash Payment乂 Call Girls in Dwarka Mor
call Now 9811711561 Cash Payment乂 Call Girls in Dwarka Morcall Now 9811711561 Cash Payment乂 Call Girls in Dwarka Mor
call Now 9811711561 Cash Payment乂 Call Girls in Dwarka Mor
vikas rana
 

Recently uploaded (15)

$ Love Spells^ 💎 (310) 882-6330 in West Virginia, WV | Psychic Reading Best B...
$ Love Spells^ 💎 (310) 882-6330 in West Virginia, WV | Psychic Reading Best B...$ Love Spells^ 💎 (310) 882-6330 in West Virginia, WV | Psychic Reading Best B...
$ Love Spells^ 💎 (310) 882-6330 in West Virginia, WV | Psychic Reading Best B...
 
Top Rated Pune Call Girls Tingre Nagar ⟟ 6297143586 ⟟ Call Me For Genuine Se...
Top Rated  Pune Call Girls Tingre Nagar ⟟ 6297143586 ⟟ Call Me For Genuine Se...Top Rated  Pune Call Girls Tingre Nagar ⟟ 6297143586 ⟟ Call Me For Genuine Se...
Top Rated Pune Call Girls Tingre Nagar ⟟ 6297143586 ⟟ Call Me For Genuine Se...
 
2k Shots ≽ 9205541914 ≼ Call Girls In Jasola (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Jasola (Delhi)2k Shots ≽ 9205541914 ≼ Call Girls In Jasola (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Jasola (Delhi)
 
(Anamika) VIP Call Girls Navi Mumbai Call Now 8250077686 Navi Mumbai Escorts ...
(Anamika) VIP Call Girls Navi Mumbai Call Now 8250077686 Navi Mumbai Escorts ...(Anamika) VIP Call Girls Navi Mumbai Call Now 8250077686 Navi Mumbai Escorts ...
(Anamika) VIP Call Girls Navi Mumbai Call Now 8250077686 Navi Mumbai Escorts ...
 
2k Shots ≽ 9205541914 ≼ Call Girls In Dashrath Puri (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Dashrath Puri (Delhi)2k Shots ≽ 9205541914 ≼ Call Girls In Dashrath Puri (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Dashrath Puri (Delhi)
 
call Now 9811711561 Cash Payment乂 Call Girls in Dwarka Mor
call Now 9811711561 Cash Payment乂 Call Girls in Dwarka Morcall Now 9811711561 Cash Payment乂 Call Girls in Dwarka Mor
call Now 9811711561 Cash Payment乂 Call Girls in Dwarka Mor
 
2k Shots ≽ 9205541914 ≼ Call Girls In Mukherjee Nagar (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Mukherjee Nagar (Delhi)2k Shots ≽ 9205541914 ≼ Call Girls In Mukherjee Nagar (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Mukherjee Nagar (Delhi)
 
LC_YouSaidYes_NewBelieverBookletDone.pdf
LC_YouSaidYes_NewBelieverBookletDone.pdfLC_YouSaidYes_NewBelieverBookletDone.pdf
LC_YouSaidYes_NewBelieverBookletDone.pdf
 
9892124323, Call Girls in mumbai, Vashi Call Girls , Kurla Call girls
9892124323, Call Girls in mumbai, Vashi Call Girls , Kurla Call girls9892124323, Call Girls in mumbai, Vashi Call Girls , Kurla Call girls
9892124323, Call Girls in mumbai, Vashi Call Girls , Kurla Call girls
 
(Aarini) Russian Call Girls Surat Call Now 8250077686 Surat Escorts 24x7
(Aarini) Russian Call Girls Surat Call Now 8250077686 Surat Escorts 24x7(Aarini) Russian Call Girls Surat Call Now 8250077686 Surat Escorts 24x7
(Aarini) Russian Call Girls Surat Call Now 8250077686 Surat Escorts 24x7
 
The Selfspace Journal Preview by Mindbrush
The Selfspace Journal Preview by MindbrushThe Selfspace Journal Preview by Mindbrush
The Selfspace Journal Preview by Mindbrush
 
WOMEN EMPOWERMENT women empowerment.pptx
WOMEN EMPOWERMENT women empowerment.pptxWOMEN EMPOWERMENT women empowerment.pptx
WOMEN EMPOWERMENT women empowerment.pptx
 
2k Shots ≽ 9205541914 ≼ Call Girls In Palam (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Palam (Delhi)2k Shots ≽ 9205541914 ≼ Call Girls In Palam (Delhi)
2k Shots ≽ 9205541914 ≼ Call Girls In Palam (Delhi)
 
8377087607 Full Enjoy @24/7-CLEAN-Call Girls In Chhatarpur,
8377087607 Full Enjoy @24/7-CLEAN-Call Girls In Chhatarpur,8377087607 Full Enjoy @24/7-CLEAN-Call Girls In Chhatarpur,
8377087607 Full Enjoy @24/7-CLEAN-Call Girls In Chhatarpur,
 
Pokemon Go... Unraveling the Conspiracy Theory
Pokemon Go... Unraveling the Conspiracy TheoryPokemon Go... Unraveling the Conspiracy Theory
Pokemon Go... Unraveling the Conspiracy Theory
 

What is a MOOC?

  • 1. What is a MOOC? Nathalie Villa-Vialaneix nathalie.villa@toulouse.inra.fr http://www.nathalievilla.org Journée “MOOC et Formation Continue en Statistique” IHP, Paris, 3 mars 2015 Nathalie Villa-Vialaneix | What is a MOOC? 1/9
  • 2. Definition Massive Online Open Course On-line, MOOCs are generally released on a given platform which gathers several courses. The registration to the platforme is often free; they often use several tools related to new web technologies (videos, interactive pages, ergonomic navigation tools...); the course is simultaneously given for several thousands learners at a fixed period (e.g., “Data Analysis”, J. Leek, JHU : 134 431, “Aléatoire”, S. Méléard, EP : 9 600, “Introduction à la programmation orientée objet en C++”, Chappelier et al, EPFL : 14 140); active cooperation between learners via forums; wide range of topics: some are very general (“Statistics one ”), others are highly specialized (“Data management for clinical research ”). Nathalie Villa-Vialaneix | What is a MOOC? 2/9
  • 3. Some MOOC platforms non exhaustive list, restricted to platforms I have already tested coursera: https://www.coursera.org • created in 2012, 107 partners (mostly academics) • more than 1000 courses and 21 specialization programs (that gather courses on a common topic); 71 courses in the topic “Statistics and Data analysis” • courses are given in 21 languages (36 are given in French) • uses its own platform program (not open-source but very well made, simple, easy to navigate and integrating many tools, such as videos, quiz-in-video, quizzes, pages to upload essays...) • can provide verified certificates of validation (this service is not free) Nathalie Villa-Vialaneix | What is a MOOC? 3/9
  • 4. Some MOOC platforms non exhaustive list, restricted to platforms I have already tested Udacity: https://www.udacity.com • created in 2012, 15 partners (mostly firms, such as Google, Nvidia, facebook...) • ∼ 45 courses, all oriented toward IT and 5 “nanodegrees”; 15 in the field of “Data Science” (some of them are DB courses) • courses are given in English • uses its own platform (a bit less easy to navigate between the different parts of the course; comprises videos with quizzes inside the video, quizzes and a forum; the non-free version of the course also gives access to reviewed projects) • can provide verified certificates of validation (this service is not free) Nathalie Villa-Vialaneix | What is a MOOC? 3/9
  • 5. Some MOOC platforms non exhaustive list, restricted to platforms I have already tested edX: https://www.edx.org • created in 2012, 38 partners (mostly academics but including, e.g., the Linux Foundation) • 429 courses and 13 Xseries; 45 courses in the field “Statistics and Data Analysis” • mostly in English but some courses are in French (at least 5), Chinese, ...? • platform based on the open-source program “Open edX”, partially developed by Google and also used by the French platform FUN (less flexible and easy to navigate, integrates videos, and quizzes but (as far as I know) no quiz-in-video and no page to upload essays...) Nathalie Villa-Vialaneix | What is a MOOC? 3/9
  • 6. Some MOOC platforms non exhaustive list, restricted to platforms I have already tested FUN (French platform): http://www.france-universite-numerique.fr • created in 2013, 43 partners • ∼ 100 courses; 29 courses in the field “Sciences” (at least one is taken from coursera) • courses are given in French • platform based on the open-source program “Open edX” which has been customized (e.g., uses DailyMotion instead of YouTube for posting videos) Nathalie Villa-Vialaneix | What is a MOOC? 3/9
  • 7. Navigating inside a MOOC Nathalie Villa-Vialaneix | What is a MOOC? 4/9
  • 8. Navigating inside a MOOC Nathalie Villa-Vialaneix | What is a MOOC? 4/9
  • 9. Examples of course outlines “Data Analysis” (J. Leek, John Hopkins, coursera) pre-requisites: apparently, none... week 1 overview on data analysis and R week 2 data management and organizing a data analysis week 3 graphics and PCA week 4 statistical inference and linear regression week 5 ANOVA, GLM, variable selection week 6 statistical learning, cross validation, regression trees week 7 smoothing, bootstrap, bagging week 8 multiple test correction, validation by simulation, summary Nathalie Villa-Vialaneix | What is a MOOC? 5/9
  • 10. Examples of course outlines “Data Analysis” (J. Leek, John Hopkins, coursera) pre-requisites: apparently, none... week 1 overview on data analysis and R week 2 data management and organizing a data analysis week 3 graphics and PCA week 4 statistical inference and linear regression week 5 ANOVA, GLM, variable selection week 6 statistical learning, cross validation, regression trees week 7 smoothing, bootstrap, bagging week 8 multiple test correction, validation by simulation, summary plus: quizzes every week and 2 projects with real life and complex data on weeks 3 and 6 (2 weeks are given to give the project back) and evaluate the projects of 3 other students at least Nathalie Villa-Vialaneix | What is a MOOC? 5/9
  • 11. Examples of course outlines “Data Analysis” (J. Leek, John Hopkins, coursera) pre-requisites: apparently, none... week 1 overview on data analysis and R week 2 data management and organizing a data analysis week 3 graphics and PCA week 4 statistical inference and linear regression week 5 ANOVA, GLM, variable selection week 6 statistical learning, cross validation, regression trees week 7 smoothing, bootstrap, bagging week 8 multiple test correction, validation by simulation, summary plus: quizzes every week and 2 projects with real life and complex data on weeks 3 and 6 (2 weeks are given to give the project back) and evaluate the projects of 3 other students at least ⇒ Be honnest, this cannot be done if you have no pre-requisites!! Nathalie Villa-Vialaneix | What is a MOOC? 5/9
  • 12. Material It is usually composed of: videos (short ∼ 10 minutes, ∼ 5/10 for each week) with the teacher, slides, animation on slides, movie... Nathalie Villa-Vialaneix | What is a MOOC? 6/9
  • 13. Material It is usually composed of: videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes, videos are interrupted by very basic quizzes very helpful to check that you have well understood the main idea and keep you focused on the video Nathalie Villa-Vialaneix | What is a MOOC? 6/9
  • 14. Material It is usually composed of: videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes, videos are interrupted by very basic quizzes Usually, videos can be downloaded (MP4 format) as well as the slides (HTML or PDF) Nathalie Villa-Vialaneix | What is a MOOC? 6/9
  • 15. Material It is usually composed of: videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes, videos are interrupted by very basic quizzes quizzes: generally every week, they are short and can be part of the evaluation. They are also more difficult than the basic quizzes included in the videos Nathalie Villa-Vialaneix | What is a MOOC? 6/9
  • 16. Material It is usually composed of: videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes, videos are interrupted by very basic quizzes quizzes: generally every week sometimes one or several exercises are given that are most frequently part of the evaluation. They are marked by i) the platform itself (if it can be tested, as for programs or a cleaned data sets) or ii) by several other students (for reports) Nathalie Villa-Vialaneix | What is a MOOC? 6/9
  • 17. Material It is usually composed of: videos (short ∼ 10 minutes, ∼ 5/10 for each week) Sometimes, videos are interrupted by very basic quizzes quizzes: generally every week sometimes one or several exercises additional material (for further concepts), a discussion forum (that I haven’t been using much), a wiki (students’ material), live meeting, subtitling teams for the videos... Nathalie Villa-Vialaneix | What is a MOOC? 6/9
  • 18. Evaluation Most MOOCs deliver certificates of validation. basic version: a given amount of quizzes have been answered properly, sometimes several attempts are allowed (sometimes with a penalty at each new attempt); sometimes, the final mark is a combination of the results to quizzes and the results to projects (partially evaluated by your pairs) Nathalie Villa-Vialaneix | What is a MOOC? 7/9
  • 19. Evaluation Most MOOCs deliver certificates of validation. basic version: a given amount of quizzes have been answered properly, sometimes several attempts are allowed (sometimes with a penalty at each new attempt); sometimes, the final mark is a combination of the results to quizzes and the results to projects (partially evaluated by your pairs) verified certificates: part of the MOOC business model is based on these certificates that are charged; they are sometimes corrected by an instructor (or an assistant) Nathalie Villa-Vialaneix | What is a MOOC? 7/9
  • 20. Some remarks Why do I finish a MOOC? the program, pre-requisites, requested effort, ... must be designed carefully videos must be short (can be watched at spare time) videos are preferably interactive (can be watched while doing something else) quizzes must force me to come back to the course (preferably to PDF version of the slides) frequent assignments with a realistic objective synchronization and deadlines are mandatory Nathalie Villa-Vialaneix | What is a MOOC? 8/9
  • 21. Some remarks Why do I finish a MOOC? the program, pre-requisites, requested effort, ... must be designed carefully videos must be short (can be watched at spare time) videos are preferably interactive (can be watched while doing something else) quizzes must force me to come back to the course (preferably to PDF version of the slides) frequent assignments with a realistic objective synchronization and deadlines are mandatory ⇒ only about 8 certificates on 23 MOOCs for me... Nathalie Villa-Vialaneix | What is a MOOC? 8/9
  • 22. Some remarks Why do I finish a MOOC? the program, pre-requisites, requested effort, ... must be designed carefully videos must be short (can be watched at spare time) videos are preferably interactive (can be watched while doing something else) quizzes must force me to come back to the course (preferably to PDF version of the slides) frequent assignments with a realistic objective synchronization and deadlines are mandatory ⇒ only about 8 certificates on 23 MOOCs for me... I am not that lazy: the ratio of registered people who have obtained a certificate is about 2/3%... Nathalie Villa-Vialaneix | What is a MOOC? 8/9
  • 23. Thank you for your attention... ... questions? Nathalie Villa-Vialaneix | What is a MOOC? 9/9