Jordi Sancho - Learning Analytics - LASI Bilbao 2015
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Transcript of Jordi Sancho - Learning Analytics - LASI Bilbao 2015
Learning analytics to ignite massive collaborative projects
Jordi Sancho Interactive Media Lab - LMI
Social work department - Faculty of Education University of BarcelonaLearning Analytics Summer Institute, June 23th Deusto, Bilbao
Problem: • Massive collaboration projects in real life but not in education (we
can’t analyse and assess them)• Student’s assignments are wasted efforts (motivation, learning
and outcomes).
Solution: To develop new analytic methodologies that scale the assessment and ignite massive collaborative learning projects.
SUMMARY
Starting point
BIGFAILURE
Redice 04Project
10 subjects10 teachers900 students
Develop and link the most important concepts (in your opinion) for each subject
(wiki)
BIGFAILURE
We learnt…
What do we need to guide and assess an elephant stampede?
Macro level1. Classify collaborators and contents according to their role on the process.2. Classify created documents according to content.3. Classify collaborators according to their type and role of contribution.4. Classify documents according to their quality (teacher’s criteria).
Individual levelAssign and visualise all the above items for each student and for each document.
To manage a massive collaboration project on content creation, we need to:
Accepted the challenge: PhD thesis in 2012
1. Classify collaborators and contents according to their role and importance on the process
(SNA)
Editor 1 Editor 2
Page 1
Page 2
Editor 3
Page 3
Page 1 Page 2 Page 3
Editor 1 1 0 1
Editor 2 1 1 0
Editor 3 0 0 1
Editor 1 Editor 2 Editor 3
Editor 1 0 1 1
Editor 2 1 0 0
Editor 3 1 0 0
Page 1 Page 2 Page 3
Page 1 1 0 1
Page 2 1 0 0
Page 3 1 0 0
1013750310220556
10858746
10859855
10878442
10916172
11006376
11771561
98072306
98205505
Network of Editors
Network of Concepts
2. Classify created documents according to content(Factorial analysis)
3. Classify collaborators according to their type and role of contribution.
(k-means clustering)
Central group-Cluster 5-
CORREC_CORRECTA
INFO_RELLEV
EXEMPLE_RELLEV EXEMPLE_IRRELL CORREC_INC ESBORRA_CORRECTAM ESBORRA_INCORR REESTRUCT_CORRECT REESTRUCTURA_INCORR COMBINA_ARG CITA_FONTS
INFO_IRRELL
CREA_ENLLAC
FORMAT_WIKI 10137503, 5.0 10220556, 5.0 94176261, 5.0
Correcció ortogràfica correcta
Introdueix informació rellevant
Esborra correctamentEsborra incorrectamentReestructura correctamentReestructura incorrectament
Cita fonts
Introdueix informació irrellevant
Crea enllaç (hipervincle)
Format wiki
Combina argumentacions
Correcció ortogràfica incorrectaExemple irrellevantExemple rellevant
Variable values
Variables
Editors
Activity+-
CORREC_CORRECTA
INFO_RELLEV
EXEMPLE_RELLEV EXEMPLE_IRRELL CORREC_INC ESBORRA_CORRECTAM ESBORRA_INCORR REESTRUCT_CORRECT REESTRUCTURA_INCORR COMBINA_ARG CITA_FONTS
INFO_IRRELL
CREA_ENLLAC
FORMAT_WIKI 10222461, 3.0 10235050, 3.0 10528372, 3.0 11019072, 3.0 admin, 3.0
CORREC_CORRECTA
INFO_RELLEV
EXEMPLE_RELLEV EXEMPLE_IRRELL CORREC_INC ESBORRA_CORRECTAM ESBORRA_INCORR REESTRUCT_CORRECT REESTRUCTURA_INCORR COMBINA_ARG CITA_FONTS
INFO_IRRELL
CREA_ENLLAC
FORMAT_WIKI 10858746, 4.0 10861701, 4.0 10889830, 4.0 10897331, 4.0 98072306, 4.0
Linkers- Cluster 4 -
Formatters- Cluster 3 -
CORREC_CORRECTA
INFO_RELLEV
EXEMPLE_RELLEV EXEMPLE_IRRELL CORREC_INC ESBORRA_CORRECTAM ESBORRA_INCORR REESTRUCT_CORRECT REESTRUCTURA_INCORR COMBINA_ARG CITA_FONTS
INFO_IRRELL
CREA_ENLLAC
FORMAT_WIKI 10187391, 2.0 10259885, 2.0 10273594, 2.0 10854480, 2.0 10859855, 2.0 10876445, 2.0 10878431, 2.0 10878442, 2.0 10882900, 2.0 10883084, 2.0 10914061, 2.0 10916334, 2.0 10916916, 2.0 11002040, 2.0 11003521, 2.0 11006376, 2.0 11011405, 2.0 11017963, 2.0 11165512, 2.0 11763334, 2.0 11763850, 2.0 11771561, 2.0 97176752, 2.0 98205505, 2.0
CORREC_CORRECTA
INFO_RELLEV
EXEMPLE_RELLEV EXEMPLE_IRRELL CORREC_INC ESBORRA_CORRECTAM ESBORRA_INCORR REESTRUCT_CORRECT REESTRUCTURA_INCORR COMBINA_ARG CITA_FONTS
INFO_IRRELL
CREA_ENLLAC
FORMAT_WIKI 10020441, 0.0 10087431, 0.0 10090931, 0.0 10129453, 0.0 10137702, 0.0 10155611, 0.0 10209640, 0.0 10241254, 0.0 10286124, 0.0 10287406, 0.0 10311475, 0.0 10853205, 0.0 10859914, 0.0 10865901, 0.0 10869316, 0.0 10875480, 0.0 10877694, 0.0 10882686, 0.0 10890235, 0.0 10892372, 0.0 10903340, 0.0 10907046, 0.0 10907551, 0.0 10916135, 0.0 10916172, 0.0 11004641, 0.0 11008491, 0.0 11010786, 0.0 11010856, 0.0 11013785, 0.0 11014474, 0.0 11100471, 0.0 11103492, 0.0 11104752, 0.0 11109921, 0.0 11110050, 0.0 11118376, 0.0 11122915, 0.0 11130560, 0.0 11131422, 0.0 11135773, 0.0 11136532, 0.0 11167284, 0.0 11171020, 0.0 11173153, 0.0 11174763, 0.0 11771336, 0.0 93255175, 0.0
11012923, 1.0 11013520, 1.0 11014441, 1.0 11016025, 1.0 11016482, 1.0 11017845, 1.0 11100224, 1.0 11101333, 1.0 11106631, 1.0 11106874, 1.0 11107504, 1.0 11110805, 1.0 11111586, 1.0 11113410, 1.0 11117875, 1.0 11119824, 1.0 11122882, 1.0 11126312, 1.0 11127550, 1.0 11129602, 1.0 11130335, 1.0 11132634, 1.0 11136086, 1.0 11138573, 1.0 11141362, 1.0 11142880, 1.0 11144965, 1.0 11147043, 1.0 11151125, 1.0 11152105, 1.0 11153564, 1.0 11155745, 1.0 11156062, 1.0 11156810, 1.0 11157440, 1.0 11161581, 1.0 11162141, 1.0 11162734, 1.0 11163530, 1.0 11168986, 1.0 11169233, 1.0 11171996, 1.0 11175496, 1.0 11176712, 1.0 11177530, 1.0 11178790, 1.0 11181785, 1.0 11182124, 1.0 11182765, 1.0 11185182, 1.0 11186346, 1.0 11186560, 1.0 11223344, 1.0 11751445, 1.0 11751666, 1.0 11760560, 1.0 93503826, 1.0 95034133, 1.0 96225791, 1.0 98050820, 1.0 98221336, 1.0 99027095, 1.0 99028193, 1.0 99054012, 1.0 99140016, 1.0 99400534, 1.0
Informers and more- Cluster 2 -
Informers- Cluster 0 -
Passives- Cluster 1 -
3. Classify documents according to their quality (teacher’s criteria).
(Naive Bayes predictive algorithm)
Dichotomisation criteria
Type 1 texts
Type 2 texts
Language analysisLanguage analysis
Learning
Algorithm
Detects and identifies
Patterns
Probabilisticclassification
Text to classify
Input
From clusteranalysis
May the algoritm/programme identify if contents have been developed following teacher’s directives?
Text modelType 1
Text modelType 2
May the algoritm/programme identify if contents have been developed following teacher’s directives?
75% Concepts developed following directives.
62% Concepts not developed following directives.
Wiki
Metodologia
Anàlisi factorial
Algoritme predictiu
naive Bayes
Informació
Continguts
Wiki
Tipologies d’editors
Clústers per k-means
Classificació continguts
segons aportacions
Blocs temàtics
Importància editors
Importància continguts
Anàlisi de xarxes socials
Editors Aportacions
Editor importance
Content’s importance
Editor typologies
Thematic blocs
Contents classified based on
contributions
INFORMATION INTEGRATION
VISUALIZATION
Grau entre 1 i 9Grau entre 21 i 54Grau entre 10 i 20
Més de 32 edicions (percentil 95)11-31 edicionsMenys 11 edicions
Tamany del node segons la mesura d’intermediació
Individual information
Grau entre 1 i 9Grau entre 21 i 54Grau entre 10 i 20
Més de 32 edicions (percentil 95)11-31 edicionsMenys 11 edicions
Tamany del node segons la mesura d’intermediació
Grau entre 1 i 9Grau entre 21 i 54Grau entre 10 i 20
Més de 32 edicions (percentil 95)11-31 edicionsMenys 11 edicions
Tamany del node segons la mesura d’intermediació
10273594
Degree centrality: 9Intermediation: 4th (2,59%)C2: Informers and moreTeoria econòmica (5th editor)Social (10th editor)
Individual information
10137503
10187391
10220556
10222461
10235050
10259885
10273594
10528372
10854480
10858746
10859855
10861701
10876445
10878431
10878442
10882900
10883084
10889830
10897331
10914061
10916334
10916916
11002040
11003521
11006376
11011405
11017963
11019072
11165512
11763334
11763850
11771561
94176261
97176752
98072306
98205505
Clúster 4
Clúster 3
Clúster 5
Clúster 2
C 2: Molta informació i altresC 3: Editors de formatC 4: VinculadorsC 5: Grup motor
General information
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8Factor 9Factor 10
Més de 40 edicions30- 40 edicions20-30 edicionsMenys de 20 edicions
Tamany del node segons el grau de centralitat General information
Macro level1. Classify collaborators and contents according to their role on the process.2. Classify created documents according to content.3. Classify collaborators according to their type and role of contribution.4. Classify documents according to their quality (teacher’s criteria).
Individual levelAssign and visualize all the above items for each student and for each document.
To manage a massive collaboration project on content creation, we need to:
CONCLUSIONS
Starting with the usual teaching process and “poaching” data that makes sense.
1 . WHERE IS YOUR STANDING?
Designing learning processes with the analytics in mind.
vs.
We can think big. We can rethink learning. We should ignite massive collaborating projects
(doing the same but just marginally better is not enough)
1805
1387
2. DRAWING NEW MAPS HAS PROBLEMS
“ Unknown Parts”
Sometimes the addition of an improved methodology produces a loss of useful knowledge. In the short time.
Are we still there?
Sancho, J. (2012) Muerte y resurrección de la universidad (again) en manos de la colaboración masiva: avanzar los MOOC. Dins de Bergmann, Juliana i Grané, Mariona (Editores) La universidad en la nube. Barcelona: Transmedia XXI.
Sancho, J. (2011) La evaluación de proyectos colaborativos a gran escala basados en wikis mediante el análisis de redes sociales. Dins de Cano, E. (Editora)(2011) Aprobar o aprender. Estrategias de evaluación en la sociedad red. Barcelona: Transmedia XXI.
Thank you!
Jordi [email protected]: @JordiSanchoS