VII Jornadas eMadrid
Round Table: "Learning Analytics"
Analytics in Open Education
Madrid, 4 de julio de 2017
Edmundo Tova...
Learning Analytics (LA)
— “The measurement, collection, analysis and reporting of
data about learners and their contexts, ...
The Environment
MOOCs and Open Education
A way of carrying out education, often using digital
technologies. Its aim is to widen access and participation to
everyon...
Profiles of MOOC participants
Problem analysis
ü MOOC format is characterized by the great diversity of enrolled
people
ü ...
New measurements
New measurements needed
— Different audiences than traditional HE degrees
— Different intentions and motivations
Source: o...
For whom?
— Main actors in this scenario: MOOCs providers and EC
— Cross provider issues to learn more about the populatio...
Collection
MOOCKnowledge project
Initiative launched by the Institute of Prospective Technological Studies (IPTS).
Executed by the Op...
Clustering
Analysis
Approach 1: Data mining
PhD student: Rosa Cabedo
Extended profiles of MOOC participants
74 participants with high languages
proficiency, reasonable motivation before their
enrollment in a new course, and withou...
Analysis
Approach 2: SNA
PhD Student: Jorge López
Exploration of SNA to the
Moocknowledge data
— An initial study has been conducted on the potential of
social network anal...
Study case
— Scenario:
— Analysis of the behavior of students attending the different levels of
involvement to the MOOC wh...
Study case.
1. Clustering of groups of participants
— The SNA analysis is based on the
similarity of participant profiles:...
Study case.
3. Identifying profile of students
“bridge”
— Profile of students contributing to the objective
— Second stage...
Reporting
Visualization Data
—Users should explore datasets even if the publisher of
the data does not provide any exploration or
vi...
Conclusions
ü Clustering is highlighted as a feasible technique for uncovering
participants’ profiles in MOOC format
ü Ext...
Thank you!
Edmundo Tovar Caro (etovar@fi.upm.es)
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VII Jornadas eMadrid "Education in exponential times". Mesa redonda eMadrid. Analytics in Open Education. Edmundo Tovar, UPM. 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Mesa redonda eMadrid. Analytics in Open Education. Edmundo Tovar, UPM. 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Mesa redonda eMadrid. Analytics in Open Education. Edmundo Tovar, UPM. 04/07/2017.

  1. 1. VII Jornadas eMadrid Round Table: "Learning Analytics" Analytics in Open Education Madrid, 4 de julio de 2017 Edmundo Tovar Caro etovar@fi.upm.es Technical University of Madrid CC BY Edmundo Tovar
  2. 2. Learning Analytics (LA) — “The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environment in which occurs” Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5
  3. 3. The Environment MOOCs and Open Education
  4. 4. A way of carrying out education, often using digital technologies. Its aim is to widen access and participation to everyone by removing barriers and making learning accessible, abundant, and customisable for all. It offers multiple ways of teaching and learning, building and sharing knowledge. It also provides a variety of access routes to formal and non-formal education, and connects the two Open Education is seen as Source: JRC IPTS Report: Opening up Education: a support framework for higher education institutions. http://publications.jrc.ec.europa.eu/repository/handle/JRC101436 Open Education CC BY Jonatan Castaño Muñoz Jornada red.es-UC3 sobre formación digital con tecnología abierta
  5. 5. Profiles of MOOC participants Problem analysis ü MOOC format is characterized by the great diversity of enrolled people ü There is a lack of knowledge of participants’ background ü It is needed to provide a real picture of the heterogeneity of MOOC participantsº
  6. 6. New measurements
  7. 7. New measurements needed — Different audiences than traditional HE degrees — Different intentions and motivations Source: online course report State of the MOOC 2016: A Year of Massive Landscape Change For Massive Open Online Courses CC BY Jonatan Castaño Muñoz Jornada red.es-UC3 sobre formación digital con tecnología abiert
  8. 8. For whom? — Main actors in this scenario: MOOCs providers and EC — Cross provider issues to learn more about the population of MOOC learners and to develop policy guidelines — Benchmarking reports will provide MOOC providers insigths about their MOOCs compared with other MOOCs
  9. 9. Collection
  10. 10. MOOCKnowledge project Initiative launched by the Institute of Prospective Technological Studies (IPTS). Executed by the Open University of the Netherlands (OUNL) in cooperation with the Open University of Catalonia (UOC) and Technical University of Madrid (UPM) MOOCKnowledge pursues to assess the current perspective of learners as participants of European MOOCs: when, why and how the participants enrolled in one or more MOOCs take advantage of them Goal: building a [large scale] and cross-provider data collection based on the experiences reported by the participants of the MOOCs offered by providers that collaborate with the project with the intention of a more deepen understanding of issues related to MOOC format Online multilingual survey: • Pre-questionnaire • Post-questionnaire • Follow-up questionnaire Pre-questionnaire: • attibutes: demographics, linguistics, motivation, intentions, LLL, MOOC experience, prior satisfaction of participants with MOOC format
  11. 11. Clustering
  12. 12. Analysis Approach 1: Data mining PhD student: Rosa Cabedo
  13. 13. Extended profiles of MOOC participants
  14. 14. 74 participants with high languages proficiency, reasonable motivation before their enrollment in a new course, and without previous experience in MOOC format 214 211 212 Preliminary findings: extended profiles 104 participants with high languages proficiency, reasonable motivation before their enrollment in a new course, and a high degree of previous satisfaction in MOOC format 53 participants with high languages proficiency, reasonable motivation before their enrollment in a new course, and indifference regarding MOOC format
  15. 15. Analysis Approach 2: SNA PhD Student: Jorge López
  16. 16. Exploration of SNA to the Moocknowledge data — An initial study has been conducted on the potential of social network analysis (SNA) and its metrics for: — Analyzing data obtained from the answers the participants. — Exploring useful scenarios. — The data source used comes from 715 participants of the MOOC on Test Anxiety Management.
  17. 17. Study case — Scenario: — Analysis of the behavior of students attending the different levels of involvement to the MOOC who have registered — Groups of interest: managers in open education, academic staff of MOOCs,… — Questions: C2B3_9, C2B3_10, C2B3_11, C2B3_12, C2B3_13 y C2B3_14 — Objective: — To increase the size of the target cluster in next editions of the MOOC — Phases: — 1. Clustering of participants — 2. Target cluster? — 3. Identifying profile of students “bridge” between the target cluster and other clusters — 4. Promoting the participation of “bridge” students in the future
  18. 18. Study case. 1. Clustering of groups of participants — The SNA analysis is based on the similarity of participant profiles: if two participants give the same answer to a question, a relationship exists between them. — The network consists of 316 nodes (participants) with 42117 relationships pointing. — We obtained three clusters represented with different colors. — Each cluster represents the pattern of participants that have the tendency to answer of the same way. HOW? SNA metrics and filters: • Weight (link) Clustering: modularity metric method After applying multiple filters to the network the number of nodes is reduced to 306 and the number of edges to 23216.
  19. 19. Study case. 3. Identifying profile of students “bridge” — Profile of students contributing to the objective — Second stage of tertiary education — Employed for wages — Woman — Spanish — Born in the 1960s — Living together with partner — Working in the Public administration and defense; compulsory social security sector HOW? SNA metrics and filters: Betweenness
  20. 20. Reporting
  21. 21. Visualization Data —Users should explore datasets even if the publisher of the data does not provide any exploration or visualization means. —Applying information visualization techniques to the data set helps users to explore large amounts of data and interact with them.
  22. 22. Conclusions ü Clustering is highlighted as a feasible technique for uncovering participants’ profiles in MOOC format ü Extended profiles as result of applying Data Mining or SNA techniques ü The identification of underlying relationships in the internal structure of participants’ ofeatures might facilitate the making- decisions process of policy-makers and educational-agents regarding the target audience their institutions Open Education, as well as MOOC format, could be positively impacted by a more realistic understanding of people’s profiles
  23. 23. Thank you! Edmundo Tovar Caro (etovar@fi.upm.es)

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