VII Jornadas eMadrid "Education in exponential times". Llanos Tobarra: "Studying Students' Behavior in UNED-COMA MOOCs". 05/07/2017.

1. ANALYZING STUDENTS’ BEHAVIOR IN UNED-COMA
MOOCS
Ll.Tobarra, S. Ros, R. Hernández,A. Robles-Gómez,
R. Pastor,A. C. Caminero, J. Cano, and J.
Claramonte

2. UNED COMA
 UNED, has started the UNED-COMA (Curso Online Masivo Abierto; in English, UNEDMOOC) project for
managing massive courses with multimedia videos and social interaction tools (like forums).
 38 courses.
 Several editions.
 xMOOCs.
 Certificates:
 On-line badge.
 Face-to-face exam.
 In particular, UNED-COMA is an open initiative created in the 2012 year.
 The OpenMOOC platform is employed for this purpose.
 The main objective of this initiative has been the exploration of the rich experience with the distance educational
methodology employed at UNED.
 Not oriented towards learning analytics.
 Nowadays this platform has been replaced by OpenEDX
 URL: https://unedabierta.uned.es/wp/

3. EXISTING DATA
 OpenMooc has three databases:
 Database PostgreSQL related towards the structure of the courses and users profile data.
 Database MySQL, a database for each forum:
 OpenMOOC is based on AskBoot for handling forums (https://askbot.com/).
 Database NoSQL MongoDB, oriented towards activities interactions  this database was not available.
 Youtube analytics from 2015:
 Fundación CSEV (https://www.youtube.com/user/FundacionCSEV).
 UNED Abierta (https://www.youtube.com/user/UNEDcursoscoma).

4. EVOLUTION OF STUDENT REGISTRATION

5. ACTIVITY OF THE STUDENTS

6. POSTGRESQL: STRUCTURAL CONCEPT
Course Users Groups
Badges
Modules
Pills (videos)
Attachements
(PDFs)
Quizzes
Peer reviews
Internal
Mail

7. POOR STUDENT PROFILE INFORMATION
 Detect the sex of the students using an
automated function that uses public name
lists such as Spanish Government Statistics
popular names lists and the usernames.
 Not always is possible.
 Almost a 10% is not classified.

8. INITIAL ANALYSIS OF KNOWLEDGE PILLS

9. YOUTUBE ANALYTICS

10. YOUTUBE ANALYTICS
• Evaluation related videos have
a high number of views and
retention.
• Rest of the videos have a
lower number of views.

11. ANALYSIS
 Hypothesis 1: shorter videos are better for the students’ outcomes.
 Hypothesis 2: courses with fewer videos are more successful that those ones with a great number of videos.
 Features:
 Number of videos per course.
 Mean duration of videos per course.
 Mean amount of visits per video in the course.
 Mean amount of likes per video in the course.

12. FORUMS INITIAL ANALYSIS

13. FORUM:ASKBOT

14. FORUM SCHEMA
Users
tags, reputation
Posts
score, vote_up_count
Three types: coments,
questions and answears
Threads
tags, views, favorites
Reviews
Activity
Content_type Type of activities: voting,
answering,…
Vote
award
badges

15. FORUM ACTIVITY

16. POSTS INSIDE THE COURSES

17. EXAMPLE OF FORUM BEHAVIOUR
 Activity evolution

18. USER BEHAVIOUR
 Statistics and histogram:
 Only 2240 students participate in
the forum (reading or writing):
around a 10% of the students in
the course.

19. FORUM
 Textual analysis.
 Words are not very
significative.

20. ANALYSIS
 Hypothesis 3: a elevated number of activities in the forums of the course is related to the student success within
the course.
 Features:
 Number of activities.
 Number of questions.
 Number of answers.
 Number of comments.
 Percentage of the number of students.
 Average number of activities per user.
 Average number of activities per day.

21. CONCLUSIONS
 Lack of the activities data has an impact in the outcomes of our analysis.
 A set of hypothesis have also been proposed and discussed from the detected candidate variables (indicators)
from datasets.
 This indicators are about students’ behavior in MOOCs given both from the point of view of videos and debate
forums.
 A clear need to improve the platform with further learning analytics.