REPUTATION IN MOOC

1. Towards learning resources rankings in
MOOCs: A Pairwise based Reputation
Mechanism
R. Centeno, M. Rodríguez-Artacho, Félix García, Elio
Sancristóbal, Gabriel Díaz, Manuel Castro
miguel@lsi.uned.es
UNED University, Spain

2. Content
• Reputation
• Integrating remote laboratories in MOOCs
• Evaluating MOOC content
• A Pairwise reputation mechanism for MOOCs
• Conclusions
IEEE EDUCON 2015, Tallinn

3. Reputation
• many types: professional links, friendships, purchases, ...
• complex: dynamism, complexity of the social structure, many
nodes (users, entities, ..)
• interaction results are unpredictable (which seller to select,
which hotel to book, …)
• how can we predict future behaviours?
3
Complex Social Networks Reputation
IEEE EDUCON 2015, Tallinn

4. Reputation mechanisms in social
networks
IEEE EDUCON 2015, Tallinn
objective: extract reputation of entities (users, objects, …)
how: gathering and aggregating opinions
Examples:

5. Reputation mechanisms in social
networks
IEEE EDUCON 2015, Tallinn
objective: extract reputation of entities (users, objects, …)
how: gathering and aggregating opinions
Examples:

6. Reputation mechanisms in social
networks
Qualitative vs.Quantitative  “Experts” vs.
Users
Unbalanced source  text oppinion vs.
Numerical rating
 Lack of accuracy numerical ratings
IEEE EDUCON 2015, Tallinn

7. MOOC Example  DIEEC UNED
Integrating remote laboratories in MOOCs
◌ Module 1: Simulator.
◌ Module 2: VISIR.
◌ Module 3: Working with resistors. Ohmic values. Voltage divider.
◌ Module 4: RLC circuits. RL, RLC & RC circuits.
◌ Module 5: Working with diodes. Differences between 1N4007 & BAT42.
Halfwave rectifier. Voltage drop on diode.
◌ Module 6: Low-pass filter. Mean value, voltage ripple, load regulation
and line regulation.
◌ Module 7: Zener diode. Zener diode as voltage regulator. Zener diode as
clipper. Construction of the current-voltage characteristic curve.
◌ Module 8: Operational amplifier. Non-inverting amplifier. Inverting
differentiator. Inverting amplifier
IEEE EDUCON 2015, Tallinn

8. Virtual Instrument Systems in Reality (VISIR)

9. MOOC Example  DIEEC UNED
Organization
 Access to experiments is provided by the
MOOC’s portal through an integrated
scheduling/booking system
 The initial settings allow 16 simultaneous
users per 60 minutes slot and for each
user a maximum of two simultaneous
slots booked and a limitation of 14 slots
per course
 With these settings, VISIR allows up to 384
students to experiment with any of the
designed practices of the MOOC

10. Cuantitative vs. Comparative reviews
IEEE EDUCON 2015, Tallinn

11. Cuantitative vs. Comparative reviews
Easier for users to state opinions when the
query compare objects in a pairwise fashion
Ben-Hur 9
Casablanca 7.4
Gone with the wind 8.2
IEEE EDUCON 2015, Tallinn

12. Cuantitative vs. Comparative reviews
• Potential problems of numerical oppinion
– Passive  wait for users to grade
– Influenciable and manipulable  viral ratings
• Reputation based on comparative reviews 
Pairwise reputation mechanism (PWRM)
IEEE EDUCON 2015, Tallinn

13. MOOC formalization
M = {U,R,LR,LU} is a MOOC where:
U = {u1,..,un} users (students AND teachers)
R = {r1,..,rj} learning resources
LR = { <ui,rj> / ui € U; rj € R }  user ui has uploaded
resource rj
LU = { <uk,rm> / uk € U; rm € R }  user uk has used
resource rm
IEEE EDUCON 2015, Tallinn

14. MOOC formalization
• Live envorinment in term of resources
• Objective  build ranking of resources within
the MOOC
• Asumption: Users have set of resources’
preferences on a subset
• Let O set of oppinions where oi=<ri,rj>
representing a pairwise query
R' Í R
IEEE EDUCON 2015, Tallinn

15. MOOC formalization
IEEE EDUCON 2015, Tallinn

16. Conclusions
• Reputation mechanism to be applied within MOOCs
• Massive implies accuracy, but resources are often not
provided by many users
• Clustering criteria  Selection process
 According to typology (Multimedia, video, audio, …)
 According to metadata (Pedagogical objectives,
granularity, etc. )
IEEE EDUCON 2015, Tallinn

17. Towards learning resources rankings in
MOOCs: A Pairwise based Reputation
Mechanism
R. Centeno, M. Rodríguez-Artacho, Félix García, Elio
Sancristóbal, Gabriel Díaz, Manuel Castro
miguel@lsi.uned.es
UNED University, Spain
Thanks!