IEEE EDUCON 2015 reputation mooc

Towards learning resources rankings in MOOCs: A Pairwise based Reputation

Mechanism

R. Centeno, M. Rodríguez-Artacho, Félix García, Elio Sancristóbal, Gabriel Díaz, Manuel Castro

[email protected] University, Spain

mailto:[email protected]

Content

• Reputation

• Integrating remote laboratories in MOOCs

• Evaluating MOOC content

• A Pairwise reputation mechanism for MOOCs

• Conclusions

IEEE EDUCON 2015, Tallinn

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


Reputation mechanisms in social networks


objective: extract reputation of entities (users, objects, …)

how: gathering and aggregating opinions

Examples:

Reputation mechanisms in social networks

Qualitative vs.Quantitative “Experts” vs. Users

Unbalanced source text oppinion vs. Numerical rating

Lack of accuracy numerical ratings


MOOC Example DIEEC UNEDIntegrating 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


Virtual Instrument Systems in Reality (VISIR)

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

Cuantitative vs. Comparative reviews



Easier for users to state opinions when thequery compare objects in a pairwise fashion

Ben-Hur 9

Casablanca 7.4

Gone with the wind 8.2



• Potential problems of numerical oppinion

– Passive wait for users to grade

– Influenciable and manipulable viral ratings

• Reputation based on comparative reviewsPairwise reputation mechanism (PWRM)


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 uploadedresource rj

LU = { <uk,rm> / uk € U; rm € R } user uk has usedresource rm


MOOC formalization

• Live envorinment in term of resources

• Objective build ranking of resources withinthe 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


MOOC formalization


Conclusions

• Reputation mechanism to be applied within MOOCs

• Massive implies accuracy, but resources are often notprovided by many users

• Clustering criteria Selection process

According to typology (Multimedia, video, audio, …)

According to metadata (Pedagogical objectives, granularity, etc. )


Towards learning resources rankings in MOOCs: A Pairwise based Reputation

Mechanism

R. Centeno, M. Rodríguez-Artacho, Félix García, Elio Sancristóbal, Gabriel Díaz, Manuel Castro

[email protected] University, Spain

Thanks!

mailto:[email protected]

IEEE EDUCON 2015 reputation mooc

Data & Analytics

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