article
Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study
Autor
Corbi, Alberto
Burgos, Daniel
Institución
Resumen
Recommender systems require input information in
order to properly operate and deliver content or behaviour
suggestions to end users. eLearning scenarios are no exception.
Users are current students and recommendations can be built
upon paths (both formal and informal), relationships, behaviours,
friends, followers, actions, grades, tutor interaction, etc. A
recommender system must somehow retrieve, categorize and
work with all these details. There are several ways to do so: from
raw and inelegant database access to more curated web APIs or
even via HTML scrapping. New server-centric user-action
logging and monitoring standard technologies have been
presented in past years by several groups, organizations and
standard bodies. The Experience API (xAPI), detailed in this
article, is one of these. In the first part of this paper we analyse
current learner-monitoring techniques as an initialization phase
for eLearning recommender systems. We next review
standardization efforts in this area; finally, we focus on xAPI and
the potential interaction with the LIME model, which will be also
summarized below.