Review of current student-monitoring techniques used in elearning-focused recommender systems and learning analytics. The Experience API & LIME model case study

Corbi, A., & Burgos, D. (2014). Review of current student-monitoring techniques used in elearning-focused recommender systems and learning analytics. The Experience API & LIME model case study. International Journal of Artificial Intelligence and Interactive Multimedia, 2(7), 44-52.

Abstract

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.

Link: http://www.ijimai.org/journal/sites/default/files/files/2014/09/ijimai20142_7_6_pdf_27449.pdf