"This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000–2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into seven clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years."
Drachsler H., Verbert K., Santos O.C., Manouselis N. (2015) Panorama of Recommender Systems to Support Learning. In: Ricci F., Rokach L., Shapira B. (eds) Recommender Systems Handbook. Springer, Boston, MA
1. SUMMARY
The paper is a survey of 82 recommender systems (RecSys) for Technology Enhanced Learning (TEL), from 2000 to 2014, based on empirical data rather than conceptual drafts. It is important because of: the rapid increase of digital educational materials, the need for standardization of RecSys for TEL, and other reasons.
The paper uses 1)Specific Supported Tasks, (2)Approach (user model, domain model, personlization), and (3)Operation model (architecture, location, mode) to classify RecSys into 7 clusters of: “1. TEL RecSys following collaborative filtering approaches as in other domains 2. TEL RecSys that propose improvements to collaborative filtering approaches to take into account the particularities of the TEL domain 3. TEL RecSys that consider explicitly educational constraints as a source of information for the recommendation process 4. TEL RecSys that explore other alternatives to collaborative filtering approaches 5. TEL RecSys that consider contextual information within TEL scenarios to improve the recommendation process 6. TEL RecSys that assess the educational impact of the recommendations delivered 7. TEL RecSys that focus on recommending courses (instead of resources within them)”
The paper concluded by a short list of trends, current challenges and future research directions.
Identified trends are: (1) The most applied task for RecSys is Finding good Items (2) There are increasing efforts toward clustering and classification methods (3) There are increasing interest in Graph-based and Knowledge-based approach for personalizations (4) Passive mode was used by most RecSys for making recommendations
2. STRENGTHS
The paper provides quite an impressive list of RecSys for TEL and a live research group with new papers being added. The paper also went beyond listing of papers and provided lists of trends, current challenges and potential research directions.
3. WEAKNESSES
More charts could be used to demonstrate trends or tables with check boxes for readers to keep a good big picture regarding the 82 papers. However, it is important to note that the paper was under a page limit.
