TY - JOUR
T1 - A systematic review
T2 - Machine learning based recommendation systems for e-learning
AU - Khanal, Shristi Shakya
AU - Prasad, P. W.C.
AU - Alsadoon, Abeer
AU - Maag, Angelika
PY - 2020
Y1 - 2020
N2 - The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.
AB - The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.
KW - Collaborative filtering
KW - Content-based
KW - E-learning
KW - Hybrid
KW - Recommendation system
KW - Recommender
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U2 - 10.1007/s10639-019-10063-9
DO - 10.1007/s10639-019-10063-9
M3 - Article
AN - SCOPUS:85076850448
SN - 1360-2357
VL - 25
SP - 2635
EP - 2664
JO - Education and Information Technologies
JF - Education and Information Technologies
ER -