A Systematic Mapping Review on MOOC Recommender Systems

Abstract

Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

“…In general, technology trends in MOOC recommender systems are heading towards the employment of ML algorithms [11]. Approaches applied to the learning context with a perspective of considering a wide set of variables for recommendation are Bayesian iJEP -Vol.…”

“…In educational data mining, Machine Learning (ML) algorithms like association rules and decision trees are not only used for course recommendation but are also used for MOOC dropout and demotivation prediction [10]. In a recent Systematic Literature Review of [11], twenty-seven research papers relied on ML techniques among 116 research works analyzed by [11] on MOOC recommender systems developed from 2013 to 2021.…”

“…However, new methods of a machine acquiring knowledge include reinforcement learning and transfer learning. This induced a large sphere of selected ML algorithms by researchers for MOOC recommendations such as k-means, K-Neighrest Neighbor, Apriori association algorithm, and decision tree [11].…”

“…Considering the related work identified by [11] as mentioned above and another systematic literature review of [12] on adaptive content recommenders in personalized learning environments from 2015 to 2020, the prediction model that we suggest in this paper is an ML-based prediction model for a MOOC personalized recommender. Indeed, the MOOC ML-based recommender "should decide two strategies: learner/learning object model and the recommendation technique" [12].…”

Mining sequential patterns with flexible constraints from MOOC data

“…Using SPs to recommend MOOC teaching materials is a promising approach [ 34 ]. Taking a student's sequence of past courses, Wang and Zaïane [ 36 ] implemented a course recommender system based on three sequence-related approaches, including SPM.…”