Abstract—Digital education platforms like learning
management systems (LMS) have made the virtual
teaching-learning process very much handy. The LMS must
include additional features to track and review the learner’s
behavior in the teaching-learning process. This study aims to
identify the low achievers with the assessment marks which let
the course instructors understand the learner’s cognitive level
and enables the facilitators to recognize the student’s
perspective of the course based on their reviews collected from
the questionnaire. In the outcome, recommender systems are
incorporated with the learning analytics by using the K-Means
clustering algorithm. This algorithm has helped the facilitators
to segregate and identify the set of low achievers based on their
assessment scores and also to predict the appropriate reason
behind such slow performance. Apart from this, the results of
this study have also suggested that facilitators incorporate the
use of various emerging pedagogical methods in the
teaching-learning process to maximize the learner’s
performance and accentuate the level of virtual classrooms.
Index Terms—Learning management system, ICT tools, learing analytics, pedagogical models, clustering algorithms.
The authors are with the Computer Science and Engineering Department, VIT University, Chennai-600127, Tamil Nadu, India (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Monica Maiti and M. Priyaadharshini, "Recommender System for Low Achievers in Higher Education," International Journal of Information and Education Technology vol. 12, no. 12, pp. 1390-1398, 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).