Open AccessPrediction of Learner’s Performance in Adaptive E-Learning System using Learning Analytics
Author(s) -
Soni Sweta,
Shalini Mahato,
Laxmi Kumari Pathak
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1049/1/012006
Subject(s) - learning analytics , popularity , computer science , analytics , adaptation (eye) , educational data mining , learning management , relation (database) , business analytics , artificial intelligence , data science , machine learning , multimedia , psychology , data mining , social psychology , neuroscience , business model , marketing , business , business analysis
The main objective of Learning Analytics is to collect, interpret and investigate the information for setting proper co-relation to improve the students’ learning experiences. As the popularity and demand of learning analytics are on rise, higher education is continuously moving from offline to online E-Learning Educational System. It has been further promoted on account of COVID-19. Learning pedagogies, evaluation measures and feedback measures have also been changed accordingly. The aim of this paper is to discuss the efficacy of learning analytics in online educational system using e-learning platform. R programming language with GGPLOT2 is used as Visualization tool to focus on gain insights of adaptation of learning analytics. The results show the better computational performance in terms of predicting students to improve their learning achievements and mitigating risk of failures.