A Comparative Study of Statistical Learning and Adaptive Learning

TL;DR

This paper compares Statistical Learning and Adaptive Learning in e-learning contexts, concluding that Adaptive Learning is more efficient based on various characteristic parameters.

Contribution

It provides a comparative analysis of Statistical and Adaptive Learning methods, highlighting the superior efficiency of Adaptive Learning.

Findings

Adaptive learning outperforms statistical learning in efficiency

The study identifies key parameters influencing learning effectiveness

Adaptive learning better personalizes the learning experience

Abstract

Numerous strategies have been adopted in order to make the process of learning simple, efficient and within less amount of time.. Classroom learning is slowly replaced by E-learning and M- learning. These techniques involve the usage of computers, smart phones and tablets for the process of learning. Learning from the internet has become popular among the e-learners where learner tends to rely greatly upon information provided by the World Wide Web. However, the e-learners have to go through a huge volume of data produced by the first tier search engine, some of which are not suited to the interest of the user. Various strategies, namely Statistical Learning and Adaptive Learning, have been adopted to cater to the need of the user and produce data best suited to the interest of the user. The authors have tried to present a comparative study of Statistical Learning and Adaptive Learning based on certain parameters, which arise from the characteristics of the learning process. As a consequence of the comparative study, it has been concluded that Adaptive learning is more efficient than Statistical learning.