Citation: M. R. M., V.; Rodriguez, C.;
Navarro Depaz, C.; Concha, U.R.;
Pandey, B.; S. Kharat, R.;
Marappan, R. Machine Learning
Based Recommendation System for
Web-Search Learning. Telecom 2023, 4,
118–134. https://doi.org/10.3390/
telecom4010008
Academic Editors:
Alexandros-Apostolos Boulogeorgos,
Thomas Lagkas,
Panagiotis Sarigiannidis,
Vasileios Argyriou and
Pantelis Angelidis
Received: 27 December 2022
Revised: 19 January 2023
Accepted: 20 January 2023
Published: 01 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Machine Learning Based Recommendation System for
Web-Search Learning
Veeramanickam M. R. M.
1,2
, Ciro Rodriguez
3
, Carlos Navarro Depaz
4
, Ulises Roman Concha
4
,
Bishwajeet Pandey
5
, Reena S. Kharat
6
and Raja Marappan
7,
*
1
Centre of Excellence for Cyber Security Technologies, Chitkara University Institute of Engineering and Technology,
Chitkara University, Punjab 140401, India
2
Postdoctoral Scholar, Department of Software Engineering, Universidad Nacional Mayor de San Marcos,
Lima 15081, Peru
3
Department of Software Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru
4
Facultad de Ingenieria de Sistemas e Informatica, Universidad Nacional Mayor de San Marcos,
Lima 15081, Peru
5
Research Consultant, Gyancity Research Consultancy, Motihari. Associate Professor, Department of CSE,
Jain University Bangalore, Karnataka 560069, India
6
Associate Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering,
Pune 411044, India
7
Senior Assistant Professor, School of Computing, Shanmugha Arts Science Technology and Research Academy,
SASTRA Deemed University, Thanjavur 613401, India
* Correspondence: raja_csmath@cse.sastra.edu or professor.m.raja@gmail.com
Abstract:
Nowadays, e-learning and web-based learning are the most integrated new learning
methods in schools, colleges, and higher educational institutions. The recent web-search-based
learning methodological approach has helped online users (learners) to search for the required topics
from the available online resources. The learners extracted knowledge from textual, video, and
image formats through web searching. This research analyzes the learner’s significant attention to
searching for the required information online and develops a new recommendation system using
machine learning (ML) to perform the web searching. The learner’s navigation and eye movements
are recorded using sensors. The proposed model automatically analyzes the learners’ interests while
performing online searches and the origin of the acquired and learned information. The ML model
maps the text and video contents and obtains a better recommendation. The proposed model analyzes
and tracks online resource usage and comprises the following steps: information logging, information
processing, and word mapping operations. The learner’s knowledge of the captured online resources
using the sensors is analyzed to enhance the response time, selectivity, and sensitivity. On average,
the learners spent more hours accessing the video and the textual information and fewer hours
accessing the images. The percentage of participants addressing the two different subject quizzes,
Q1 and Q2, increased when the learners attempted the quiz after the web search; 43.67% of the
learners addressed the quiz Q1 before completing the web search, and 75.92% addressed the quiz Q2
after the web search. The average word counts analysis corresponding to text, videos, overlapping
text or video, and comprehensive resources indicates that the proposed model can also apply for a
continuous multi sessions online search learning environment. The experimental analysis indicates
that better measures are obtained for the proposed recommender using sensors and ML compared
with other methods in terms of recall, ranking score, and precision. The proposed model achieves a
precision of 27% when the recommendation size becomes 100. The root mean square error (RMSE)
lies between 8% and 16% when the number of learners < 500, and the maximum value of RMSE
is 21% when the number of learners reaches 1500. The proposed recommendation model achieves
better results than the state-of-the-art methods.
Keywords:
online searching; online learning; eye tracking; online resources; recommender system;
soft computing
Telecom 2023, 4, 118–134. https://doi.org/10.3390/telecom4010008 https://www.mdpi.com/journal/telecom
