To Enhance the Pedagogy of Personalized Learning by Utilizing Multi-Robot Systems (MRS)

Sanjida Ahmed , Prof. Dr. Md. Fokhray Hossain

multi-robot system (MRS), personalized learning, improved student engagement, interactive learning, educational robot, adaptive learning, classroom robotics, human-robot interaction, collaborative learning, artificial intelligence in education

Personalized learning has been a significant educational approach since the early 1960s, aiming to tailor learning experiences based on individual student needs. There is potential to further enhance personalized learning in large classrooms with advanced technologies such as multi-robot systems (MRS). This study proposes to explore the implementation of MRS in educational sectors to improve student engagement, motivation, and learning outcomes. The proposed research design includes surveys and interviews with the educator and the student, followed by a proposed solution. This research carries on qualitative and quantitative data analysis & their respective results are involved in the proposed methodology. The anticipated impact is a significant improvement in learning experiences and outcomes for the students, including those with additional learning needs.

Published Paper   E-Certificate

"To Enhance the Pedagogy of Personalized Learning by Utilizing Multi-Robot Systems (MRS)", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 11, page no.345 - 353, November-2024, Available :https://ijsdr.org/papers/IJSDR2411045.pdf

Volume 9 Issue 11, November-2024

Pages : 345 - 353

Paper Reg. ID: IJSDR_212552

Published Paper Id: IJSDR2411045

Downloads: 000346999

Research Area: Science & Technology

Country: Dhaka, Dhaka, Bangladesh

Published Paper PDF: https://ijsdr.org/papers/IJSDR2411045

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2411045

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition