Home
World Journal of Advanced Research and Reviews
International Journal with High Impact Factor for fast publication of Research and Review articles

Main navigation

  • Home
    • Journal Information
    • Editorial Board Members
    • Reviewer Panel
    • Abstracting and Indexing
    • Journal Policies
    • Our CrossMark Policy
    • Publication Ethics
    • Issue in Progress
    • Current Issue
    • Past Issues
    • Instructions for Authors
    • Article processing fee
    • Track Manuscript Status
    • Get Publication Certificate
    • Join Editorial Board
    • Join Reviewer Panel
  • Contact us
  • Downloads

eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in April 2026 (Volume 30, Issue 1) Submit manuscript

The development of RBL - STEM learning materials to improve students' computational thinking skills in solving convolutional neural network problems

Breadcrumb

  • Home
  • The development of RBL - STEM learning materials to improve students' computational thinking skills in solving convolutional neural network problems

Muharromah M D 1,  *, Kristiana A I 1, Slamin 2 and Dafik 1

1 Department of Postgraduate Mathematics Education, University of Jember, Indonesia.
2 Department of Computer Science, University of Jember, Indonesia.
 
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(01), 2373-2381
Article DOI: 10.30574/wjarr.2024.21.1.0219
DOI url: https://doi.org/10.30574/wjarr.2024.21.1.0219
 
Received on 07 November 2023; revised on 22 January 2024; accepted on 24 January 2024
 
In 21st century learning, computational thinking skills have become one of the essential competencies that need to be emphasised in the development of knowledge. To enhance computational thinking skills, research-based learning (RBL) with a science, technology, engineering and mathematics (STEM) approach, known as RBL-STEM, can be used. This study aims to explore RBL-STEM activities, describe the process and outcomes of developing RBL-STEM materials, and analyse data. In this research, the RBL-STEM framework is used to improve students' computational thinking skills in applying Convolutional Neural Network (CNN) to identify coffee plant diseases using a quadcopter drone and its flight path with resolving dominating set. The research method used is Research and Development (R&D). This research develops RBL-STEM materials and produces learning material products in the form of semester study plans, student assignment designs, student worksheets and learning outcome tests. The results of the development of the materials show validity with a validity criterion of 92%. Implementation using RBL-STEM materials was found to be practical with a practicality criterion of 96.25% and effective with an effectiveness criterion of 94.32%. In addition, students were highly engaged and provided very positive feedback on the learning experience. Pre-test and post-test analysis showed an improvement in students' computational thinking skills when solving CNN problems.
 
Computational Thinking Skills; Research Based-Learning (RBL); Science, Technology, Engineering and Mathematics (STEM); Convolutional Neural Network (CNN)
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-0219.pdf

Preview Article PDF

Muharromah M D, Kristiana A I, Slamin and Dafik. The development of RBL - STEM learning materials to improve students' computational thinking skills in solving convolutional neural network problems. World Journal of Advanced Research and Reviews, 2024, 21(1), 2373-2381. Article DOI: https://doi.org/10.30574/wjarr.2024.21.1.0219

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.

All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

Get Certificates

Get Publication Certificate

Download LoA

Check Corssref DOI details

Issue details

Issue Cover Page

Editorial Board

Table of content

Copyright © 2026 World Journal of Advanced Research and Reviews - All rights reserved

Developed & Designed by VS Infosolution