Potential study SDGs 4 of deep learning approaches to improve problem solving with machine learning inovation: Empirical and bibliometric analysis

¹ Department of Physics Education, Universitas Negeri Surabaya, Indonesia
² Elementary School Teacher Education, Bhinneka University PGRI, Indonesia
³ Social and Behavioral Sciences, Tilburg University, Netherlands

^* Corresponding author: dwikoranto@unesa.ac.id

Abstract

Research with the aim of analyzing the potential of deep learning in improving problem solving in physics learning is a part of the implementation of point 4 of the SDGs was carried out using bibliometric analysis and supporting empirical data. Bibliometric analysis was carried out using Scopus basic data with an evaluation of the development of science using statistical data over the past 5 years. While empirical research data was obtained from the population of physics/science teachers and prospective teachers in East Java, Indonesia. The results obtained that the trend of physics learning research with a deep learning approach in educational articles over the past 5 years has continued to increase every year. A total of 579 data were obtained with a discussion of deep learning in problem solving to improve the quality of learning. The largest type of source in this study was journals with 435 publications and the largest type of document in this study was articles with 419 publications. The novelty of the study found that a deep learning approach could be used in physics learning through the application of machine learning to improve problem solving skills as a form of effort to implement SDGs point 4. It is recommended for future researchers to integrate deep learning in physics to improve problem solving according to the global trend of adapting innovative learning through digital media with the use of machine learning.