Submissions

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Author Guidelines

Authors are invited to make a submission to this journal. All submissions will be assessed by an editor to determine whether they meet the aims and scope of this journal. Those considered to be a good fit will be sent for peer review before determining whether they will be accepted or rejected.
Before submitting, authors are responsible for obtaining permission to publish any material included with the submission, such as photos, documents, and datasets. All authors identified on the submission must consent to be identified as an author. Where appropriate, research should be approved by an appropriate ethics committee in accordance with the legal requirements of the study's country.
An editor may desk reject a submission if it does not meet minimum standards of quality. Before submitting, please ensure that the study design and research argument are structured and articulated properly. The title should be concise, and the abstract should be able to stand on its own. This will increase the likelihood of reviewers agreeing to review the paper. When you're satisfied that your submission meets this standard, please follow the checklist below to prepare your submission.

Submission Preparation Checklist

All submissions must meet the following requirements.

  • This submission meets the requirements outlined in the Author Guidelines.
  • This submission has not been previously published, nor is it before another journal for consideration.
  • All references have been checked for accuracy and completeness.
  • All tables and figures have been numbered and labeled.
  • Permission has been obtained to publish all photos, datasets, and other material provided with this submission.

Articles - Physics Education

This section publishes research on physics teaching and learning across educational levels, including instructional design and implementation, inquiry- and laboratory-based learning, problem-solving and modeling approaches, and contextual learning; it also includes studies on cognition in physics education (conceptual understanding, misconceptions, multiple representations, critical thinking, metacognition, and creativity), assessment and evaluation (instrument development/validation and measurement models for evidence-based decision-making), technology-enhanced learning (simulations, virtual labs, computational modeling, data analysis tools, AI-supported learning and assessment, and digital sensors), and physics teachers and professional development, alongside evidence-based synthesis papers (systematic reviews, bibliometric analyses, and meta-analyses) on issues and trends such as AI integration, higher-order thinking, laboratory transformation to virtual/hybrid formats, modeling and data-driven learning, science literacy, sustainability, and equity.

Person in Charge (PIC): Joko Saefan; Joko Siswanto; Hadi Ngasbey; Nur Khoiri.

Article - Physics

This section publishes original research across fundamental, applied, and computational physics, including theoretical and mathematical developments, experimental and instrumentation studies, and numerical modeling or simulation, covering areas such as mechanics, electromagnetism, thermodynamics, statistical physics, waves and optics, modern physics, nuclear and particle physics, astrophysics and cosmology, as well as materials and solid-state physics, medical physics, geophysics, environmental physics, energy physics, and industrial physics; it also welcomes evidence-based synthesis papers (systematic literature reviews, bibliometric analyses, and meta-analyses) that map current issues and emerging trends in physics, including the growing role of advanced computation, artificial intelligence, big data, sustainability, opportunities, and challenges.

Person in Charge (PIC): Joko Saefan; Wawan Kurniawan; Wahyu Hardyanto; Tetty Novalina Manik.

Special Issues: Technology Use in Physics Education

This section is dedicated to submissions for the Special Issue “Technology Use in Physics Education.” Manuscripts must align with the Special Issue scope and follow the journal’s Author Guidelines. All submissions undergo the journal’s standard peer-review process. Please select this section during submission to ensure correct routing. Submissions are accepted until 31 December 2026, as stated in the Special Issue announcement.

Special Issue: Machine Learning for Physics: PINNs, CNNs, and Beyond

This section is dedicated to submissions for the Special Issue “Machine Learning for Physics: PINNs, CNNs, and Beyond.” Manuscripts must align with the Special Issue scope and follow the journal’s Author Guidelines. All submissions undergo the journal’s standard peer-review process. Please select this section during submission to ensure correct routing. Submissions are accepted until 28 February 2027, as stated in the Special Issue announcement.