Data-driven Science Instruction

Data-Driven Science Instruction is a systematic approach to science teaching and learning that belongs to the category of instructional methodologies and is distinguished by its reliance on empirical data—such as student performance, formative assessments, and observational metrics—to inform, refine, and individualize pedagogical strategies, curriculum design, and instructional decision-making in science education.

105

Publications

4.2K

Citations

390

Authors

189

Institutions

Publications per year

2017–2026

62

Authors

390

  1. EH
    Elizabeth H. Schultheis

    Michigan State University, United States

    4 publications · 134 citations

    4

    h-index

  2. MK
    Melissa K. Kjelvik

    Michigan State University, United States

    4 publications · 134 citations

    4

    h-index

  3. SG
    Simon Gladman

    The University of Melbourne, Australia

    3 publications · 338 citations

    3

    h-index

  4. CR
    Carlos Rodríguez Monroy

    Universidad Politécnica de Madrid, Spain

    3 publications · 39 citations

    3

    h-index

  5. HH
    Hans-Rudolf Hotz

    Friedrich Miescher Institute, Switzerland

    3 publications · 338 citations

    3

    h-index

1–5 of 390

Venues

  1. Journal of Research in Science Teaching

    JRST · 4 publications · 125 citations

    4

    h-index

  2. Harvard Data Science Review

    4 publications · 128 citations

    4

    h-index

  3. PLoS Computational Biology

    3 publications · 154 citations

    3

    h-index

  4. Hydrology and earth system sciences

    2 publications · 78 citations

    2

    h-index

  5. Research Papers in Education

    2 publications · 69 citations

    2

    h-index

1–5