Surface Feature Analysis

Surface Feature Analysis is a computational method in Automated Essay Scoring that identifies and quantifies superficial linguistic characteristics—such as word count, sentence length, and lexical diversity—to assess writing quality without interpreting semantic or rhetorical content.

104

Publications

4.4K

Citations

365

Authors

164

Institutions

Publications per year

2017–2026

14

Authors

365

  1. LL
    Lorinda L. Cherry

    Nokia (United States), United States

    3 publications · 80 citations

    3

    h-index

  2. RC
    Richard Catrambone

    Georgia Institute of Technology, United States

    2 publications · 108 citations

    2

    h-index

  3. JA
    James A Maslanik

    University of Colorado Boulder, United States

    2 publications · 56 citations

    2

    h-index

  4. MA
    M. A. Tschudi

    University of Colorado Boulder, United States

    2 publications · 56 citations

    2

    h-index

  5. FC
    Fuhua Cheng

    Villanova University, United States

    2 publications · 36 citations

    2

    h-index

1–5 of 365

Venues

  1. Computer-Aided Design and Applications

    15 publications · 290 citations

    12

    h-index

  2. Measurement Science and Technology

    3 publications · 102 citations

    3

    h-index

  3. Annals of Glaciology

    3 publications · 85 citations

    3

    h-index

  4. IEEE Transactions on Communications

    2 publications · 57 citations

    2

    h-index

  5. 2

    h-index

1–5