Machine learning for automated content analysis: characteristics of training data impact reliability


Conference paper


Rebeckah Fussell, Ali Mazrui, N. G. Holmes
2022 Physics Education Research Conference Proceedings, American Association of Physics Teachers, Grand Rapids, MI, 2022 Sep, pp. 194--199


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APA   Click to copy
Fussell, R., Mazrui, A., & Holmes, N. G. (2022). Machine learning for automated content analysis: characteristics of training data impact reliability. In 2022 Physics Education Research Conference Proceedings (pp. 194–199). Grand Rapids, MI: American Association of Physics Teachers. https://doi.org/10.1119/perc.2022.pr.Fussell


Chicago/Turabian   Click to copy
Fussell, Rebeckah, Ali Mazrui, and N. G. Holmes. “Machine Learning for Automated Content Analysis: Characteristics of Training Data Impact Reliability.” In 2022 Physics Education Research Conference Proceedings, 194–199. Grand Rapids, MI: American Association of Physics Teachers, 2022.


MLA   Click to copy
Fussell, Rebeckah, et al. “Machine Learning for Automated Content Analysis: Characteristics of Training Data Impact Reliability.” 2022 Physics Education Research Conference Proceedings, American Association of Physics Teachers, 2022, pp. 194–99, doi:10.1119/perc.2022.pr.Fussell.


BibTeX   Click to copy

@inproceedings{fussell2022a,
  title = {Machine learning for automated content analysis: characteristics of training data impact reliability},
  year = {2022},
  month = sep,
  address = {Grand Rapids, MI},
  pages = {194--199},
  publisher = {American Association of Physics Teachers},
  doi = {10.1119/perc.2022.pr.Fussell},
  author = {Fussell, Rebeckah and Mazrui, Ali and Holmes, N. G.},
  booktitle = {2022 Physics Education Research Conference Proceedings},
  month_numeric = {9}
}


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