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Automated scoring of morphological complexity (MC) in student writing is a valid alternative to manual analysis. This tool accurately measures writing complexity and correlates with teacher ratings of quality.

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Area of Science:

  • Linguistics
  • Educational Psychology
  • Computational Linguistics

Background:

  • Assessing written language complexity is crucial for understanding student development.
  • Manual analysis of morphological complexity (MC) is time-consuming and labor-intensive.
  • Automated tools offer a potential solution for efficient and objective analysis.

Purpose of the Study:

  • To validate an automated scoring procedure for calculating morphological complexity (MC) from written transcripts.
  • To assess the utility of an open-access tool, Morpholex, for measuring MC.
  • To compare automated MC measures with traditional hand-coding and teacher ratings of writing quality.

Main Methods:

  • 146 written responses from fifth-grade students were analyzed.
  • Morphological complexity (MC) was assessed using both hand-coding by trained scorers and the automated Morpholex tool.
  • Correlational analyses examined the relationship between hand-coded and automated MC measures, and their predictive validity for writing quality.

Main Results:

  • Automated MC measures showed a strong correlation (r = .63) with hand-coded derivational morpheme counts.
  • Both derivational and inflectional morphemes significantly predicted teachers' overall ratings of writing quality.
  • The Morpholex tool demonstrated strong agreement with manual assessment methods.

Conclusions:

  • Automated scoring of MC is a valid and potentially valuable alternative to manual analysis.
  • This automated approach can aid in monitoring student writing growth and assessing factors influencing perceived academic writing quality.
  • The Morpholex tool offers a practical solution for researchers and educators evaluating written language complexity.