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<p>This study evaluates MI Write, an automated writing evaluation (AWE) system, as a benchmark for tracking middle-school students’ writing growth within multi-tiered systems of support. Multilevel growth models were applied to 3,299 students (<i>n</i> = 464 English learners [ELs]) in Grades 6–8 across fall, winter, and spring benchmark writing prompts, estimating change in the MI Write Total Score, six analytic traits, and 81 NLP-derived features. Analyses compared whether growth differed according to EL-status measured as either a binary variable or as continuous measure of English language proficiency. Students gained an average of 1.6 points in overall quality across the school year, a standardized gain of <i>d</i> = 0.32. Non-ELs improved steadily, whereas ELs showed little growth between fall and winter but a steeper rise from winter to spring; total annual gains were equivalent, and English-proficiency did not predict growth. All traits improved, with ELs advancing slightly faster in development and sentence fluency. Feature analysis revealed ELs growing more in foundational skills (sentence complexity, spelling), while non-ELs progressed faster in advanced structures (subordination, sophisticated verbs, rare vocabulary). These distinct linguistic pathways produced similar overall gains, underscoring different instructional needs. Findings support MI Write’s viability as a formative benchmark that can inform data-driven decisions for diverse learners.</p>

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Writing growth among non-English learners and English learners in grades 6–8 using automated writing evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月12日

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  • Joshua Wilson,
  • Corey Palermo,
  • Arianto Wibowo,
  • Fan Zhang,
  • Adiba Nusrat

摘要

This study evaluates MI Write, an automated writing evaluation (AWE) system, as a benchmark for tracking middle-school students’ writing growth within multi-tiered systems of support. Multilevel growth models were applied to 3,299 students (n = 464 English learners [ELs]) in Grades 6–8 across fall, winter, and spring benchmark writing prompts, estimating change in the MI Write Total Score, six analytic traits, and 81 NLP-derived features. Analyses compared whether growth differed according to EL-status measured as either a binary variable or as continuous measure of English language proficiency. Students gained an average of 1.6 points in overall quality across the school year, a standardized gain of d = 0.32. Non-ELs improved steadily, whereas ELs showed little growth between fall and winter but a steeper rise from winter to spring; total annual gains were equivalent, and English-proficiency did not predict growth. All traits improved, with ELs advancing slightly faster in development and sentence fluency. Feature analysis revealed ELs growing more in foundational skills (sentence complexity, spelling), while non-ELs progressed faster in advanced structures (subordination, sophisticated verbs, rare vocabulary). These distinct linguistic pathways produced similar overall gains, underscoring different instructional needs. Findings support MI Write’s viability as a formative benchmark that can inform data-driven decisions for diverse learners.