# Combining Human and Automated Scoring Methods in Experimental Assessments of Writing: A Case Study Tutorial file\_download **Size:** 833.74 KB **Format:** PDF **Date:** 01/12/2026 [ Download Resource file\_download ](/sites/g/files/omnuum9881/files/2026-01/mozer-et-al-2023-combining-human-and-automated-scoring-methods-in-experimental-assessments-of-writing-a-case-study.pdf) This article presents a practical, research-based framework for combining human scoring with automated text analysis to evaluate writing outcomes in randomized controlled trials. Using a large-scale content literacy intervention as a case study, the authors demonstrate how machine learning and natural language processing tools can augment traditional human-coded assessments of student writing by generating a rich set of additional text-based outcomes. These automated measures—ranging from vocabulary use and discourse structure to psychological and linguistic features—help unpack how and why an intervention improved students’ argumentative writing, beyond what a single holistic score can reveal. The study shows that automated methods can efficiently identify treatment-driven changes in writing style, vocabulary, and reasoning, while also cautioning that machine-generated scores should supplement, not replace, human judgment. By offering a clear analytic workflow, open-source tools, and concrete examples, the article provides a scalable template for researchers seeking to deepen causal interpretations of writing outcomes in education research. See also:- [ Research and Reports ](/resource-type/research-report) - [ Researcher ](/audience/researcher) - [ School/District Leader ](/audience/schooldistrict-leader) - [ Curriculum ](/focus-areas/curriculum) - [ Reading Instruction ](/focus-areas/reading-instruction) - [ School Improvement & Redesign ](/focus-areas/school-improvement-and-redesign) - [ MORE (Model of Reading Engagement)/READS Lab ](/projects/more-model-reading-engagement) - [ 2024 ](/year/2024) - [ K12 ](/sector/k12)