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Record W2767393219

Using Coh-Metrix to Access Cohesion and Difficulty in High-School Textbooks

2006· article· en· W2767393219 on OpenAlexaboutno aff
David F. Dufty, Erin J. Lightman, Philip M. McCarthy, Danielle S. McNamara

Bibliographic record

VenueeScholarship (California Digital Library) · 2006
Typearticle
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsnot available
Fundersnot available
KeywordsCohesion (chemistry)ComprehensionLinguisticsArgument (complex analysis)MemphisComputer sciencePsychologyPhilosophyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Using Coh-Metrix to Assess Cohesion and Difficulty in High-School Textbooks Philip M. McCarthy, Erin J. Lightman, David F. Dufty, and Danielle S. McNamara Department of Psychology Memphis. TN 38152 {pmccarthy, elightman, d.dufty, d.mcnamara} @mail.psyc.memphis.edu) Table 1). The results confirmed out hypothesis: Cohesion indices were higher for science texts than for history texts (LSA, F(1, 273) = 437.72, p < .01; argument overlap, F(1, 273) = 742.07, p<.01). The FKGL difficulty index showed no significant difference between genres. Across chapters, our results suggested science texts were less cohesive near the end of units, whereas history texts tended to be more cohesive (see Table 1). Our study suggests that Coh-Metrix can facilitate sophisticated analysis of texts, helping to establish benchmarks and typical patterns of textual cohesion and difficulty. With greater understanding of cohesion between genres and across textual units, Coh-Metrix stands to offer a broader assessment of text that may better facilitate assignments of text to readers. Recent research in text processing has emphasized the importance of the cohesion of a text in comprehension (e.g., McNamara, 2001). Cohesion is the degree to which ideas in the text are explicitly related to each other and facilitate a unified situation model for the reader. Such research has led to the development of a computational tool, Coh-Metrix, (Graesser et al., 2004) that delivers over 300 indices of textual cohesion and difficulty. We hypothesized that a Coh-Metrix analysis of texts would indicate that cohesion indices - more so than traditional, shallow difficulty indices such as Flesch-Kincaid Grade Level (FKGL, Klare, 1974-75) - would identify characteristics of texts. Specifically, we hypothesized that within the expository domain, science texts would demonstrate greater cohesion than history texts, as the former dealt with less familiar subjects and would be likely to employ greater redundancy. We further hypothesized that as the parts of a text (beginning, middle, and end) serve different rhetorical purposes, that the sophisticated indices of Coh-Metrix would identify these differences. To test our hypothesis, we sampled three representative 1000-word sections from the beginning, middle and end of each chapter of seven commonly used high-school text books (three from science and four from history). Each section was analyzed using Coh-Metrix indices of Cohesion (argument overlap, latent semantic analysis (LSA), and number of connectives) as well as FKGL to assess difficulty. Acknowledgements This research was supported by the Institute for Education Sciences (IES R3056020018-02). References Graesser, A.C., McNamara, D., Louwerse, M., & Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavioral Research Methods, Instruments, and Computers, 36, 193-202. Klare, G. R. (1974–1975). Assessing readability. Reading Research Quarterly, 10, 62-102. McNamara, D. S. (2001). Reading both high-coherence and low-coherence texts: Effects of text sequence and prior knowledge. Canadian Journal of Experimental Psychology, 55, 51-62. Results and Discussion We conducted an Analysis of Variance to assess differences between genres and across textual units (see Table 1. Results for Measures of Cohesion and Difficulty Science F-K LSA AO Con History Beginning Middle End Sig Beginning Middle End Notes: standard errors are in parentheses; * p<.05; ** p<.01 Sig

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.251
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2006
Admission routes1
Has abstractyes

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