MétaCan
Menu
Back to cohort
Record W2767393219

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

2006· article· en· W2767393219 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0040.008
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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