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Record W4242858377 · doi:10.24124/2011/bpgub1488

Exploring the relationship between reading comprehension and math word problem test achievement

2011· dissertation· en· W4242858377 on OpenAlexaffabout
Amy L. Lovell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsUniversity of AlbertaUniversity of Northern British Columbia
Fundersnot available
KeywordsReadabilityTest (biology)Reading comprehensionReading (process)Mathematics educationComprehensionPsychologyAchievement testWord (group theory)LinguisticsStandardized test

Abstract

fetched live from OpenAlex

This professional inquiry explores the relationships between students' reading comprehension and their performance on a grade six math word problem test, and it evaluates the readability of the wording for word problem test items. Students' results on the 2008 Alberta Provincial Achievement Test for Part B, Word Problems, were compared to their reading levels on the Canadian Achievement Test and the Gates MacGinitie Reading Tests, to evaluate the correlation between reading comprehension and word problem performance. The researcher calculated the readability of test questions and invited students to comment on the difficulty of the wording for each question. This investigation revealed a strong positive correlation between the students' levels of reading comprehension and their scores on the math Provincial Achievement Test Part B. Analysis of scores for individual questions on the math test revealed some surprising anomalies that deserve investigation in a later study. The author shares insights that she will apply to her own teaching to assist students to improve their reading comprehension abilities and their math word problem success. She also provides advice for test construction and recommends further investigation of this research question with a larger sample size. --P. ii.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.278
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.350
GPT teacher head0.412
Teacher spread0.063 · 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 teacher head, 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".

Quick stats

Citations1
Published2011
Admission routes2
Has abstractyes

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