MétaCan
Menu
Back to cohort
Record W3133682786 · doi:10.1016/j.lindif.2021.101985

Assessing reading and online research comprehension: Do difficulties in attention and executive function matter?

2021· article· en· W3133682786 on OpenAlexfundno aff
Laura Kanniainen, Carita Kiili, Asko Tolvanen, Mikko Aro, Øistein Anmarkrud, Paavo H. T. Leppänen

Bibliographic record

VenueLearning and Individual Differences · 2021
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersAcademy of FinlandMcMaster University
KeywordsPsychologyFluencyComprehensionReading comprehensionTask (project management)Cognitive psychologyReading (process)Structural equation modelingVerbal fluency testFunction (biology)Executive functionsDevelopmental psychologyCognitionMathematics educationComputer scienceLinguisticsNeuropsychology

Abstract

fetched live from OpenAlex

This study evaluated the relation between sixth graders' (N = 426) teacher-rated difficulties in attention and executive function (EF) and their comprehension skills. Reading comprehension was assessed with a multiple-choice task and online research and comprehension (ORC) with a problem-solving task. The analyses were controlled for gender, reading fluency and nonverbal reasoning. To investigate differences in students' performance between the tasks, comprehension skills in the multiple-choice task were also controlled for in the ORC task. Structural equation models showed that teacher-rated attention and EF difficulties were related to students' performance more in the problem-solving task than in the multiple-choice task. After controlling for all the background variables, these difficulties explained 9% of the variance of ORC performance in girls and 4% in boys. These results indicate that for students with attention and EF difficulties the ORC task was more challenging than the reading comprehension task.

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)0.011
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.387
Teacher spread0.284 · 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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueLearning and Individual DifferencesSame topicReading and Literacy DevelopmentFrench-language works237,207