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Record W4386328079 · doi:10.21248/l1esll.2023.23.1.485

High school students’ attentional stance, modes of reading engagement, and self-insight during literary reading

2023· article· en· W4386328079 on OpenAlexfundno aff
Peter Grandits, Janez Krek

Bibliographic record

VenueL1 Educational Studies in Language and Literature · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsStructural equation modelingReading (process)PsychologyConfirmatory factor analysisReliability (semiconductor)Test (biology)Experiential learningReading motivationCognitive psychologyDevelopmental psychologyMathematics educationComputer scienceLinguistics

Abstract

fetched live from OpenAlex

The primary aim of this study was to analyze the validity and reliability of an instrument capable of measuring high school students’ attentional stance, modes of reading engagement, and self-insight during literary reading. For this purpose, a self-report questionnaire was administered to high school students in three Austrian regions (N = 417). First, confirmatory factor analysis was conducted to test the validity and the reliability of the preconceived measurement model. Second, the interrelationships among the validated constructs were analyzed through structural equation modeling. The fit and the validity of the structural model were evaluated, and the mediating effect of expressive reading was tested. The study yielded an instrument with valid and reliable scores that assesses 9 dimensions of high school students’ reading experiences. The basic Kuiken-Douglas model (2017) on reading engagement and reading outcome could be replicated. Structural equation modeling indicated that high attentional focus negatively predicted expressive-experiential reading that in turn facilitated self-insight. This implies that students should be allowed leaky attention so that they can work with literary texts in a self-modifying way in literature education. Limitations are discussed.

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 score0.248
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.362
Teacher spread0.344 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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