High school students’ attentional stance, modes of reading engagement, and self-insight during literary reading
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".