Vztah rychlého odpovídání v testu z anglického jazyka a výsledného skóre u různých skupin žáků
Bibliographic record
Abstract
This study focuses on the effort of students when answering achievement test questions and its relationship with their final test score. The aim of the study was to determine the extent to which insufficient effort responding in tests occurs among different groups of students based on gender and school type, and how the varying degrees of insufficient effort among these groups may be reflected in their comparison based on the final test score. The research sample consisted of male and female students from the 9th grade of basic school (N = 631) and the corresponding grades of multi-year secondary general schools (N = 713). As a research tool, we employed an online questionnaire application, which included an English placement test (Cambridge EFL Placement Test) with 25 multiple-choice questions. To identify insufficient effort, we employ time characteristics of students’ behavior during testing, recorded by an online testing application developed for this purpose. The results of the study indicate that insufficient effort is a marginal phenomenon among students of multi-year secondary general schools (3.1%), while affecting almost a quarter of basic school students (23.6%). In both school types, boys exhibit insufficient effort approximately twice as much as girls. Analysis of test results suggests that lower scores of boys compared to girls may be largely explained by lower effort of boys when completing the test. Our findings suggest that insufficient effort responding in tests may have a significant impact on conclusions of studies that compare different groups of students based on test results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".