The Impact of Gender and Reading Level on: Student Perception, Academic Practice, and Student Enjoyment
Bibliographic record
Abstract
Much research has been conducted on reading levels of elementary school students. Teachers search for learning experiences that lend inclusion to all genders and levels. How does this all lay out for the students? The initial trial of the study looks into the impact and differences of gender and/or reading level on areas such as school enjoyment, self-perception, and academic practice in the lives of 59, fifth grade, suburban students. A subsequent study was done with identical goals in the following school year as well. This newer study (trial two) included 103 students from grades two through five in the same suburban area school. These students were given surveys that allowed scores to be ascertained in the aforementioned areas of concern. Surveys were given out and scored on a Likert-type scale ranging from 1-5 (1=most negative, 5=most positive). The data was entered into SPSS where various tests such as MANOVA, correlations, Levene’s, and t-tests were performed. While a great deal of the areas were not initially identified as statistically significant in trial one, the area of gender and its relationship with enjoyment did appear to be significant at the p<.05 level. The larger, more recent trial, found gender’s impact on school enjoyment, self-perception, and academic practice time. This study could give educators introspective into the way their students think about school.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".