The Effect of Gender and Teaching Methods on Academic Success in Virtual Reality to Reduce Gender Disparity in Technology
Bibliographic record
Abstract
This study adopted a pre-test post-test quasi-experimental design. The sample size was 162 students from eight universities. The sample was categorized into two groups: Group I (n=79) and Group II (n=83). Electric VLab, provided the environment. A researcher-made achievement test, comprising multiple-choice, essay and practical questions was used for assessment and data collection. Two weeks before the treatment, students in both groups were given a pre-test in electronics circuit construction and assembly. Before the treatment, one week was used to train the groups on how to use the Electric VLab. During the treatment, each intact class in Group I was taught using the direct instruction method, and the other classes in Group II were divided into units of five students with a selected peer tutor leading each unit while the teacher coordinated the learning. At the end of treatment, the post-test was administered to both groups. Mean statistics, standard deviation, and analysis of covariance were used to analyze the data. There was no significant difference between the achievement of male students in both groups. Female students in indirect instruction classes achieved significantly higher than their counterparts in direct instruction classes. There were significant effects of interaction between teaching methods and gender.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".