Bibliographic record
Abstract
Given the growth of technology ending in the emergence of smart schools in educational systems, the study improvement and extension of innovative teaching methods are of great importance.Thus, this paper studies the effect of doing e-homework on learning of learners with field dependent and independent learning styles in a semi-experimental design with pre-test and post-test, which is the purpose of this paper.The sample of this paper consists of 76 students from vocational schools of Tehran 15 th District, who are non-randomly assigned (in two groups of 38 as experimental and control groups).This study examined the performance of both groups in one subject before and after training through pretest and post-test using a researcher-made test.To determine the cognitive style, students are asked to respond to Witkin Group Embedded Figures Test (GEFT).Both groups are exposed to the same education with the difference that the students of the experimental group -in contrast to the control group -did their homework electronically.As a result, statistical analysis of data -performed using the mean differences and covariance analysis -showed that doing the homework electronically enhanced learning of field independent learners more than the ones field dependent learning style.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.956 | 0.934 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".