A STUDY ON COMPUTER LITERACY PROGRAMME FOR VILLAGE HIGHER SECONDARY SCHOOL STUDENTS
Bibliographic record
Abstract
In our contribution we focused on finding out the computer literacy of higher secondary school students. This paper presents the results of Computer literacy questionnaire. Questions in the computer literacy questionnaire (CLQ) were concerned with the usefulness of Computer Literacy Programme (CLP), Learning Experience, Teaching efficiency of students, Community wise participation, and gender wise participation. Questions were generally open ended, but we offered possibilities too. The CLQ was filled by students (n = 97) of village higher secondary school. The number of boys (n =58) and girls was similar (n = 39). We used Pearson chi - square test for finding of statistically significant difference between genders. In our research we found that nearly all students use computers. Most students, nearly 80 %, belong to other backward class community. About quarter of all students use the computer at home. This is caused by the fact that service for access to the computer is still expensive for the most of people in Tamilnadu government school. We found that the most of the students understands computer. It means that computer literacy of students is improving. Girls scores higher marks than boys. The computer literacy quotients of girls are more than boys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".