Effect of Digitized Textbooks on Secondary School Students’ Domains of Learning
Bibliographic record
Abstract
In developing countries like Pakistan, digitized text books are one of the most recent educational reforms brought about by the educational technology This study analyzed the effectiveness of Punjab Information Technology Board’s (PITB) digitized textbooks on students’ cognitive, affective and psychomotor domains of learning. The study was delimited to only those levels of learning domains that were specified in National Curriculum of Pakistan, 2006. The nature of the study was quantitative and employed Quasi Experimental Non-Equivalent Control Group Design. Sample of the study comprised of 56 students studying Chemistry in grade 9 at a public sector school of district Lahore, Pakistan. Experimental group was taught by using digitized Chemistry textbook and control group was taught by using conventional mode of instruction. The intervention lasted for 12 weeks. Data was collected by using three different valid and reliable instruments. Data was then analyzed using descriptive and inferential statistics. All hypotheses were tested at a significance level of 0.05. The results revealed that there was no significant effect of digitized textbooks on students’ cognitive domain. But there was statistically significant effect of digitized Chemistry textbook on students’ affective and psychomotor domains. Recommendations were made to bring learning in cognitive domain at par with affective and psychomotor domains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".