Barriers to Use of Educational Technology in the Learning Process of Primary School Students in District 13 in Tehran
Bibliographic record
Abstract
The aim of this study was to examine the barriers to use of educational technology in the learning process of primary school students in district 13 of Tehran. This research in terms of purpose is practical, in terms of the title characteristics of the research is descriptive, and in terms of data collection method is a field research. The statistical population is 350 people, consisted of all primary school teachers and administrators for girls in district 13 of Tehran in the academic year of 2016-2017. Random sampling was simple and available. The sample size was calculated about 124 people based on Cochran formula. In this research, a questionnaire was used to collect data. Content validity of the questionnaire was approved by using expert and corrective opinions of some teachers and subject specialists, and to determine the reliability of the questionnaire the Cronbach’s alpha coefficient was used. In order to analyze the collected data, the proportional statistical tests such as correlation coefficients and regression analysis were used that in this regard, the spss statistical software was used. The research results showed that 4 human, cultural, and physical factors and courses content can create barriers in the use of educational technology in the process of student learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".