The Relationship Between Preschool Teachers’ Computer And Internet Use and Online Learning Motivation
Bibliographic record
Abstract
The increasing use of technology in the world and the search for new education methods have made learning processes independent of time and space such as distance education and online learning. Moreover, health concerns have made online learning environments more popular during the pandemic process. Therefore, in this study, the relationship between pre-school teachers' computer and internet use and online learning motivation was examined. For this purpose, the relational scanning model, one of the quantitative research methods, was used in the study. The sample of the study consists of 160 preschool teachers. Computer and Internet Usage Scale and Online Learning Motivation Scales were used to collect data in the study. t-test, one-way variance (ANOVA), Pearson correlation and, simple linear regression analysis was used for data analysis. As a result, it has been revealed that the online learning motivations of preschool teachers do not differ significantly according to gender, time spent on the internet and, the number of media tools used to access the internet. Besides, it has been observed that the online learning motivation of pre-school teachers who have just started the profession is higher than experienced teachers. It was revealed that as the self-efficacy of pre-school teachers using computers and the internet increased, their online learning motivation also increased.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".