Investigation of Academic Staff’s Self-Efficacy Using the Educational Internet
Bibliographic record
Abstract
The Covid-19 pandemic has had negative effects throughout the whole world, including education systems. To overcome this negativity, all educational institutions have turned to internet-based education. However, the educator’s self-efficacy is of more importance in this system. This study aimed to reveal the connection between the University academic staff’s genders, ages, titles (doctorate/non-doctorate), and work year characteristics and their self-efficacy beliefs about their educational Internet usage. The sample consists of 100 [51% (n = 51) female and 49% (n = 49) male] academic staff, who were selected according to convenience sampling in the Faculty of Education and Faculty of Sport Sciences at Uludag University. In this study, the “Educational Internet Usage Self-Efficacy Beliefs” scale, developed by Şahin (2009), was used to collect data. Descriptive statistics refer to number and percentage for qualitative variables; quantitative variables are summarized using mean, median, standard deviation, and minimum and maximum statistics. Univariate analyses used binary group comparisons with the Student’s t-test and relationships between numerical variables and Spearman correlation coefficients. Multiple regression analysis was used, in conjunction with the backward method, for the multivariate linear regression method. Analysis results alpha (Type I error) value was evaluated at the level of 0.05 significance. The mean level of self-efficacy belief of academic staff is 109,42. Since the highest score that can be obtained from the scale is 140, the relationships between scale score and age and duration of service variables are significant according to the univariate analysis, while differences in gender and PhD and non-PhD groups are not significant. When multiple linear regression analysis is applied with the backward stepwise method, age and academic title variables are significant in the model. Additionally, the mean scale scores of PhD academicians are higher than others.
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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 | 0.003 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".