AN ANALYSIS OF HIGH SCHOOL STUDENTS’ READINESS FOR ONLINE LEARNING IN THE CONTEXT OF DIFFERENT VARIABLES
Bibliographic record
Abstract
In the first quarter of the 21st century, the interest and demand for online education continue to grow. The fact that online education is accessible without time and space constraints, enables lifelong learning, and is used in international emergencies such as Covid-19 are considered among the most critical factors in its increasing popularity worldwide. In addition, the recent COVID-19 and natural disasters have made it imperative that online education be utilized at all education levels, from higher to basic education. This study’s objectives are to assess the demographic and technological readiness of Eskisehir Province high school students (grades 9–12) for online learning in the context of these characteristics. The study, which was constructed using the cross-sectional survey model, the study’s link was distributed to all high school students enrolled in both public and private institutions during the fall semester of the 2022–2023 school year and the data were gathered from 507 (n=507) learners. According to the data obtained in the study, it was concluded that students’ general readiness levels for online learning were high. When analyzed according to the sub-dimensions of the scale, it is seen that students’ computer self-efficacy and self-learning levels are medium. In contrast, their internet use self-efficacy is high. In light of the preceding, it is determined that stronger technology usage abilities and increased technological integration into the classroom are necessary for students to be better equipped for online learning. It is believed that crucial data from this study was gathered to comprehend the readiness levels of pupils for online learning. However, it is advised that the study’s shortcomings be taken into account and that in the future, more thorough research with larger sample sizes be done.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
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".