Bibliographic record
Abstract
The COVID-19 pandemic has hindered the provision of educational services for a considerably long time. Even today, students, teachers, educators, and other stakeholders continue to struggle with the permanent traces of the pandemic. The present research intends to delve into the opinions of students and teachers at the high school and at the tertiary level while investigating some key variables. A combination (variation) design, one of the mixed research methods, was deployed in this regard. Utilizing random sampling, 428 high school students, 189 teachers of different branches, 673 university students and 105 faculty members were employed in the research. The Montreal Cognitive Assessment (MoCA), Zung Self-Rating Depression Scale (SDS), Generalized Anxiety Disorder Screener (GAD-7), Motivation Scale (MS) and Semi-Structured Interview Questions were utilized as the data collection instruments. The data obtained within the scope of the research were analyzed using SPSS 25, AMOS 24, and The PROCESS macro for the SPSS (Hayes, 2018) program. In addition to the models developed by Hayes (2018), path analyses produced by the researchers of the current study were also referred to. At the end, it was determined that the cognitive test results of high school students were low, and their motivation results were affected by the variables of depression and anxiety. These variations appear to be creating significant differences in variables such as gender, receiving psychological support, providing mentor support, adaptation to the post-pandemic process, digital competence and infrastructure competence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".