Examining of the Emotional Mood about Their Online Education of First-Year Students Beginning Their University Education with Distance Education Because of COVID-19
Bibliographic record
Abstract
The Covid-19 pandemic continues to be negatively effective in many areas of life. It has also affected the face-to-face education of 2020 fall semester. In Turkey, Students who have recently entered the university in 2020 started their undergraduate education with distance education like upper grade students. However, the fact that the first graders fell into the distance education system before experiencing university life and without knowing their teachers and friends created different emotional mood. The aim of this study is to analyze the emotional mood of students who have just get into university and have to start their undergraduate education with distance education. Exploratory sequential mixed method is used as a method of this study. Phenomenology method is used the first stage of the method and the survey method is used as the second stage of the method. A total of 18 fresh students (12 female and 6 male) in the departments of Faculty of Education and Faculty of Sport Sciences of Mus Alparslan University were the sample group of the qualitative stage. And 141 students (87 female and 54 male) were the sample of the quantitative stage. As a result of the qualitative analysis, the emotional moods of the students were grouped under four sub-themes: emotion of shock, unfamiliarity with the system, emotion of curiosity and anxiety for the future. First-year students stated that they experienced feelings of shock such as sadness and anxiety when they learned that they would start university with distance education instead of face-to-face education. The students stated that they wondered about the method of teaching the lessons, whether the lessons would be efficient, how the exams would take place, that they were unfamiliar with the system, and that they were worried about the future due to all these uncertainties. According to the analyzes obtained from the survey, it is understood from the answers that the 141 participant students experienced emotional states similar to the results obtained in qualitative findings at a rate of 90% and above. Only the rate of students experiencing an emotion of shock was 64.5% and the rate of experiencing future anxiety was 85.8%. As a result, it was understood from the analyses that qualitative and quantitative results were parallel to each other.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".