A Study on Progress of Students in Nizwa College of Technology with Special Emphasis on Stress Related Issues
Bibliographic record
Abstract
In today’s competitive world, challenges faced by students from different sources contribute to stress and effects the consistency in their performance. So the researcher felt a need to study the reasons behind their underperformance. An exploratory study, conducted to know the latent intentions of the respondents towards learning process in Nizwa College of Technology, across three departments— Engineering, Business studies and Information Technology. Out of total population of students studying in three departments of Nizwa College of Technology, a sample of 102 students were taken for research which comprised a mix of Male and Female Students. The sampling method adopted for the study is convenient sampling method, wherein the students were contacted on a random basis by administering a structured online questionnaire using google forms. Data is collected from primary and secondary sources. The supportive literature review and the conceptual framework were taken from secondary sources. Data was analyzed with the help of T-test and Factor analysis. It is found that most of the students get worried and tired when they are in stress. To reduce the stress levels almost 33% of students believe that talking to their friends and taking rest reduces stress. It revealed that various factors that contribute to stress are economic, personal, college resources, learning methods, outcomes, teaching methods, recreational activities and infrastructural facilities. Hence, it is concluded that apart from academic issues there are many other things which are contributing towards stress among students. Many students are either worried or tired because of the pressure they face in their day to day activities. Stress related issues cannot be ignored in an academic environment, as this may lead to a negative impact on a student’s life.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".