Factors Affecting the Learning Responsibility of First Year Students at Suratthani Rajabhat University
Bibliographic record
Abstract
This research aimed to study the level of learning responsibility of first-year students at Suratthani Rajabhat University and to compare learning responsibilities of first-year students at Suratthani Rajabhat University. The students were separated by gender, age, religion, faculty and academic discipline. The research samples were first-year students in the first semester of the 2011 academic year at Suratthani Rajabhat University, who were selected by using a Krejcie & Morgan Table with not more than 5% error. A total of 354 samples were randomly selected with Proportional Stratified Random Sampling method from three academic disciplines. These disciplines were humanities and social sciences, science and technology and the group of health sciences. A two-part questionnaire was used to collect data, which was analyzed by frequency, percentage, mean and standard deviation. Hypotheses testing and validation was conducted by t-test, F-test, One Way ANOVA and Scheffe's test. The research results found that level of learning responsibility of first-year students at Suratthani Rajabhat University was generally at a high. The students placed importance on understanding new knowledge by using past experiences. Sensibility and ability to work with others effectively was given least importance but remained at a high level. The comparison of learning responsibilities of first-year students at Suratthani Rajabhat University found that gender, age, religion, faculty and academic discipline caused differences in students’ views on learning responsibility by a significance level of .05
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".