COVID-19 Pandemic: Do Learning Motivation and Learning Self-Efficacy Exist among Higher Vocational College Students?
Bibliographic record
Abstract
COVID-19 first appeared in the first quarter of 2020 and spread rapidly throughout the world. Now, schools in China have resumed face-to-face teaching on campus, but the COVID-19 Pandemic still impacts normal teaching activities and student psychology. This quantitative research revealed the levels of learning motivation and learning self-efficacy among higher vocational college students. This study also investigated whether these variables vary according to students' gender, hometown, family structure and field of study. In addition, this research examined the relationship between students' learning motivation and learning self-efficacy. The sample for the survey was 1018 students from a public higher vocational college in Shandong Province. The research collected data via two surveys, the Learning Motivation Scale (LMS) designed by Tian and Pan (2006) and the Learning Self-Efficacy Scale (LSS) designed by Liang (2000). The research used percentages, means, standard deviations, independent group t-test and Pearson correlation coefficient to analyze the data. The results revealed that higher vocational college students' learning motivation and learning self-efficacy scores were above the median score of the two scales. The study found that learning motivation did not vary according to students' gender, field of study or family structure. However, students from different hometowns showed a significant difference in their learning self-efficacy but no significant difference in their learning motivation. Finally, the researchers discovered a significant positive correlation between learning motivation and learning self-efficacy.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".