The Mental Health and Life Education Curriculum Development Based on Kolb's Experiential Learning Theory
Bibliographic record
Abstract
This research aimed to 1) study the factors influencing the development of subjective well-being, 2) develop a mental health and life education curriculum based on Kolb’s experiential learning theory, 3) compare students' subjective well-being before and after the implementation of a mental health and life education curriculum based on the Kolb’s experiential learning theory. The sample group was thirty undergraduate students from Guangxi Vocational Normal University in the first year of Artificial Intelligence Class 1 (Vocational Normal Teacher). The research instruments were 1) a questionnaire on factors influencing the development of subjective well-being, 2) an interview form on factors influencing the development of subjective well-being, 3) lesson plans, 4) a subjective well-being scale, 5) a teaching opinion interview form, and 6) an observational record of student behavior. The research results indicated that 1) three factors affect students' subjective well-being: life satisfaction, positive affect, and negative affect. 2) The mental health and life education curriculum consisted of six components: (1) concept, (2) contents and time, (3) objective, (4) learning processes according to Kolb's experiential learning theory, (5) learning resources and (6) evaluation, and 3) students’ subjective well-being improved after the implementation of a mental health and life education curriculum based on the Kolb’s experiential learning theory. According to the results of the study, it is suggested that universities should implement management and teaching based on students' emotional and affective experiences and add the content of life education to universities' ideological and political theory education curriculum and students' professional curriculums. Students should put the knowledge and skills they have learned in life education into practice and maintain an optimistic attitude.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".