The Development of an Instructional Model Based on Interactive Learning Theory to Improve Undergraduate Students’ Reflection Ability
Bibliographic record
Abstract
This research aimed to: 1) study the factors that affect the development of the third-year students’ reflection ability in Baise University; 2) develop an instructional model based on interactive learning theory; 3) compare the third-year students’ reflection ability before and after using the instructional model based on interactive learning theory. The sample group was 32 third-year students’ reflection ability at Baise University. The research instruments were 1) questionnaires, 2) interview forms, 3) lesson plans, 4) questionnaire on reflection ability, and 5) teaching opinion interview form 6) observational records of student behaviour. The research was conducted in three steps: studying the factors that affect the development of third-year students’ reflection ability, developing an instructional model based on interactive learning theory, and the experimental and improvement process. The study results showed: 1) the factors that affect the development of students' reflection ability consisted of three aspects (1) school teaching management, (2) teachers' teaching style, and (3) students' learning behaviour 2) The instructional model based on interactive learning theory included four elements: (1) principle, (2) objectives, (3) learning process, and (4) results; 3) students’ reflection ability was improved after the implementation of an instructional model.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".