An Instructional Approach Through Situated-Based and Interdisciplinary Learning to Enhance Literary Knowledge and Attitude Towards Literary Learning for University Students
Bibliographic record
Abstract
For college students, opening Chinese courses is the inheritance and continuation of Chinese culture, which can improve students' literary quality. However, because the content of college Chinese course selection has higher requirements for students' literary foundation and loses the pressure of examination, traditional teaching methods can not stimulate students' interest in learning in class, students gain little, and teachers' teaching is more like completing tasks. Therefore, this study explores the methods of college Chinese learning situated-based learning and interdisciplinary learning. situated-based learning can create immersive situations that stimulate students' interest and increase their emotional engagement, while interdisciplinary learning can connect literature with other content such as history, sociology, and philosophy to help students interpret literary works from a diverse perspective. This study adopted a quasi-experimental design and selected 50 primary school students from Lijiang Normal University as the research objects and divided them into three groups: the control group using traditional teaching methods, the experimental group using comprehensive teaching methods and the verification group without intervention. Literary knowledge test and literary learning attitude scale were used to assess the cognitive and emotional changes of students. It can be seen from the research results that the experimental group is significantly better than the other groups in literature knowledge and attitude, and has a more profound interpretation of texts. Therefore, the combination of situational learning and interdisciplinary learning is an effective strategy to strengthen college literature education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".