Experimental Study on Career Experiential Teaching mode for English Major Freshmen in Application-oriented Colleges
Bibliographic record
Abstract
To meet the demands of the market and society, application-oriented colleges must align their teaching with the real professional world, emphasizing the linkage with industry and actively engaging in career experiential teaching. This study aims to construct a career experiential teaching mode specifically tailored for English majors. After successfully constructing such a model, the author conducted an experimental study targeting freshmen majoring in English to assess its effectiveness in enhancing their career readiness and overall development. The participants were English major freshmen from the Class of 2022 at Guangzhou College of Commerce, enrolled in an Orientation course specifically designed to incorporate the experiential teaching model. After thoroughly analyzing questionnaire responses and students’ training reports, the author discovered that students participating in the career experiential teaching mode exhibited greater engagement, motivation, and satisfaction with their learning experience. Furthermore, they demonstrated a deeper understanding of career-related concepts, improved practical skills, and comprehensive ability development. Therefore, it is concluded that this career experiential teaching mode effectively prepares students for successful careers and enhances their overall capabilities. This research adds empirical evidence to the existing knowledge base regarding the benefits of integrating career experiential teaching into higher education, providing valuable insights for educators to adopt in preparing students for a smooth transition into the workforce.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".