Difficulties and Countermeasures in the Teaching Management of Free Normal Graduates for the Professional Master’s Degree of Education in China
Bibliographic record
Abstract
At present, the cultivation of free normal graduates for the professional master’s degree of education in China is in the stage of exploration and practice. Therefore, there appear some problems in the process of cultivation and teaching management, adversely affecting the quality of cultivation. This paper analyzes the problems and their causes in the course of teaching management and summarizes them mainly in the following aspects: First, students’ being relatively dispersed leaves management inconvenience of time and space; second, the contradiction between work and study leaves little time to study; third, part of the free normal masters pay little attention to study and lack learning initiatives. Based on the above aspects, this paper proposes the following countermeasures: First, monitoring and evaluation should be strengthened to consummate the management system; second, informationalization of management should be accelerated to ease the inconvenience of time and space; third, multi-participation should be involved to improve the efficiency of management; four, the guidance of individuals should be strengthened to improve their self-awareness.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".