Secondary School Learning Management Model for Shanxi, China After Covid-19
Bibliographic record
Abstract
The COVID-19 pandemic instigated a global educational crisis, compelling an abrupt transition from traditional in-person instruction to emergency remote teaching. This sudden shift underscored the need for robust learning management models capable of navigating unprecedented disruptions. The objectives of this research were to ascertain the needs and recommendations for designing a post-COVID-19 learning management model for secondary schools in Shanxi Province, and to develop and evaluate the model. We conducted the research in three phases, the initial investigation using survey and interview techniques, the construction and revision of the model by focus-group meeting, and the evaluation of the model by stakeholders. The statistics were used. The findings of the first phase provided the needs of the model and recommendations for its design. We called the learning management model derived from this research the ILAR Model, which included the three elements of Investigation (I), Learning Action (LA), and Reflection (R). The investigation provided student backgrounds for lesson planning; learning action consisted of learning roles, learning resources, and learning activities; and reflection included learning evaluation and learning feedback. The model evaluation revealed the highest quality in all aspects: appropriateness, feasibility, and effectiveness.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".