Secondary School Learning Management Model for Shanxi, China After Covid-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it