Digital Teaching Supervision Model of Educational Supervisors under the Office of Primary Educational Service Area in Northeastern Region
Bibliographic record
Abstract
This research aimed to: 1) Investigate the effectiveness of digital teaching guidance provided by educational supervisors affiliated with the Northeastern Primary Education Area Office. 2) Develop a model for digital teaching guidance by educational supervisors under the Northeastern Primary Education Service Area Office. 3) Evaluate the feasibility and benefits of the proposed model. The research consisted of three phases: Phase 1: Investigating the effectiveness of digital teaching guidance by educational supervisors, based on relevant documents, research, and interviews with a purposively sampled group of 30 individuals demonstrating outstanding practices (Best Practice). Phase 2: Developing a model for digital teaching guidance by educational supervisors and evaluating the model’s suitability through a seminar involving 7 experts, using the Connoisseurship approach. Phase 3: Evaluating the feasibility and benefits of the digital teaching guidance model by educational supervisors. The sample group comprised 50 educational supervisors affiliated with the Northeastern Primary Education Service Area Office, selected through multi-stage random sampling. Statistical analysis used in the study included mean, percentage, and standard deviation. The research findings revealed that: 1. Effective digital teaching guidance by educational supervisors consists of four key processes: 1) Analysis of context, 2) Development of teaching guidance plans, 3) Observation of teaching practices, and 4) Reflection on outcomes. Each process involved problem analysis, needs identification, collaborative planning, and the systematic use of digital tools and online social media aligned with the teaching guidance process. The effectiveness of teaching guidance was evaluated through the assessment of teachers’ knowledge, understanding, and learning management abilities, collaboratively determined and facilitated through an online Professional Learning Community (PLC). 2. The developed model comprises five components: 1) Principles and concepts, 2) Objectives, 3) Core content of the model, including the four-step digital teaching guidance process, 4) Implementation guidelines, and 5) Success conditions. The model’s suitability was assessed at the highest level. 3. In the feasibility and benefits assessment, the overall evaluation indicated the highest level of feasibility and benefits.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".