Blockchain Applications and Challenges in Cross-Institutional Educational Evaluation Data Sharing
Bibliographic record
Abstract
Data empowers educational evaluation, and blockchain technology aids in the governance of educational evaluation data.The union of big data and blockchain technology has prompted the development of educational evaluation toward digitalization and precision of educational evaluation.This paper combines the multifaceted governance utility of blockchain technology for educational evaluation data and proposes to improve the consensus mechanism in educational evaluation information sharing.The PBFT consensus algorithm is updated with node contribution reward and punishment mechanism, the consensus nodes are selected by Fibonacci function characteristics, and the consistency protocol is optimized, so as to design a practical Byzantine fault-tolerant algorithm NCG-PBFT based on node contribution grouping, and analyze the credit value, throughput, normal block out delay, and the number of communications of NCG-PBFT consensus algorithm.Build a comprehensive education quality evaluation platform and bring in the improved PBFT consensus algorithm to test the operation performance of the comprehensive education quality evaluation platform.When the request frequency tends to be stable, the education comprehensive evaluation system of NCG-PBFT consensus algorithm is able to improve the system throughput by 74.54% compared with the PBFT algorithm, which is able to meet the performance and stability requirements of the education comprehensive quality evaluation system.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".