Research on Improving Education Quality and Efficiency through Artificial Intelligence and Big Data Analysis
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
This study mainly focuses on using artificial intelligence and big data analysis technology to improve the quality and efficiency of education. Firstly, we introduced the basic concepts and development history of artificial intelligence and big data analysis, and outlined their current application status in the field of education. Then, the advantages and challenges of artificial intelligence and big data analysis in improving education quality and efficiency were discussed. Next, the integration application of artificial intelligence and big data analysis was explored, and practical cases in the field of education were provided. We discussed in detail the specific methods and applications of using artificial intelligence and big data analysis to improve education quality and efficiency, including the construction and optimization of personalized teaching models, prediction and intervention of student learning behavior, and the development and application of teacher assistance tools. Finally, the main conclusions of the study were summarized, the limitations of the study were pointed out, and suggestions were made for future research directions and development in the field of education. Through this study, it is hoped that it can provide reference and guidance for research on using artificial intelligence and big data analysis to improve education quality and efficiency.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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