Design and Implementation of Precision Teaching Mode Based on Big Data Technology
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
Precision teaching refers to the precision and personalization of teaching objectives, teaching processes, and teaching procedures.In the teaching process, teachers use big data technology and artificial intelligence methods to first accurately design and evaluate various aspects of teaching, and then accurately analyze teaching effectiveness.Based on the conclusions drawn from the analysis, continuous adjustment of teaching methods, improvement of teaching plans, and enhancement of teaching efficiency have achieved positive feedback, thereby promoting the precision of classroom teaching.This paper analyzes both the research background and current situation of precision teaching, and explains how to carry out precision teaching in the context of big data.Based on the conditional expectation method in statistics, this paper first constructs the mathematical model of teaching evaluation in precision education; then builds the data processing model, services model and application model of precision teaching respectively.At last, the solution to achieve precision teaching from the perspective of big data is proposed.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it