Innovation of Virtual Simulation Teaching Strategy Based on Deep Learning Algorithm in Mechanical Manufacturing Experimental Teaching
Bibliographic record
Abstract
Traditional mechanical manufacturing experimental teaching is limited to one teacher demonstrating operations to several students at the same time, which is difficult to take into account and evaluate the differences in knowledge mastery of different students.In order to improve the above teaching defects, firstly, the teaching evaluation of students' experimental level is carried out based on their experimental operation behaviors through K-means clustering.On this basis, a deep learning-based knowledge tracking SAFFKT model is designed to empower and update students' knowledge status.A personalized teaching recommendation method for virtual simulation is proposed based on students' knowledge state, and the hidden semantic matrix decomposition recommendation algorithm for teaching recommendation is improved and implemented.The AUC and ACC of SAFFKT model are significantly higher than that of the comparison model (p<0.01), and it is robust.The F1 value of the recommended experiments was 0.775, indicating a better recommendation effect.The teaching evaluation model achieves accurate classification of students' experimental behavior and yields different learning characteristics of three types of students.Therefore, the innovative work of virtual simulation teaching strategy in this paper is of practical significance.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".