Research on the Hybrid Teaching Mode of Mechanical Fundamentals in the Context of Artificial Intelligence
Bibliographic record
Abstract
The purpose of this study is to explore the innovation and application of mechanical foundation teaching mode in the context of artificial intelligence, and to improve students' learning efficiency and understanding ability through a hybrid teaching mode, which combines online and offline teaching methods. In the experiment, by comparing and analyzing the differences between traditional teaching mode and blended teaching mode in student learning effectiveness, the superiority of blended teaching mode in mechanical foundation courses was obtained. At the same time, this study also pointed out the problems and solutions in the implementation of blended learning mode, providing strong theoretical support and practical guidance for the optimization of future teaching modes. In the experimental stage, we explored the effectiveness of Long Short Term Memory (LSTM), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) in educational technology applications through four experiments. In the benchmark performance evaluation experiment, the accuracy based on the LSTM model was 75%, the recall was 80%, and the F1 score was 77%. In the second learning path recommendation effectiveness evaluation experiment, the LSTM model improved the average score of students by 15 points in recommending learning paths. In the evaluation experiment of improving learning motivation and participation, the learning motivation score based on the LSTM model was 90 points, and the participation score was 92 points. From the above experimental data conclusions, it can be seen that the LSTM model has great potential in educational technology applications, especially in designing personalized learning paths, improving learning motivation and engagement, and promoting long-term learning outcomes.
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 | 0.002 |
| 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 | 0.001 | 0.002 |
| 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".