Innovative Pedagogical Reforms in Theoretical Mechanics: Exploratory Research and Practical Implementation
Bibliographic record
Abstract
To address existing challenges in teaching theoretical mechanics and enhance instructional quality, the teaching team implemented innovative reforms. Guided by a "student-centered" philosophy and powered by digital intelligence technologies with Chaoxing AI as the engine, the course reconstructed a three-dimensional objective system for theoretical mechanics. This system prioritizes knowledge delivery as its foundation, ability cultivation as its core, and value shaping as its ultimate goal. By deeply integrating course content with intelligent elements—such as knowledge graphs, AI teaching assistants, and AI agents—an intelligent theoretical mechanics curriculum was developed. Practice demonstrates that this reform provides students with intelligent learning support and teachers with efficient pedagogical assistance, achieving bidirectional empowerment in teaching and learning. It enables seamless integration between offline and online modalities, resulting in more targeted and effective offline classroom designs, alongside more personalized and intelligent online self-directed learning. Consequently, classroom teaching quality has improved, students' innovative and practical abilities have been developed, and their comprehensive growth has been advanced.
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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".