Student-Centered Online Teaching Practices in Theoretical Mechanics
Bibliographic record
Abstract
The COVID-19 pandemic has had a serious impact on education at all levels and types, and the education system, including colleges and universities, has been forced to respond by a sudden shift to online teaching. Successful online teaching requires careful thinking, planning, and technical and human support from teachers. Based on the reflection on the offline classroom teaching of theoretical mechanics for many years and the online teaching practice of first half of 2020, the author summarizes effective strategies for implementing online teaching of theoretical mechanics. We prepared the theoretical mechanical lessons through a “student-centered” approach, such as preparing teaching materials, students and teaching methods. These teaching strategies include (i) Adopting live-stream teaching as the main teaching method, (ii) Applying electronic blackboard to online deductions, (iii) Linking theory to practice for better understanding of knowledge, (iv) Integrating curriculum content in ideological and moral education, (v) Conducting formative assessment to supervise and motivate online learning. The implementation of these online teaching strategies has effectively promoted the development of students' independent learning ability.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".