Bibliographic record
Abstract
The use of videotaped microlectures as a new medium of teaching has been receiving increased attention in China’s educational reform agenda. Since 2012, microlecture competitions targeting various levels of education have been held nationwide. However, a top–down contest-based approach, while efficiently popularizing the concept of microlectures, may create a false impression among classroom teachers unfamiliar with technology, leading them to confuse microlectures with exhibitions of complex computer and media technologies and, thus, intimidating them from trying the new teaching mode. Using autoethnography to document in detail the author’s production of a nationally awarded microlecture, the present study highlights what classroom teachers can do using technology-mediated teaching and asserts that teachers’ personal practical knowledge, rather than technology, plays the decisive role in producing a microlecture. It also argues that by taking on the dual role of ethnographer-as-researcher and ethnographer-as-informant, classroom teachers can use reflective autoethnography as a meaningful learning experience to understand and critique their teaching practices and develop living educational theories for the enhancement of technological pedagogical and content knowledge (TPACK) in massive open online courses (MOOCs).
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".