Based on MOOC+SPOC Teaching Reform and Practice of Computer Basic Course in University
Bibliographic record
Abstract
The emergence of MOOC has attracted wide attention from the educational circles at home and abroad. It is both a challenge and an opportunity for the traditional higher education. With the teaching reform of the course of “College computing” is deepening, MOOC will be introduced into the traditional classroom and through the “MOOC ten SPOC” way to achieve the complementary advantages, which all the quality of teaching is of great significance. This paper takes the “University Computer Foundation” MOOC of Changchun University of Science and Technology as an example. Then this paper introduces the exploration and practice experience of the reform of computer course in University by the way of “MOOC+SPOC”. It uses the hierarchical MOOC teaching content which fuse MOOC with the traditional classroom teaching. It introduce the “MOOC ten SPOC” teaching practice process, to achieve the combination of online and offline, curricular and extra-curricular complementary Hybrid Teaching and which analyzes the students for the evaluation of the curriculum, summed up the “MOOC +SPOC” teaching reform practice experience.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".