Study of Mathematics Programs Imbedded in Digital Learning Formats to Bridge Junior and Senior High School Curriculums
Bibliographic record
Abstract
The purpose of this study was to investigate the e-learning method in math to implement the curriculum articulation between junior high school and senior high school, and evaluated its learning effects to improve the implement method of curriculum articulation, to strengthen the students’ digital mathematical materials of curriculum articulation and to quantify the analyses of the students’ learning effects. The research strategies firstly concentrate on interviews to set up teaching content units; secondly evaluate and establish e-learning administrative platform, and then design e-learning materials in ADDIE systems. By previewing and revising contents of these materials, they are practically used in the library activities. In addition to record the pretest and post-test scores of the learners, the data of on-line voting are collected in order to do the comparative analyses to form conclusions as reference for the related researches.The research result showed that using e-learning methods to implement curriculum articulation activities got positive improvement not only in human efforts but also in materials and time; besides, the methods inspired the learners’ motivation, strengthened the learning effects and got the positive satisfaction in the whole learning activities. Researchers suggest that the related researches should be conducted continually so as to meet problems of curriculum articulation in new curriculum of senior high school and help those students who are in need to do automatically learning. Gradually, it can promote school into the learning resources center in the community. I
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".