The Use of e-Learning Course during the COVID-19: A Systems Thinking Approach
Bibliographic record
Abstract
In the era of theInternet, most educators have been supported by powerful tools ranging from e-books and e-learning sites to cloud services, and students’ learning environment has been a mix between traditional study (in class) and e-learning through some kind of online learning platform. Due to the uncertainty of the rapidly changing COVID-19 situation, all colleges and universities have to shutter their physical campuses and move their courses to remote and online formats hastily. This prompted many to wonder if all of the faculty are ready and qualified to teach online courses and/or if all of the students are ready to learn in the comprehensive online environment. If not, what ultimate impact will be to our higher education during this national emergency virus pandemic since there is no choice but depend on where they sit currently, not to mention the negative reviews and concerns regarding the online education. To make this transfer seamlessly and conflict mitigation, this paper applied systems thinking for an e-Learning course and proposed a flexible grading method for an e-learning environment, which will enhance students’ grades by allowing students to control their own study paces and the amount of efforts spent in the course, which can bring a successful online learning experience.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".