Collaborative IDE For E-Classroom With Progress Tracking Of Students
Bibliographic record
Abstract
The online classrooms are now great in trend. Due to the ample amount of internet - sharing and getting knowledge becomes simple and cost-effective. Nowadays, many schools provide online classes in all areas of the study. This simplicity and easiness attract more and more students towards acquiring knowledge online. According to a private company which helps users to find the best cost-effective coding bootcamps, between the year 2018 and 2019, online coding bootcamps increased 177% based on a market study of 79 cities in US and Canada. \n \nWith the growth in online coding classes and bootcamps, need arise for tools to help instructors and students to get the highest benefits of the new methods of learning and delivering a lecture. The main problem of this kind of learning is the environment, in the coding/programming field setting up infrastructure and environment is the most crucial part. Another issue is the interaction between instructor and student and progress tracking. \n \nIn this paper, I will be focusing on providing a solution for two main issues: 1) Environment setup, 2) Progress tracking. This report includes my approach to solving these problems by developing software and description of its implementation.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.041 |
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".