Bibliographic record
Abstract
E education is not entirely new concepts. It is grown in each country. It is taking roots for Indian students as well. One can be provided the best education in the world direct from the persons who wrote the courses for online study. The courses are available for any subject and the levels can be from beginner to higher advances. Individual could find the right course and course level without difficulty. In the 21st Century, students may stay at home and take education across the world. E education system includes the development of e materials. Teaching students online, governing students online, providing the infrastructure and support within which e education can operate, planning and managing e education. Today MOOC Massive Open Online Course is one of the way for geographically dispersed students for participation in web based, free distance learning programme. The word 'MOOC' was coined in 2008 by Dave Cormier University of Manitoba . 2012 was a big year of MOOCs. In this year, various types of MOOCs became well known throughout the world. The Digital Literacy Mission was announced as a part of Union Budget 2016 to cover 60 million rural households within next three years, "Digital Highways" that are being created as a part of the "Digital India Mission" will play an important role in connecting India and Bharat. We do believe that the full impact of Digitalization in Education will play become stronger in 2018 and beyond. Today's students search for university or colleges offering subjects of their choice. Then they enroll for the courses without stepping out of their houses. The students don't have to deal with traffic or boring lectures while persuing the course. Craig 2015 shows the pressure created by the rising cost of higher education and the free offerings of MOOCs. He made the people understand for the first time that the different components of an education offering could be supplied by companies. Various courses in various fields were designed by different organizations related to MOOC. The emergence and use of MOOC for professional teacher development is still uncommon. Researches published were very less on this concept .Over 6.5 million students have enrolled in over 800 free courses from over 200 different learning institutions Gallagher and Garrett,2013 .There are numerous MOOC providers . Dr. Madhuvanti Kulkarni "SattvaGuna Enrichment Programm (Course) - An Effective MOOC" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Digital Economy and its Impact on Business and Industry , October 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18707.pdf
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.023 | 0.010 |
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".