Using E-Learning and ICT Courses in Educational Environment: A Review
Bibliographic record
Abstract
With the quick emergence of computers and related technology, Electronic-learning (E-learning) and Information Communication and Technology (ICT) have been extensively utilized in the education and training field. Miscellaneous methods of integrating computer technology and the context in which computers are used have affected student learning in many different ways. Integration of technology can help to create courses that are easier and more effective for learners, especially if ICT and E-learning are integrated into the educational process. ICT, when used in a sufficient and suitable manner, can help learners to understand IT better. Moreover, it can help learners to use the absolutely necessary skills which can guarantee success in empowering the learners with IT awareness and skills which are essential for success in today’s knowledge economy. This study aims to conduct a survey on the role of E-learning and ICT courses in educational environment. By having ICT courses available online via E-learning, every learner will adapt and be comfortable with the continuous upcoming technologies and innovations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".