ELEARNING CURRENT SITUATION AND EMERGING CHALLENGES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims to present and discuss current, as well as future challenges of eLearning technologies in the higher education institutions and organizations.ELearning has greatly transformed our way of learning by the use of the newly developed technologies and applications. This paper explores the eLearning current situation. After a brief eLearning history, from the earlier 1960’s, with the first generalized computer assisted instruction system PLATO (Programmed Logic for Automatic Teaching Operations) to the 2010's with the development of social media for learning and the MOOC (Massive Open Online Courses). After that, the paper provides a review of the eLearning concept and how it has evolved over the years, followed by a look at the current technologies (from CD-ROMs to Virtual worlds and Game authoring technologies), applications and platforms being used. The emerging challenges are eventually discussed: needs for identifying suitable strategies and understanding the technology and pedagogy integration for effective eLearning implementations referring to pedagogical and cognitive aspects, level of ICT skills for both all the people involved in teaching, total commitment from management for eLearning system operationalization and sustainability, need for software quality frameworks and standards.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it