Barriers Regarding Adoption And Inclusion Of Future Technology In Education
Bibliographic record
Abstract
Today, the advancements in technology and modernized methods of teaching-learning have changed the attitude of entire world towards future technology. Everyone is indulging in developing and incorporating latest tools, devices and technologies in education system. Smart classes are the best examples that incorporated various technological devices and gadgets such as smart board, interactive boards etc. The future technology has transformed our lives i.e., it offers chance for creating new industries and supporting new businesses for economic sustainability; enhances quality of life with social inclusion in terms of social sustainability; and lowering environmental impact by creating greener society for environmental sustainability. Today, Indians instantly adopt modernization and globalization in every aspects of life, but in context of e-learning, India is somewhat lacking behind the developed countries like USA, UK, Canada etc.. The government, non-government organizations, policy makers and stakeholders must put their emphasis towards inclusion and incorporation of technology into the teaching-learning process. Thus, this research paper highlights some essential issues regarding challenges and concerns about adoption, inclusion and implementation of future technology in the present educational scenario, thereby, reason out significant suggestions for their optimum utilization.
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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".