Teachers’ Perspectives Surrounding ICT Use amongst SEN Students in the Mainstream Educational Setting
Bibliographic record
Abstract
At the present time, ICT (Information and Communication Technology) is recognised as a pressing consideration ineducational establishments worldwide. Accordingly, the present research proved to be valuable to teaching staff,teachers in (Special Educational Needs) SEN and leaders in schools. This work’s aim was centred on explaining andexamining the viewpoints and experiences of teachers in regard to the adoption of ICT for learners with SEN (SpecialEducational Needs) in the overall learning setting, and identifying the relationship between inclusion and ICT.Moreover, the various approaches and conditions centred on making ICT more inclusive have been established.Qualitative interviews were carried out with a sample of twenty teachers, the findings of which suggest the adoption ofICT for learners with SEN of all ages; more specifically, computer systems were widely used for word processingalongside writing. ICT may be applied in an effort to fill the void in terms of inclusion whilst accordinglycounterbalancing any contribution and differences in terms of the inclusion of those learners with SEN. However, in aneffort to achieve this, there is a need for teaching to be adapted to the needs of students, with pedagogy needing to beincorporated alongside technology. In regard to further research, suggestions could include more comparableresearches or researches concerning the potential of one-to-one for those students with SEN.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".