Teachers’ Experiences and Perceptions Regarding Technology at Early Childhood
Bibliographic record
Abstract
With mobile technology rapidly permeating all aspects of modern society, including education, research on teaching and learning has not only demonstrated its benefits but also highlighted certain limitations, while resistance to their usage continues to be a common response among teachers. However, with the Covid-19 pandemic bringing about unprecedented changes in all levels and sectors of education since 2020, students have been compelled to adopt e-learning to master specific learning skills, such as spelling and counting, while teachers have served as curators for this new educational environment. In this context, the aim of this research is to investigate the experiences of early childhood teachers regarding the use of technology among learners aged five in early childhood educational institutions after the pandemic to offer suitable suggestions for the future of learning. The study participants for this qualitative approach-based research were 20 early childhood teachers identified using a comprehensive case design by employing purposive sampling methods. A questionnaire comprising open-ended questions was sent to the study participants over WhatsApp. This was followed by an interview, after which the obtained data were subjected to content analysis. The participants stated that completing e-activities may be considered time spent productively by students because such activities facilitate access to basic learning skills. The study results could help teachers enhance their skills by making sense of the factors that affect their use of e-learning in the classroom.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".