Challenges Faced by Teachers in Teaching Literacy and Numeracy for Slow Learners
Bibliographic record
Abstract
The current study explored pedagogical strategies carried out by teachers to support special need children in improving their level of literacy and numeracy. The current study utilized qualitative research design where classroom observations were carried out to explore teaching activities used by teachers in teaching language and mathematics. It was then followed by a focus group discussion to identify problems faced by children in numeracy and literacy. This research was carried out in one of the elementary schools in Yogyakarta, Indonesia. Data collected was analyzed manually by focusing on the main aspects. Results demonstrated that 27 activities were frequently used by teachers in teaching language and mathematics. Those activities are the common teaching practice for slow learners. In order to evaluate the effectiveness of those practices, a focus group discussion with a group of students was carried out. Results revealed that most students have problems in literacy (spelling, reading complex words, and write long words) and numeracy (counting, subtraction, multiplication and divide). As the common teaching practice was found to have minimal effect on children’s literacy and numeracy, the current study suggests rethinking of a new pedagogical approach for improving literacy and numeracy for slow learners.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".