Perceptions of Primary School Teachers Regarding the Implementation of Differentiated Instruction to Students with Learning Difficulties
Bibliographic record
Abstract
The main purpose of this paper is to investigate the perceptions of primary school teachers of all specialties in the Dodecanese (Greece) regarding the implementation of differentiated instructional strategies to support students with learning difficulties in the classroom. The research was conducted through quantitative approach using an anonymous electronic questionnaire on a sample of 174 primary school teachers of all specialties in the Dodecanese, during the period from February 13, 2021 to April 28, 2021. As for «content differentiation», the strategy most frequently used by teachers is the selection of the most crucial assignments for underachieving students with learning difficulties. As for «process differentiation», the strategy most often used is to adjust the pace of instruction to each student’s needs with learning difficulties. Regarding the «product differentiation», the strategy most used by teachers is to offer extra support to students with learning difficulties, who have difficulty finishing activities. In terms of «assessment differentiation», the strategy most frequently used is to give more time to students with learning difficulties to complete tasks or exams, while, in terms of «learning environment», the strategy most commonly used by teachers is to make a conscious effort to ensure that students engage consistently and fairly in class.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".