From Memorising to Visualising: The Effect of Using Visualisation Strategies to Improve Students’ Spelling Skills
Bibliographic record
Abstract
Spelling is an essential literacy skill and an important language component that can have a significant effect on L2 students’ future education and occupational status. However, many students struggle to master this skill, and most L2 teachers are limited to traditional approaches when teaching spelling. Therefore, this study aims to investigate both the effect of using visualisation strategies to improve the L2 students’ spelling skills and student’s attitudes towards the use of those strategies. I adopted an experimental approach, whereby the experimental group was trained to use visualisation strategies to study the spelling of new words, while the control group received no special tuition and was required to study the spelling of new words using the methods they normally use. The sample for the study consisted of 42 female sixth graders from Al-Manahej Private Elementary School in Riyadh; they were divided into two groups: 21 students in the experimental group and 21 students in the control group. In order to collect data and achieve the goal of the study, three tools were used: pre-achievement and post-achievement tests to measure the differences between the experimental and control groups’ scores. The students undertook five weekly tests to measure the effectiveness of the visualisation strategies to improve their spelling skills, and a social validity questionnaire to assess their attitudes towards the strategies. The findings were anticipated to promote the use of visualisation strategies in the field of education, to encourage curriculum designers, decision makers, and language teachers to employ them when teaching spelling.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".