Planning and Assessing Word Production to Support Lexical Spelling Learning in Grade 1
Bibliographic record
Abstract
Learning to spell is a major challenge for beginning writers because they have to develop a great deal of knowledge. Teachers are faced with a challenge as well in that they have to plan the words to be studied and assess the skill level of their students by analyzing the words they produce. The objectives of this study were to measure the lexical spelling success rate of French-speaking Québec students in grade 1 (6-7 years old) in certain spelling fragility areas, to describe the spelling variations that appeared and to discuss the relevance of planning and assessing word production. The research was conducted with 172 students (82 girls and 90 boys) from nine classes in one public urban elementary school. They were administered a dictation exercise using words selected with various criteria. The analysis was conducted on a corpus of 2076 words produced. The researchers then calculated the spelling success rate and the percentage of errors, in addition to doing a fine-grained analysis of the phonographic, orthographic and morphographic productions and the types of errors made (omission, substitution, addition and displacement). The results highlighted changes under way. They also showed that the success rate varies according to the spelling fragility areas being targeted and, surprisingly, that the rate fluctuates for the same fragility areas. The dictation used seems to be an “economical” tool for various reasons, among others because it is easy to administer (requires little time) and the spelling success rate is easy to determine.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".