Early self-teaching: Testing the nature of orthographic learning and learning transfer in beginning readers
Bibliographic record
Abstract
The current study aimed to clarify the nature of orthographic learning during independent reading (i.e., self-teaching) among beginning readers. The most prominent theory addressing learning of word-specific orthographic forms, the self-teaching hypothesis, predicts that beginning reading is beginning orthographic learning. And yet, empirical evidence to date has focused on older children. Here we test the extent to which beginning readers learn new words during self-teaching experiences, whether they transfer that learning to their subsequent processing of related words, and the role of decoding in both processes. In this study, children in Grades 1 and 2 read simple nonwords (e.g., lurb) embedded in short stories adapted to be appropriate for early readers. Children then completed orthographic choice tasks to test both their learning of those words and the transfer of learning to novel words that are either morphologically or orthographically related (e.g., lurber and lurble, respectively). Results indicated that children in Grades 1 and 2 learned the spelling patterns of novel words. Further, they were able to transfer that learning to their processing of the novel related words; however, only orthographically related words showed clear evidence of spelling-specific transfer. Notably, only children in Grade 2 were able to do retain their learning three days later. Finally, results indicated that accurate phonological decoding is not required for learning to occur, although it may facilitate learning for children in Grade 2. Taken together, these findings help to better understand the nature of self-teaching in beginning readers, informing future research and educational practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".