Comparing Implicit and Explicit Morphological Analysis Instruction for Upper Elementary Readers
Bibliographic record
Abstract
Purpose We compared the effects of two types of instruction on novel suffix learning. Instruction differed in the explicitness of the attention given to the morphological structure of the targets. Learning in form and meaning was measured in trained and transfer words.Method Over three days Grade 3 (N = 83, 45.8% females, Mage = 8.4) and Grade 5 (N = 86, 47.7% females, Mage = 10.4) students with English as their first language (93% caucasian, 4% East Asian, 2% Latino, 1% First Nations, Metis and Inuit) received training on the definitions of pseudowords with a salient morphological structure (e.g. nim meaning small in “hillnim”). Training activities explicitly taught the morphological structure of the words, or exposed participants to this structure implicitly while teaching the use of general context clues. Participants’ learning was assessed immediately (one day) after training, and at follow-up one week later, using a suffix identification task, a word definition, and a multiple-choice task.Results Participants at both grade levels scored similarly on the form task across conditions, but in terms of meaning, explicit training on morphological structure yielded better results for both grade levels and word types. However, for Grade 5 the differences across training conditions were only significant in the word definition task.Conclusion Our results support explicitly teaching the structure of morphologically complex words. Explicit instruction was effective even for older students with more reading experience.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".