The Effect of Explicit Instruction through Combined Input-Output Tasks on the Acquisition of Indirect Reported Speech in English
Bibliographic record
Abstract
Grammar is being rehabilitated (e.g., Doughty & Williams 1998a) and recognized for what it has always been (Thornbury, 1997, 1998, cited in Burgess & Etherington, 2002): an essential, inescapable component of language use and language learning. Few would dispute nowadays that teaching and learning with a focus on form is valuable, if not indispensable. What perhaps is still the subject of debate is the degree of explicitness such teaching and learning should display. The ultimate goal of any instruction is to make L2 learning implicit, like L1 (due to ease of access and automaticity of it). The current study examines the effect of explicit instruction on the participants’ acquisition of explicit and implicit grammatical knowledge in the case of indirect reported speech. The descriptive-survey method was used in this research. The results revealed that this type of instruction fosters both short- and long-term acquisition of explicit grammatical knowledge. However, the study could not foster the acquisition of implicit knowledge.
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 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".