Multi-Word Verbs vs. One-Word Verb Construction: Between Avoidance and Preference in EFL Learning Contexts
Bibliographic record
Abstract
Although English allows for both one-word and multi-word verbs, past research has shown that EFL students commonly struggle with, and even avoid, multi-word verbs. This study aims at examining the influence of utilizing contextualized authentic materials via reading activities on EFL intermediate learners' attitudes towards avoiding and preferring using MWV (multi-word verbs, also known as phrasal verbs (PV)) or one-word verbs. The study used both experimental and descriptive methods, and data was collected via pre- and post-tests. MWVs are introduced through reading activities to two learning groups: group A represents the experimental group, and group B acts as the control group. MWVs were introduced to the participants of group A via contextualized authentic materials (texts, pictures, and cartoons) and to group B through non-authentic contextualized reading texts. The analyzed data has shown that the attitudes of group A’s participants improve towards using some MWVs' types on many occasions rather than utilizing their counterparts of one-word verbs. For example, they prefer using transparent and semi-transparent prepositional PVs as well as transparent adverbial PVs. On the other hand, the findings have shown that the participants avoid using non-transparent PVs regardless of their constructions (prepositional PVs, adverbial PVs, or phrasal prepositional verbs). In addition, they avoid using adverbial PVs of the semi-transparent PV’s types. Hence, contextualized authentic material via reading activities is a more effective strategy for enhancing EFL learners' attitudes towards preferring using MWVs rather than their counterparts of one-word verbs, specifically the transparent prepositional and adverbial of PV’s types.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".