Effects of Learners’ English Proficiency Level in Learning English Prepositions through the Schema-Based Instruction
Bibliographic record
Abstract
The purpose of this paper is to compare the efficiency of the core schema-based instruction (SBI) in learning English prepositions between two groups that were placed depending on learners’ level of English proficiency. The SBI in the present study refers to a way of teaching in which the schematic core meaning of a given lexical item is provided. This is essentially different from the translation-based instruction (TBI) in that the SBI, which takes cognitive linguistics (CL) as its theoretical basis, provides learners with a single abstract core meaning in vocabulary learning, whereas the TBI provides a list of several meanings and learners memorize it without a chance of paying attention to the semantic connection among its meanings. The efficiency of the SBI in comparison with the TBI has been investigated in the previous studies with high expectation, but not all of them have shown its significant achievement over the TBI. For this reason, several researchers have pointed out that the effects of the SBI may be influenced by learners’ English proficiency level (e.g., Cho, 2016; Imai, 2016). However, this issue has not been fully explored empirically. The participants of this study, 41 students at a technical college in Japan, learned the six English prepositions (at, in, on, to, for, with) in accordance with the SBI. Then the participants were divided into two groups depending on the results of their TOEIC Bridge scores that they took about one month before this study. In order to assess the difference in efficiency between the two groups, pre- and post-tests targeting the six prepositions were used. The results of the pre- and post-test scores and t-tests suggested that the SBI worked more effectively for the learners with higher English proficiency than the ones with lower English proficiency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".