The Correlation Between Performances Among Lexical-Related Tasks and The Performance in The Sentence Construction Task
Bibliographic record
Abstract
This study employed a quantitative correlational design to explore the correlation between the students' performances among lexical-related tasks and how these tasks affect the performance in a sentence construction task. Using IBM SPSS Statistics Version 22’s Pearson Partial Correlation Test, this study calculated participants' performance in primary lexical attributes by recognizing the following aspects of lexical knowledge: pronunciation patterns, morphological structures, syntactic properties, semantic characteristics such as abstract and interconnectedness, and a complete sentence construction in a strict naturalistic classroom setting. The test results showed that the participants made 297.05 seconds on average for 42 correct responses in Lexical Decision Task, 5.88 seconds per picture projected on the screen in Picture-Naming Task, 8.33 seconds for each word in Semantic Judgment Task, and 30.17 seconds on average to complete a sentence. These results concluded that the participants' performance in identifying strings of letters does not correlate significantly with their performance in understanding how a particular word functions grammatically within a sentence. In terms of the level of automaticity, the participants’ performance exceeded the average performance. The findings suggested that their performance in understanding primary lexical attributes in single lexicons does not facilitate their understanding of semantic characteristics. Henceforth, the students’ lexical knowledge does not yet construct an integrated linguistic representation in the target language acquisition. The study confirmed previous evidence that stated that a better performance in lexical-related tasks significantly impacted sentence processing and construction skill.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".