Detecting and Comparing the Incremental Changes of Three Aspects of Word Knowledge in Educational and Naturalistic Settings
Bibliographic record
Abstract
Knowing words involves knowing multiple aspects of word knowledge (Nation, 2013).Most previous research focused on single aspects and measured the ultimate gains at single points of time.The uniqueness of this dissertation lies in detecting and comparing the productive incremental changes of three aspects of word knowledge (meaning, spelling, and word part) in Educational (e.g., school) and naturalistic settings (e.g., workplace) at different time intervals over a 24-month period.Using Nation's (2013) Word Knowledge Framework, two main studies and a follow-up study were conducted from January 2018 to January 2020.Drawing on Yin, ( 2014) a single case study design was used with multiple embedded units of analysis, across two main studies and a follow-up study.Five upper-intermediate Arabic-speaking learners participated in Study 1, and two advanced Arabic-speaking learners participated in Study 2. Three participants from Study 1 and both participants from Study 2 participated in the Follow-up Study.The same battery of penand-paper tests (i.e., spelling, multiple choice and fill-in-the-blanks) was used in both the two main studies and the follow-up, which were then analyzed statistically.Data drawn from semistructured interviews were analyzed using Saldaña's (2013) first cycle and top down coding method.Results countered those previously reported (e.g., González-Fernández & Schmitt, 2019; Schmitt, 1998).A developmental hierarchy was detected among the three measured aspects of word knowledge.They developed concurrently and in varying proportions.Basic meaning knowledge always enjoyed the highest gains, followed by spelling, word part, and polysemy knowledge.A relationship between the incremental changes of the three measured aspects and vocabulary size was also detected.iii Vocabulary learning strategies such as word lists, word parts, and orthographic repetition seemed to play a positive role in word knowledge development.Factors such as lack of adequate word exposure opportunities and learners' first language transfer were found to negatively affect word knowledge development.This study concludes that certain aspects require more attention and time than others.Learners need to be exposed several times to the target words and in different contexts.They also need to be taught and trained to use the different vocabulary learning strategies to enhance attainment.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".