Web-based Concordancing and Other Reference Resources as a Problem-solving Tool for L2 Writers: A Mixed Methods Study of Korean ESL Graduate Studentsâ Reference Resource Consultation
Bibliographic record
Abstract
The present study investigated how 6 Korean graduate students at a Canadian university used a suite of multiple Web-based reference resources (named i-Conc), consisting of concordancers and dictionaries, as a cognitive tool for solving linguistic problems encountered over the course of completing—in English, their second language (L2)—an academic writing assignment for one of their graduate courses. Using a mixed methods design employing surveys, interviews, screen recordings, a query tracking log, and detailed case studies, the thesis provides rich descriptions of (a) the processes, and outcomes of the 6 participants’ uses of i-Conc as a reference tool for their writing authentic academic tasks and (b) their perceptions of the suite as a means of writing assistance.\nOverall, i-Conc served as an intellectual partner that aided the participants in strategically solving lexical and grammatical problems during their writing assignments: About 70 % of the problems they addressed with i-Conc resulted in correct text formulations or revisions. The different resources in i-Conc were each shown to have unique functions for which they were best suited, suggesting that concordancing may optimally be consulted in combination with, not in place of, other resources. The benefits of consulting i-Conc for L2 writing went beyond simply helping the participants’ problem solving to potentially facilitating their language acquisition. Input-feedback interactions with the reference suite prompted the participants to carry out robust meaning negotiations in their efforts to verify their intuitive hypotheses and to venture beyond their current linguistic repertoires.\nParticipants acted on these potential benefits somewhat differently. Case studies and cross-case analyses demonstrated complex interactions between the participants’ individual traits and goals, the educational contexts for which they were writing, and their perceptions and evaluations of particular affordances provided by i-Conc. These findings imply that to build meaningful cognitive partnerships with reference tools, L2 writers should receive progressive guidance on principles for effective reference resource consultation along with training in strategies for using different types of resources, contingent on individuals’ abilities and ongoing needs arising from their macro and micro contexts for writing and for language learning.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".