THE SPEAKING SECTION OF THE TOEFL IBT™ (SSTIBT): TEST‐TAKERS' REPORTED STRATEGIC BEHAVIORS
Bibliographic record
Abstract
ABSTRACT This study responds to the Test of English as a Foreign Language™ (TOEFL®) research agenda concerning the need to understand the processes and knowledge that test‐takers utilize. Specifically, it investigates the strategic behaviors test‐takers reported using when taking the Speaking section of the TOEFL iBT™ (SSTiBT). It also investigates how the reported strategic behaviors differed across integrated and independent tasks in the SSTiBT, as well as the relationship between test‐takers' reported strategic behaviors and their performance on the tasks as determined by their test scores. The participating students were 14 graduate and 16 undergraduate engineering students whose first language was Chinese. The results indicate that test‐takers reported using 49 separate strategies when completing the SSTiBT tasks. Of the five strategy categories, the metacognitive, communication, and cognitive strategies were proportionally the most frequently reported. The interrelationships among these three categories were negative. Undergraduates reported using significantly more communication strategies, whereas graduates reported using significantly more cognitive and affective strategies. No statistically significant differences were found in reported strategy use across proficiency levels. The integrated tasks were more alike with respect to reported strategy use than were the independent and integrated tasks. Furthermore, the integrated tasks elicited a wider variety of reported strategy use than the independent tasks. Overall, we found no relationship between the total number of reported strategic behaviors and total test score on the SSTiBT. We conclude that strategy use is integral to performing SSTiBT tasks and should therefore be considered as part of the construct of communicative performance. However, the relationship between strategy use and test performance is varied and is due to complex interactions among test‐taker characteristics, tasks, and contexts.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".