How assessing reading comprehension with multiple-choice questions shapes the construct: a cognitive processing perspective
Bibliographic record
Abstract
This article provides renewed converging empirical evidence for the hypothesis that asking test-takers to respond to text passages with multiple-choice questions induces response processes that are strikingly different from those that respondents would draw on when reading in non-testing contexts. Moreover, the article shows that the construct of reading comprehension is assessment specific and is fundamentally determined through item design and text selection. The data come from qualitative analyses of 10 cognitive interviews conducted with non-native adult English readers who were given three passages with several multiple-choice questions from the CanTEST, a large-scale language test used for admission and placement purposes in Canada, in a partially counter-balanced design. The analyses show that: • There exist multiple different representations of the construct of ‘reading comprehension’ that are revealed through the characteristics of the items. • Learners view responding to multiple-choice questions as a problem-solving task rather than a comprehension task. • Learners select a variety of unconditional and conditional response strategies to deliberately select choices; and • Learners combine a variety of mental resources interactively when determining an appropriate choice. These findings support the development of response process models that are specific to different item types, the design of further experimental studies of test method effects on response processes, and the development of questionnaires that profile response processes and strategies specific to different item types.
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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 | 0.027 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".