Computer familiarity and test performance on a computer-based cloze ESL reading assessment. Teaching English with
Bibliographic record
Abstract
d.modo @ alumni.ubc.ca Researchers have raised questions regarding the connection between learner familiarity with computers and performance on computerized tests virtually since interest arose in studying the applicability of computers for assessment purposes. However, despite this longstanding attention, at present, there has been a surprising lack of research that explores the connection between computer familiarity and performance on computerized tests that fall outside of the traditional multiple-choice discrete-point tests that have historically predominated in the fielf of testing and assessment. The current study aims to address this gap in previous research by examining the relationship between computer familiarity and computer-based test performance on a computer-based test of second language reading that is integrative rather than discrete-point. The study investigated the online reading ability of ESL students from one secondary school in a large city in western Canada (61 females and 59 males in the sample, ages 13-19, M=15.73). The students responded to a questionnaire about their computer familiarity and then completed an online multiple-choice cloze test. Contrary to other most other findings based on discrete-point tests, the results revealed that the familiarity variables do account for a small but significant amount of the variability in the computer-based test scores. 1.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".