Qualifying with Different Types of Quizzes in an Online EFL course: Influences on Perceived Learning and Academic Achievement
Bibliographic record
Abstract
This quasi-experimental study explored how different online exam types differentiate learners’ academic achievement and perceived learning. The participants comprised 95 undergraduate students enrolled in an English course at a Turkish university in three groups, each taking a different type of quiz: with multiple-choice, open-ended, and mixed type questions. The results indicated that the academic achievement of the students in multiple-choice and open-ended groups increased and that quiz results improved the most for the multiple-choice group relative to the other groups. The study found a moderate level of significant relationship between cognitive and affective perceived learning and multiple-choice quiz scores. In addition, the study found a weak level of significant relationship between cognitive and affective perceived learning and mixed-design quiz scores, and between cognitive learning and the academic achievement scores of the mixed-design group. Semi-structured online interviews undertaken to further explain the quantitative data displayed positive influences of the different types of quizzes in terms of study behaviors and satisfaction. The findings of this study are expected to shed light for practitioners aiming to use different online assessment 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".