Formative Learning Assessment with Online Quizzing: Comparing Target Performance Grade and Best Performance Grade Approaches
Bibliographic record
Abstract
Background: Using online low stakes MCQ quizzing as a formative learning method is common in many introductory courses; allowing for rapid student feedback. Purposes: A comparative study oftwo different approaches for administering low stakes multiple choice quizzes as tools to enhance formative learning in a large-lecture introductory marketing course was undertaken.Methodology/Approach: The sample population was 490 students drawn from two separate cohorts (Fall, n=172; and Winter, n=318). Both cohorts were subjected to 8 sets of quizzes. The Fall cohort’squizzes were scored on the basis of a best performance grade (BPG) while the Winter cohort’s were scored employing a target performance grade system (TPG). Learning related outcomes measuredincluded: overall course percentage grades, scores on midterm and final examinations, performance on an alternative exercise, practice exam performance, class participation activity, and time spent onthe learning management system (LMS). ANCOVA and MANCOVA analyses were undertaken to compare the two treatments using major, university experience, number of weekly course meetings,number of hours on the LMS, and class participation as covariates. Findings: The results indicated that the TPG cohort performed better than the BPG cohort on the final examination and overall coursegrades. The results were statistically significant. They also had higher first attempt scores on weekly quizzes, though not all results were significant. Discussion: The findings indicate that online quizzingscored using a “Targeted Performance Grade” approach is a more beneficial motivation for formative learning than scoring with a “Best Performance Grade” approach.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".