Automated Grading of Scratch Card Based Immediate Feedback Assessment Technique (IFAT)
Bibliographic record
Abstract
Immediate Feedback Assessment Technique® (IFAT®) provides opportunities for students to answer a multiple-choice question until the correct answer is revealed. This process is claimed to promote self-learning while testing, reduce test anxiety, and provide teachers with partial knowledge recognition. Scratch card-based IFAT® was developed by Epstein and made available to the teaching community through a commercial channel. Despite several advantages, grading using IFAT® cards for a final exam of a typical first-year course requires several manual hours. To ease this tedious process, this paper discusses an automated approach for grading exams conducted using scratch card-based IFAT. An algorithm that uses the OpenCV image processing library that can detect scratched, unscratched, and varying degrees of partially scratched boxes with high accuracy has been developed and implemented to create a logical representation of the IFAT® card. Our tool demonstrated a high degree of precision (>99%) for an assessment involving 39 cards of 10 questions, each with 5 scratch boxes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".