Integrated testlets in optics and photonics: an assessment tool suitable for textbook and online delivery
Bibliographic record
Abstract
Integrated testlets are a means to assess a student’s understanding of complex knowledge through a set of scaffolded questions within an answer-until-correct format, and with grades that can, if desired, be awarded according to the number of attempts made by the student. Integrated testlets have been delivered to students at several universities in Canada, in physics, chemistry, and biology. In comparison with traditional multiple-choice-based assessments that ask items that are wholly independent of each other, an integrated testlet purposefully poses a set of integrated and dependent items that build on each other. This allows for the assessment and formative learning of deeper and more interconnected aspects of the course material. This is important in all STEM disciplines, and especially beneficial for cumulative and interdisciplinary fields such as optics and photonics. In the past few years we have for the first time extended integrated testlet delivery to an online format using the WeBWorK delivery system, and more recently included these within endof-chapter questions throughout a textbook in optics as part of our significant update and revision of the classic, internationally-known, Introduction to Optics text by F. L. Pedrotti, L. S. Pedrotti, and L. M. Pedrotti. For students this provides the benefits of integrated testlets as originally conceived, while also connecting topics within the book. This has encouraged us as authors to use deliberate and mindful composition practices, and advanced our skills in conveying the breadth and depth of the many concepts within optics and photonics.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.025 |
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".