Section on Statistical Education – JSM 2011 Including Student Ability to Assess Learning with Other Assessment Tools
Bibliographic record
Abstract
Retrospective look at ten years of assessing introductory statistics courses over quarters. Introduces a “how sure are you of this answer ” question. Since Fall Quarter 1999, the authors have collected data from a required common final in introductory statistics and some finals in introductory psychology courses. After ten years we wonder whether there is some relationship between correct response and an individual student’s assessment of their ability to answer a particular question correctly. Our study considers continuity in exams and the usefulness of asking students to assess their own problem solving ability. For each of twenty questions on a common final in an introductory statistics course, students are asked to rate their personal ability to answer that particular question correctly. Responses are studied on a number of scales. One set of scales is designed to study particular topics in introductory classes. The second set of scales looks at the difficulty of the problems in terms of literacy, skill and reasoning required to answer. In an age requiring 'customer satisfaction ' we ask whether students are able to correctly utilize basic course skills and assess personal learning.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.151 | 0.127 |
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".