A digital game‐based assessment of middle‐school and college students’ choices to seek critical feedback and to revise
Bibliographic record
Abstract
Abstract A major goal of contemporary education is to teach students how to learn on their own. Assessments have largely lagged behind this goal, because they measure what students have learned and not necessarily their learning processes. This research presents Posterlet, an assessment that collects evidence regarding the strategies that students choose while learning on their own. Posterlet is an educational game‐based assessment that measures two design thinking choices: students’ choices to seek critical (ie, negative) feedback and to revise their work while they learn graphic design principles through creating posters. This research also presents an examination of students’ choices to seek feedback and to revise, as well as of students’ learning outcomes based on these choices. This game‐based assessment approach is empirically validated with three research studies sampling nearly 300 middle‐school and college students who played Posterlet and completed a posttest. Results show that the game helps students learn, as students who play the game before completing the posttest learn more graphic design principles than students who only complete the posttest. Moreover, the choices to seek critical feedback and to revise can predict learning and can be used as valid outcome measures for learning. Findings can be used in developing and evaluating models of instruction and assessment that may help students make informed learning choices. A discussion of present and future trends in theory regarding digital feedback environments is also included.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".