Technology enhanced assessment: Ottawa consensus statement and recommendations
Bibliographic record
Abstract
INTRODUCTION: In 2011, a consensus report was produced on technology-enhanced assessment (TEA), its good practices, and future perspectives. Since then, technological advances have enabled innovative practices and tools that have revolutionised how learners are assessed. In this updated consensus, we bring together the potential of technology and the ultimate goals of assessment on learner attainment, faculty development, and improved healthcare practices. METHODS: As a material for the report, we used the scholarly publications on TEA in both HPE and general higher education, feedback from 2020 Ottawa Conference workshops, and scholarly publications on assessment technology practices during the Covid-19 pandemic. RESULTS AND CONCLUSION: The group identified areas of consensus that remained to be resolved and issues that arose in the evolution of TEA. We adopted a three-stage approach (readiness to adopt technology, application of assessment technology, and evaluation/dissemination). The application stage adopted an assessment 'lifecycle' approach and targeted five key foci: (1) Advancing authenticity of assessment, (2) Engaging learners with assessment, (3) Enhancing design and scheduling, (4) Optimising assessment delivery and recording learner achievement, and (5) Tracking learner progress and faculty activity and thereby supporting longitudinal learning and continuous assessment.
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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 | 0.357 | 0.450 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.022 | 0.017 |
| Research integrity | 0.028 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".