Technology enhanced assessment: Ottawa consensus statement and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.000 |
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 it