Leveraging the Relationship Between Assessment, Learning, and Educational Technology
Bibliographic record
Abstract
The increase in educational technology due to the COVID-19 pandemic required teachers to alter their assessment strategies. Having to pivot into a new learning environment was difficult, and conducting assessments posed additional challenges. This research, based on qualitative secondary analysis, used the substitution, augmentation, modification, and redefinition framework as a reflexive lens to explore how teachers leveraged technology to support their assessment practices. Data were collected in an Ontario school district where devices were provided to teachers and students in Grades 7 to 10, and teachers were invited to participate in an eight-week professional learning community to share lessons learned about assessment practices with technology. The research involved 61 teachers from 24 schools who shared their experiences through pre- and post-intervention surveys, discussion boards, and arts-informed data. The findings reveal the importance of fostering community in professional learning and expanding assessment practices, including student choice, meaningful feedback, differentiated instruction, and a learning culture. This study highlights the need for continual learning in the use of technology for assessment purposes, whether in person or online. Interweaving assessment and technology denotes the possibilities inherent in the ongoing learning about technology in the field of education.
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".