Evolving our Understanding of Technology-Integrated Assessment: A Review of the Literature and Development of a New Framework
Bibliographic record
Abstract
In this paper, we review the literature on technology in assessment in higher education and compare how the literature aligns with the assessment in a digital world framework (Bearman et al., 2022). We found themes in the literature that were not present in the framework (e.g., academic integrity and faculty workload) and constructs in the framework not evident in the literature (e.g., future self and future activities). Additionally, we consider other gaps in both the framework and the literature evident in day-to-day practices and government legislation or mandates, such as considering legal or ethical aspects of duty of care and the integration of Indigenous worldviews. We then developed the technology-integrated assessment framework to help instructors and administrators consider a broader range of constructs when planning assessment strategies in technology-integrated learning environments and to serve as a basis for further investigation into how the different constructs within the framework contribute to how we design, implement, and teach about assessment in digital learning environments today. We present an introduction of this technology-integrated assessment framework and discuss future research goals and opportunities.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".