Developing the Technology-Integrated Assessment Framework
Bibliographic record
Abstract
The purpose of this paper is to describe the development of a new framework for understanding technology-integrated assessment in higher education based on a review of the literature using the assessment design in a digital world framework (Bearman et al., 2022) as a lens. Our review (Madland et al., 2024) revealed both congruities and incongruities between the literature and the framework, leading to the need for further work to accurately conceptualize technology-integrated assessment. In this article, we contribute to the literature on technology-integrated assessment in higher education by proposing the technology-integrated assessment framework. This paper marks an important step in extending our understanding of the factors influencing instructors who integrate technology into their assessment practice and promoting ethical and equitable approaches to technology-integrated assessment in higher education.
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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 | 0.036 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".