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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".