Written-Based Progress Testing: A Scoping Review.
Bibliographic record
Abstract
PURPOSE: Progress testing is an increasingly popular form of assessment in which a comprehensive test is administered to learners repeatedly over time. To inform potential users, this scoping review aimed to document barriers, facilitators, and potential outcomes of the use of written progress tests in higher education. METHOD: The authors followed Arksey and O'Malley's scoping review methodology to identify and summarize the literature on progress testing. They searched 6 databases (Academic Search Complete, CINAHL, ERIC, Education Source, MEDLINE, and PsycINFO) on 2 occasions (May 22, 2018, and April 21, 2020) and included articles written in English or French and pertaining to written progress tests in higher education. Two authors screened articles for the inclusion criteria (90% agreement), then data extraction was performed by pairs of authors. Using a snowball approach, the authors also screened additional articles identified from the included reference lists. They completed a thematic analysis through an iterative process. RESULTS: A total of 104 articles were included. The majority of progress tests used a multiple-choice and/or true-or-false question format (95, 91.3%) and were administered 4 times a year (38, 36.5%). The most documented source of validity evidence was internal consistency (38, 36.5%). Four major themes were identified: (1) barriers and challenges to the implementation of progress testing (e.g., need for additional resources); (2) established collaboration as a facilitator of progress testing implementation; (3) factors that increase the acceptance of progress testing (e.g., formative use); and (4) outcomes and consequences of progress test use (e.g., progress testing contributes to an increase in knowledge). CONCLUSIONS: Progress testing appears to have a positive impact on learning, and there is significant validity evidence to support its use. Although progress testing is resource- and time-intensive, strategies such as collaboration with other institutions may facilitate its use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".