Leveraging Technology to Promote Assessment for Learning in Higher Education
Bibliographic record
Abstract
Assessment for learning (AFL) is a highly effective strategy for promoting student learning, development and achievement in higher education (Falchikov, 2003; Kirby & Downs, 2007; Nicol & Macfarlane-Dick, 2006; Rust, Price, & O’Donovan, 2003; Vermunt, 2005). However, since AFL relies on continuous monitoring of student progress through instructor feedback, peer collaboration, and student self-assessment, enacting AFL within large-group learning formats is challenging. This paper considers how technology can be leveraged to promote AFL in higher education. Drawing on data from students and instructors and recommendations from an external instructional design consultant, this paper documents the process of pairing technology and AFL within a large-group pre-service teacher education course at one Canadian institution. Recommendations for the improvement of the web-based component of the course are highlighted to provide practical suggestions for instructors to evaluate their own web-based platforms and improve their use of technology in support of AFL. The paper concludes with a discussion of areas for continued research related to the effectiveness of this pairing between assessment theory and technology.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".