Formative E-Assessment: A Qualitative Study Based on Master’s Degrees
Bibliographic record
Abstract
Formative assessment is a strategy that optimizes the learning process at any educational level, however its use is not very frequent as literature revision shows. In this paper, we analyse the use of formative assessment in online postgraduate studies (masters) in Spanish universities. Our sample was 31 online master’s degrees coordinators and we analyse the results obtained from a questionnaire with open questions using NVIVO software. Through qualitative analysis of the information supported by cross-queries of codes and attributes, we have considered formative assessment according to fields of knowledge, the type of digital tools used and the main difficulties identified. In this type of online masters, our data show that most of the teachers use this type of formative e-assessment to provide feedback to their students and as part of the final marks of the courses, too. So these results are relevant to understand the E-assessment strategies for master’s degrees. Finally, the main limitation of the study is the fact that it uses a sample limited to the geographical context of Spain. Nevertheless, these data are representative of E-assessment in Spanish master’s degrees and may be of interest for future research, for comparative studies in other contexts and even with face-to-face or blended-learning master’s degrees.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".