Learning with Peers in Higher Education: Exploring Strengths and Weaknesses of Formative Assessment
Bibliographic record
Abstract
Implementing formative assessment strategies represents a challenge for higher education institutions. As they are frequently adopted only to support summative assessment and final grading, this study aims to investigate the most effective formative assessment strategies for higher education. It emphasizes the features of peer- and group-assessment, underlining strengths and weaknesses of both formative assessment strategies. Additionally, this study investigates the relationship between metacognitive and evaluative formative assessment aspects to support students’ learning processes and highlights the connection between formative and summative approaches. In the academic year 2023–2024, 240 higher education students were involved in a four-stage mixed-method study, alternating peer- and group-assessment strategies split in two steps focused on, respectively, metacognitive and evaluative aspects. Qualitative and quantitative data were collected after each stage. The findings revealed that students preferred the group-assessment and that the metacognitive formative assessment helped them improve their learning and prepare for the final test with summative assessment. Regarding policy implications, on the basis of this study, higher education institutions should improve instructor capacity to integrate formative assessment activities in their courses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".