The implementation of peer assessment as a scaffold during computer-supported collaborative inquiry learning in secondary STEM education
Bibliographic record
Abstract
Abstract Background Computer-supported collaborative inquiry learning (CSCiL) has been proposed as a successful learning method to foster scientific literacy. This research aims to bridge the knowledge gap surrounding the role of peers as scaffolding sources in CSCiL environments. The primary objective is to explicitly implement peer assessment as a scaffolding tool to enhance students' inquiry output in terms of research question, data, and conclusion. Additionally, students’ perceptions of peer assessment within CSCiL are explored. Results The study involved 9th and 10th-grade students from 12 schools (N = 382), exploring the effects of peer assessment with and without peer dialogue. The results highlight that while adjustments were more frequently made to the research question and data, adjustments to the conclusion showed significantly greater improvement. Furthermore, students’ perceptions of peer assessment during CSCiL were examined, revealing that students generally perceive peer assessment as fair and useful, and they accept it while being willing to make improvements based on the feedback. While students did not report experiencing negative feelings, they also did not report positive emotions from the process. Additionally, the study found that including a peer dialogue in the peer assessment process did not significantly impact the abovementioned findings. Conclusions This study enriches our understanding of peer assessment as a scaffolding tool in CSCiL, highlighting its potential to improve inquiry outputs and providing valuable insights for instructional design and implementation.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".