Implementation of embedded assessment in maker classrooms: challenges and opportunities
Bibliographic record
Abstract
Purpose As maker-centered learning grows rapidly in school environments, there is an urgent need for new forms of assessment. The purpose of this paper is to report on the development and implementation of tools to support embedded assessment of maker competencies within school-based maker programs and describes alternative assessment approaches to rubrics and portfolios. Design/methodology/approach This study used a design-based research (DBR) method, with researchers collaborating with US middle school teachers to iteratively design a set of tools that support implementation of embedded assessment. Based on teacher and student interviews, classroom observations, journal notes and post-implementation interviews, the authors report on the final phase of DBR, highlighting how teachers can implement embedded assessment in maker classrooms as well as the challenges that teachers face with assessment. Findings This study showed that embedded assessment can be implemented in a variety of ways, and that flexible and adaptable assessment tools can play a crucial role in supporting teachers in this process. Additionally, though teachers expressed a strong desire for student involvement in the assessment process, we observed minimal student agency during implementation. Further study is needed to investigate how establishing classroom culture and norms around assessment may enable students to fully participate in assessment processes. Originality/value Due to the dynamic and collaborative nature of maker-centered learning, teachers may find it difficult to provide on-the-fly feedback. By employing an embedded assessment approach, this study explored a new form of assessment that is flexible and adaptable, allowing teachers to formally plan ahead while also adjusting in the moment.
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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 | 0.068 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".