Classroom Constructivism Inventory: Informing Teaching Practices
Bibliographic record
Abstract
This study addresses the need to assess critical thinking in P-12 teaching by developing the Classroom Constructivism Inventory (CCI), a tool designed to assess constructivist teaching practices. Constructivism, which emphasizes active student involvement and the construction of knowledge, has been linked to positive educational outcomes. Despite its benefits, the diverse implementation of constructivism complicates its evaluation. The CCI was created to provide a construct-valid and user-friendly instrument for assessing the presence and effectiveness of constructivist elements in classrooms. Grounded in the theories of Piaget and Vygotsky, the CCI measures adherence to constructivist principles, such as student-centered activities, democracy in classroom interactions, professionalism, and the development of inquiry skills. The instrument was tested with two groups of teachers: one trained in constructivist methods and a control group with traditional training. Results demonstrated the CCI’s subscales’ reliabilities (pretest Cronbach’s αs = .79 - .83), good test-retest reliability (rExperimental = .53; rControl = .70), and its ability to distinguish between the teaching practices of the two groups. Teachers trained in constructivist methods led more student-centered activities and encouraged greater use of inquiry skills. The findings support that the CCI can validly measure constructivist teaching in real classroom settings, providing educators with a practical tool to enhance and evaluate constructivist teaching practices. Further validation with a broader range of school faculty is recommended.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".