Using LEGO® Six Bricks® as an educational resource to address challenges pre-service teachers face during school-based teaching practice
Bibliographic record
Abstract
Pre-service teachers in South Africa frequently encounter challenges during school-based teaching practice, including a persistent theory-practice gap, insufficient mentorship, difficulties in classroom management, and struggles with curriculum differentiation. This study investigates the use of LEGO® Six Bricks®—a play-based, low-cost, and scalable educational resource—as a pedagogical tool to address these recurring challenges. Adopting a Participatory Action Learning and Action Research (PALAR) approach within a transformative paradigm, six final-year Foundation Phase students from a South African university were purposively selected to integrate LEGO® Six Bricks® into their teaching practice placements. Data collection was guided by PALAR and framed by Kolb’s experiential learning theory, enabling iterative cycles of planning, implementation, reflection, and adaptation. Key findings indicate that the use of Six Bricks® not only enhanced learners' engagement and pre-service teachers’ confidence, but also significantly contributed to developing professional identity, building rapport with mentor teachers, and facilitating inclusive pedagogical practices. Moreover, participants demonstrated increased pedagogical agency through curriculum innovation, classroom management strategies, and formative assessment techniques. The study concludes that experiential engagement with playful resources such as Six Bricks® fosters reflective practice and bridges both the theory-practice and belief-practice gaps in teacher education. It recommends structured training, reflective mentorship, and continued research into contextually relevant, low-threshold pedagogical tools to better prepare student teachers for the complexities of South African classrooms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".