Flipped Learning in Grade 7 and 9 Mathematics
Bibliographic record
Abstract
This design-based study focused on supporting students in grade 7 and 9 math classes by implementing a flipped learning model. In this study the researchers explored the perceptions of teachers and students about the benefits and challenges of a technology-enhanced pedagogy such as flipped learning. The study was conducted from January to June 2021 with two junior high math classes in a charter school in Alberta with a specialization in English language learning, and at a time when classes were shifting between in-person and online learning frequently due to COVID-19. Through a design-based approach, teachers engaged in reflective conversations and journaling, students were surveyed about their experiences with the flipped learning approach, and data analytics were reviewed from the videos and embedded quizzes assigned as pre-learning activities. The Technological Pedagogical Content Knowledge (TPACK) framework was used to explore the relationship between technology, pedagogy, and content knowledge for designing flipped learning activities. The results from this study demonstrated the efficacy of the procedures, instruments, and value in extending the study to involve more classes and subject areas. Participants were satisfied with using the flipped learning approach for improving students’ engagement, agency, and mathematical understanding. Research in flipped learning can help inform teachers and schools in any teaching scenario whether in person, when teaching online, in blended learning environments, and when employing emergency remote learning.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".