Implementing a Flipped Learning Approach With TPACK in Grades 6 to 9
Bibliographic record
Abstract
In this design-based study, a flipped learning approach using audio-visual resources as prelearning activities was examined in grades 6, 7, and 9 with four teachers and 65 students over one school year. The purpose of this study was to explore the implementation of a technology-enhanced pedagogy in science, math, and social studies. The implementation was sequenced to provide students who were also learning the English language with an opportunity to practice engaging with curriculum concepts through viewing prelearning videos with language tailored by the teacher and with embedded questions, prior to in-classroom learning activities. The technological, pedagogical, and content knowledge (TPACK) framework was used to inform the instructional design for the flipped learning activities. Monthly teacher-researcher professional learning sessions were held, and data were gathered from teachers’ reflections and a student survey. Results indicated that teachers had more class time to support students with enrichment, remediation, small group work, and active learning. Students reported that the prelearning video activities benefited their learning and complemented in-class learning activities. This study serves to inform teachers and schools considering implementation of flipped learning to support students’ understanding of content knowledge and English language learning, and researchers studying designs using flipped learning sequences.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| 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".