Toward Better Goal Clarity in Instruction: How Focus On Content, Social Exchange and Active Learning Supports Teachers in Improving Dialogic Teaching Practices
Bibliographic record
Abstract
Goal clarity is an essential element of classroom dialogue and a component of effective instruction. Until now, teachers have been struggling to implement goal clarity in the classroom dialogue. In the present study, we investigated the classroom practice of teachers in a video-based intervention called the Dialogic Video Cycle (DVC) and compared it to the classroom practice of teachers in a traditional control group. We conducted video analysis (N = 20 lessons) of teaching practices at the beginning (pre-test) and at the end of the school year (post-test). Furthermore, we performed video analysis of intervention group teacher discussions during DVC meetings (N = 6 meetings). Comparative analysis between groups revealed changes in teaching practices towards better goal clarity for DVC teachers in comparison to the traditional control group. In-depth analysis of teacher discussions during DVC meetings showed that teachers continuously focused on goal clarity as the content of teacher professional development (TPD). They shared learning experiences and were actively involved in TPD learning activities. The study illustrates how components of effective TPD programs (content focus, social and active learning) translated into redefining and changing the teaching practice.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".