Exploring the Use of Lesson Study with Six Canadian Middle-School Science Teachers
Bibliographic record
Abstract
This qualitative case study explores the use of lesson study over a ten-week period with six Ontario middle school science teachers.The research questions guiding this study were: (1) How does participation in science-based lesson study influence these teachers': (a) science subject matter knowledge (science SMK), (b) science pedagogical content knowledge (science PCK), and (c) confidence in teaching science?, and (2) What benefits and challenges do they associate with lesson study?Data sources for this study were: teacher questionnaires, surveys, reflections, preand post-interviews, and follow-up emails; researcher field notes and reflections; preand post-administration of the Science Teaching Efficacy Belief Instrument; and audio recordings of group meetings.The teachers demonstrated limited gains in science SMK.There was evidence for an overall improvement in teacher knowledge of forces and simple machines, and two teachers demonstrated improvement in over half of the five scenarios assessing teacher science SMK.Modest gains in teacher science PCK were found.One teacher expressed more accurate understanding of students' knowledge of forces and a better knowledge of effective science teaching strategies.The majority of teachers reported that they would be using three-part lessons and hands-on activities more in their science teaching.Gains in teacher pedagogical knowledge (PK) were found in four areas: greater emphasis on anticipation of student thinking and responses, recognition of the importance of observing students, more intentional teaching, and anticipated future use of student video data.Most teachers reported feeling more confident in teaching structures and mechanisms, and iii attributed this increase in confidence to collaboration and seeing evidence of student learning and engagement during the lesson teachings.Teacher benefits included: learning how to increase student engagement and collaboration, observing students, including video data, observing colleagues teach, time to collaborate, plan, and reflect, teaching the same lesson to two classes, more intentional teaching, and increasing social interactions.Teacher challenges included: teacher unfamiliarity with the students being taught, time spent taking part in lesson study, teachers in the role of observers, and impact of observers and videotaping on students and teachers during lesson enactments.Next, I must acknowledge what a wonderful supervisor Azza Sharkawy has been.Despite being very busy herself, Azza has always made time to meet with me and to carefully read all the many words that I have written.Azza has been especially awesome in the final pushes to submission and completion of corrections.She has always been positive, enthusiastic, and very supportive.At the same time she has not been afraid to push me when needed, and this dissertation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".