The effectiveness of student response systems on engagement and achievement in high school science instruction
Bibliographic record
Abstract
The use of technology to extend and change the daily experience of education in traditional classrooms continues to rise.The Alberta government is committed to using technology in classrooms and has provided direct funding to install an LCD projector and an interactive whiteboard in nearly every classroom in the province .Motivating this change is a common belief that technology will engage students and transform the classroom into a learning environment in which younger generations can underst and and excel.This study evaluated those beliefs in the context of another common technology; student response systems.A comparison was made between science units taught with and without these systems to answer the following question: Do student response systems increase class engagement and summative achievement?This study concluded that in public high school science classrooms these systems increased some measures of engagement but did not significantly improve student exam scores.I offer my sincerest gratitude to my supervisor, Dr. Bryan Hartman, who has supported me throughout my thesis.The journey turned out to be rather long, but with his patience and help I was able to complete my work while moving twice and taking on two new schools in different school divisions.I could not have hoped for a more supportive or dedicated supervisor.I also wish to thank Dr. Peter MacMillan for his advice and assistance in helping me understand my data in a much deeper and more meaningful fashion .His quick responses and insights were all that more precious knowing there were given while on sabbatical in Ireland and France.
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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 | 0.023 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".