Co-creating curriculum with students: An exploration in designing interactive, multi-touch course eBooks
Bibliographic record
Abstract
The purpose of this study is to achieve a better understanding of how creating a course eBook can enhance students’ learning experiences and build a classroom community focussed on inquiry. Using a collaborative design process that our research team developed, 72 students in four post-secondary courses worked together to write, edit, research, and design media-rich eBooks related to the syllabus themes. Each class published an eBook on the iTunes store. The student writing teams learned how to develop relevant and compelling educational content including photo galleries, scrolling sidebars, popovers, animations, surveys, quizzes, videos, and their voice. Rather than being users or consumers of digital content, learners were guided and inspired to create an interactive chapter for their course eBook compilation, with an emphasis on meaningful integration of media and technology. While there are many innovative technological and pedagogical options to engage our students and build community, there are also considerable challenges with integrating technology in meaningful and inclusive ways within the context of a university classroom setting. This visual presentation will examine the affordances and constraints of using iBooks Author to co-create curriculum with students, specifically focusing on learner empowerment, technical challenges, and the development of an academic writing community.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".