Student Co-Creation of Open Textbooks: Reflections on Power Dynamics and Building a Sense of Belonging in Higher Education
Bibliographic record
Abstract
Despite calls for social justice and inclusion in higher education, there is still growing structural inequality in terms of access to education, which extends to structural and economic oppression of marginalised groups. Student inclusion in design, creation and evaluation of curricula is lauded in research as essential for student belonging, with open textbook production as one way in which student co-creation is being explored. Yet, little work has been done to look at the challenges involved when traditional power dynamics are disrupted in partnering with students. Highlighting the collaborative endeavours that lecturers undertake with students in open textbook production and the challenges therein, this paper draws on Yuval-Davis’ (2011) work on student belonging and Fraser (2005) on social (in)justice, to explore the nexus of three complementary themes: open textbooks, students as partners, and student belonging in higher education. Data were derived from a set of interviews conducted with three open textbook authors at the University of Cape Town (UCT) on their efforts to foster co-creation practices with students in their classrooms as part of open textbook initiatives. Findings reveal that student co-creation of open textbooks has the potential to shift, or at the very least tilt, power balance and give students agency. Academics who undertook open textbook production with student co-creators respected students’ expertise and in turn, students felt a sense of value in their departments, which enabling a sense of belonging. We highlight how the reality of student co-creation is complex as academics have the intention to shift the traditional power dynamics between lecturer and student; however, examples here show this kind of transformation is gradual and continuous and often difficult to implement when the institutional culture remains hierarchical.
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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 | 0.006 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".