What does Open Science mean for Educational Technology Research? Challenges, Opportunities, and a Call for Research
Bibliographic record
Abstract
Educational technology (EdTech) research should champion the values of open science in order to be robust, methodologically rigorous, collaborative, inclusive, and transparent. ‘Open science’ is, broadly, an approach to scientific scholarship that adopts tools to promote openness, mitigate against bias, enhance opportunity for collaboration, and reduce questionable research practices. EdTech research often involves collaborative empirical work with partnerships between academia, industry, and policymakers, often with competing stakeholder timelines, agendas, and expectations. It also employs a diversity of methodologies and epistemologies, given the broad goals of EdTech as a field. Taken together, these unique features of EdTech research mean there is a distinct set of contextual and methodological challenges for engagement with open science tools and initiatives. Here, we write as a collective of academics, scholars, and industry representatives who all work in the EdTech space and hope to envisage a future for how EdTech researchers can meaningfully engage in calls to ‘open up’ science. We share insights from a stakeholder workshop event, hosted by CERES (Connecting the Educational Technology Research Ecosystem). We summarize four main open science practices: (1) open data, (2) study pre-registration, (3) positionality and conflict of interest statements, and (4) CRediT taxonomy of contributorship. For each of the practices, we summarize the key opportunities for EdTech research and highlight the unique disciplinary challenges that academics and industry must negotiate when integrating open science into EdTech research. We show how open science is an ally to EdTech research, but careful consideration about its implementation is needed.
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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 | 0.280 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.031 | 0.138 |
| Scholarly communication | 0.091 | 0.116 |
| Open science | 0.008 | 0.041 |
| Research integrity | 0.039 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".