Supporting Historical Inquiry by Design: The Implications of User Experience Design in Digital History
Bibliographic record
Abstract
My Master’s research explores the implications of user experience design (UXD) for history education in the Province of Ontario. I propose to integrate this established framework from the discipline of computer science to the development of digital resources intended for the history classroom. Long-standing efforts to shift history education away from content-based pedagogies have led Ontario and other jurisdictions to adopt historical inquiry into curriculum. Since the early 2000s, digital history has been proposed as an innovative means of engaging students in this type of knowledge construction. However, digital resources in history education often fail to meaningfully promote inquiry due to a decades-old shortfall: “Most web pages created by historians tend to be text-heavy and to contain few opportunities for innovative interactions by the user beyond a set of links to explore” (Vess, 2004, p. 386). Today, much of the discussion on digital history still revolves around its unrealized promise to engage students in historical inquiry. My research aims to shift the discursive focus away from the potential benefits of digital history and towards a practical creation process which could help realize these benefits in a Canadian context. First, a document analysis of existing digital history projects was undertaken, utilizing an operationalized lens of historical inquiry. This was concurrently accompanied by the creation of a new digital project mapping the Federal Indian Day Schools. A review of the foundations of UXD was also undertaken, contextualized by the previous explorations, which was synthesized into a new model for developing virtual resources in history education: the Digital History Design Process (DHDP). This research contributes to the classroom application of digital history by publishing a new interactive resource. It also contributes to academic literature by presenting a new framework for developing inquiry-rich resources.
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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 | 0.109 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".