Empathy Mapping: Bridging cultural and linguistic divides in international online education
Bibliographic record
Abstract
The concept of empathy, commonly used in user experience (UX) design, has gained traction in distance education communities (Matthews et al., 2017). Empathy offers designers insight into users and their contexts (Neubauer et al., 2017) and helps designers "understand how instruction would be experienced" (Parrish, 2006), thus improving the overall outcome (Lewis & Contrino, 2016; Neubauer et al., 2017; Parrish, 2006). UX designers use a visualization tool called empathy mapping to chart information about their users. Empathy maps are used at the outset of a project and continue to evolve and inform the project as new data emerges. This paper reviews literature from the fields of instructional design, distance education, and user experience design to describe the problems in current distant education design practices; to argue for the practice of empathy in distance education design; and finally, to describe empathy mapping and how it can sensitize instructors to students' circumstances, remove instructor bias, and help instructors make evidence-based decisions in the design and delivery of their courses.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".