Examining physiological and self‐report indicators of empathy during learners’ interaction with a queer history app
Bibliographic record
Abstract
Abstract Mobile apps take advantage of the ubiquity of mobile phones and can be used to share unique pedagogical experiences with multimedia content not yet available in curriculums. This preliminary study used a quasi‐experimental pretest‐posttest design to examine changes in self‐reported empathy toward sexual orientation and gender identity (SOGI) minority people. We also report on the associations between gender and a physiological measure of emotional activation, skin conductance level (SCL), on self‐reported empathy. The main results of this study that examined 57 undergraduate students at a Canadian University whom interacted with a queer history app individually were the following: Preliminary evidence that (1) students’ empathy toward SO and GI minorities can be measured using a modified version of the Scale of Ethnocultural Empathy (SEE). (2) Statistically significant increases in empathy toward SO and GI minorities pre to post app interaction. (3) Students’ pre‐ and post‐empathy levels were statistically significantly higher toward SO than GI minorities. (4) Female students had statistically significantly higher self‐reported empathy toward SO and GI minorities than males. (5) Male students had statistically significantly higher SCL than females. (6) Statistically significant interaction between SCL grouping and questionnaire administration on GI minority empathy. Findings and implications are discussed in lieu of the contributions that mobile apps can play to support social change, in particular, by fostering empathy. Practitioner Notes What is already known about this topic Evidence that mobile apps are effective learning tools. Evidence that mobile apps are emotionally engaging. Evidence that education can help dispel ignorance, known to contribute to homophobia and transphobia and promote empathy. Empathy has an emotional component. Empathy can be measured as a general trait or toward specific groups. Queer history is excluded from many formal educational curriculums. What this paper adds Preliminary evidence that a self‐report tool can be used to measure empathy toward both sexual orientation (SO) and gender identity (GI) minorities. Preliminary evidence that undergraduate students’ empathy toward SO and GI minorities increased after interacting with the Edmonton Queer History (EQH) app. Preliminary evidence that students’ empathy toward SO was statistically significantly higher than it was toward GI minorities at both pre and post tests. Preliminary evidence that female students had statistically significantly higher self‐reported empathy toward SO and GI minorities than males. Preliminary evidence that male students had statistically significantly higher skin conductance levels (SCL) than females. Preliminary evidence of a statistically significant interaction between SCL grouping and questionnaire administration on GI minority empathy. Implications for practice and/or policy Mobile apps can be used to teach students about queer history and these interactions may lead to increases in empathy. Educators can supplement their curriculum with educational technologies, such as mobile apps, to fill important gaps. Educators should consider gender differences in teaching topics that may or are intended to inspire empathy. Educators should not assume that empathy toward SO and GI minorities are the same.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".