The Development and Initial Validation of an Empathy Scale for Higher Education Instructors
Bibliographic record
Abstract
Empathy is an essential component of interpersonal relationships as it promotes a feeling or understanding of another’s emotions and can produce a response leading to altruistic behavior. Empathy demonstrated in higher education could impact student success; however, a scale that measures empathy in higher education instructors does not exist. The purpose of this study was to develop a scale to measure empathy in higher education instructors and to perform initial validation for its use in this population. The scale development started with qualitative research to identify how empathy is perceived by faculty who teach in higher education. It was found that instructors of any academic rank define empathy as multidimensional and demonstrate empathy using both cognitive and affective approaches. A theoretical model that illustrates the potential value of empathy as a prosocial behavior on student outcomes also informed the scale item development. Finally, current validated scales were used for item development, including the Toronto Empathy Questionnaire (TEQ) and the Interpersonal Reactivity Index (IRI). The developed scale underwent exploratory (EFA) and confirmatory factor analysis (CFA) along with convergent and discriminant validation. Although the scale generated from the EFA did not demonstrate a good model fit with the CFA, post hoc modifications using modification indices generated a good model fit with a 2-factor, 15-item scale (CFI=0.964; RMSEA = 0.04). The newly developed 2-factor, 15-item empathy scale provides a strong foundation for a validated tool to measure empathy in higher education instructors and will contribute to future teacher-student relationship research.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".