Authors:

Nathan Bitner

Date Created:

Not specified

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About Paper:

With increasing globalization, employers expect fresh graduates to showcase intercultural competence and cross-cultural communication abilities. Research has identified that developing empathy can help young graduates to become mindful of cultural differences and behave appropriately to mitigate them. In this study, we intend to compare perspectives of empathy between science (N=1762) and nursing (N=187) undergraduates. The students in both groups completed an online self-paced module on intercultural collaboration and empathy. At the end of the module, students were required to complete a capstone reflection assignment. The responses of the students were analyzed using the Latent Dirichlet Allocation (LDA) topic modeling technique and prominent themes present in their responses were identified. Topic modeling is an unsupervised machine learning algorithm that identifies clusters known as topics based on the frequency co- occurrence of words in the documents of the textual collection. Each topic generated by the LDA model was qualitatively analyzed by researchers to identify emerging themes for science and nursing students. Moreover, Empatico's empathy framework, which categorizes empathy into behavioral, cognitive, and emotional areas was used to identify the associated empathy area with each theme that emerged. The results of the analysis revealed that science students' responses primarily contained instances of behavioral and cognitive empathy whereas nursing students mainly showed instances of emotional empathy along with cognitive and behavioral empathy. Overall, the results of the study indicate that nursing students express empathy in a more emotional manner than science students.

Source:

Purdue University / 2023

Topics:

No topics listed

Co-authors:

Nathan Bitner