Login / Signup

Do sentiments of professors feedback change after migrating from in-person to online modalities? Pre- and during COVID-19 experience.

Lilia Carolina Rodríguez-GalvánAsad AbbasAnil Yasin ArBeatriz Garza-GonzálezPatricia Esther Alonso-Galicia
Published in: Universal access in the information society (2022)
The COVID-19 pandemic forced higher education institutions to alter how they offer classes at an unprecedented pace. Due to ambiguities and lockdown restrictions, the transition phase negatively impacted students' and professors emotions. As a result, lecturers had to cope with unfamiliar online class teaching responsibilities and develop new teaching dynamics. This work aims to analyze one of the most adversely affected procedures of teaching, the written feedback provided to students. This research strives to explore whether the professors' feedback style altered from face-to-face education to online education on digital platforms during the COVID-19 restrictions. This exploratory-design study uses a mixed methodology to explain the subject on hand based on data collected from 117 undergraduate students. Sentiment lexicographers are utilized to address and identify the emotions expressed in the texts. Trust was the most frequent emotion expressed in face-to-face and online courses. It is also observed that the sentiments of joy and sadness changed significantly among online and face-to-face groups based on the professors' feedback style and approach. Finally, the study reveals that the joy words and the sadness words associated with the learning process are the most commonly utilized sentiments. This study suggests that when the courses transitioned from face-to-face to online learning, the professors' feedback changed to a more positive feeling that expressed appreciation for the students' work, encouraging them to strive for their complete academic development, and usher them into a better learning environment.
Keyphrases
  • health information
  • social media
  • healthcare
  • medical students
  • coronavirus disease
  • quality improvement
  • autism spectrum disorder
  • high school
  • machine learning
  • deep learning