Communication Apprehension and Psychological Well-Being of Students in Online Learning
Open accessAbstract
The current pandemic has modified how education, learning, and technology interact with one another inside universities. The usage of technology for instructional purposes raises the question of whether learning that happens in an online environment is as effective as traditional classroom models. Within this context, this study explores the psychological well-being of students during the COVID-19 pandemic, using an online cross-sectional survey. Data were collected from 246 university students currently studying at a private university in India. Hierarchical regression analysis and structural equation modelling were used to study the mediating effects between communication apprehension, perceived learning, and psychological well-being under the moderating effects of intention to use social media and psychological stress. Results show that higher intentions to use social media alleviated the negative effects of communication apprehension on perceived learning. Interestingly, it was also found that perceived learning had a significant positive relationship with psychological well-being when students experienced higher levels of psychological stress (eustress). Based on the technology acceptance model (TAM) and the transactional theory of stress and coping, we attempt to integrate the findings related to these theories, which can be considered distinct to previous studies. Implications, limitations, and future directions for research and practice have also been discussed.
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Students in virtual learning environments experience elevated levels of stress and anxiety, particularly when required to interact with peers and instructors in online formats that lack clear social cues and predictable norms.
“Agrawal and Krishna (2021) have noted that students in virtual learning environments experience elevated levels of stress and anxiety, particularly when required to interact with peers and instructors in online formats that lack clear social cues and predictable norms.”
From AI Literacy in the Communication Basic Course: Insights on Embedding, Motivation, and Reducing Uncertainty (2026) · cites this paper as Agrawal and Krishna, 2021
The Introduction links communication apprehension to stress and anxiety about interacting with classmates and teachers, and Methods §3.2.2 describes nervousness during online group discussions. But the paper does not attribute that anxiety to unclear social cues or unpredictable norms. Its Discussion instead suggests that reduced nonverbal cues in online communication can lessen apprehension for shy students. (LLM, conf=0.88)
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- Authors
- Agrawal, S., & Krishna, S. M.
- Affiliations
- Chaoyang University of Technology (TW)
- Vellore Institute of Technology University (IN)
- Corresponding author
- Krishna, Shwetha M.
- Venue
- Behavioral Sciences
- Publisher
- MDPI AG
- Published
- 2021-10-23
- Volume
- 11
- Issue
- 11
- Pages
- 145
- ISSN
- 2076-328X (ISSN-L)
- PMC
- PMC8614880
- OpenAlex
- W3208590130
- Cited by
- 41
- References
- 95
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 9 · 2023: 7 · 2022: 9
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Behavioral Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Student Stress and Coping
- Social Sciences › Education › Education during COVID-19 pandemic
- Social Sciences › Psychology › Clinical Psychology › COVID-19 and Mental Health
- Keywords
- communication apprehension, psychological well-being, online learning, COVID-19 pandemic, university students, perceived learning, psychological stress, eustress, technology acceptance model, transactional model of stress and coping, mediating effect
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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