International Journal of Data and Network Science (Jan 2024)

Technology anxiety (technostress) and academic burnout from online classes in university students

  • Roberto Líder Churampi-Cangalaya,
  • Miguel Fernando Inga-Ávila,
  • Jesús Ulloa-Ninahuamán,
  • José Luis Inga-Ávila,
  • Madelyn Apardo Quispe,
  • Miguel Ángel Inga-Aliaga,
  • Francisca Huamán-Pérez,
  • Enrique Mendoza Caballero

DOI
https://doi.org/10.5267/j.ijdns.2023.9.005
Journal volume & issue
Vol. 8, no. 1
pp. 522 – 532

Abstract

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Pandemic moments have generated mental and emotional problems in students at all levels. These have been affected by the format of virtual classes, the mandatory confinement and the little physical relationship due to the existing restrictions, generating academic burnout and anxiety in university students. In this context, the objective was to know the existing relationship between burnout and anxiety in students of the FIS-UNCP, the 15-question Maslach Burnout Inventory Student Questionnaire (MBI-SS) was used with the dimensions: Emotional Exhaustion, Cynicism and Loss of Academic Efficacy and 5 questions to know the level of technological anxiety or technostress, with a population of 328 university students of 10 semesters, through the questionnaire in Office Forms. The research design was non-experimental, transectional, with a qualitative-quantitative approach and descriptive-explanatory levels. The descriptive data analysis was made based on the scale, allowing the identification of students with burnout and the structural equation modeling facilitated the establishment of the relationship between the variables. The study showed that 26 students (7.93%) suffer from academic burnout. At the same time, it has been demonstrated that there is a positive and significant relationship between emotional exhaustion and lack of academic efficacy, with technological anxiety with path values of 0.701 and 0.345 respectively, the p-values allowed demonstrating hypotheses 1 and 3 formulated. At the level of the structural model, it allows anticipating future results, since the coefficient of determination (R2) calculated was 0.838.