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Considering high school students’ experience in asynchronous and synchronous distance learning environments: QoE prediction model

  • Toni Malinovski
  • Marina Vasileva
  • Tatjana Vasileva-Stojanovska
  • Vladimir Trajkovik
Keywords: Quality of Experience, distance learning, high school students, structural equation modeling, survey


Early identification of relevant factors that influence students’ experiences is vitally important to the educational process since they play an important role in learning outcomes. The purpose of this study is to determine underlying constructs that predict high school students’ subjective experience and quality expectations during asynchronous and synchronous distance education activities, in a form of quality of experience (QoE). One hundred and fifty-eight students from different high schools participated in several asynchronous and synchronous learning sessions and provided relevant feedback with comparable opinions regarding different conditions. Structural equation modeling was used as an analytical procedure during data analysis which led to a QoE prediction model that identified relevant factors influencing students’ subjective QoE. The results demonstrated no significant difference related to students’ behavior and expectations during both distance education methods. Additionally, this study revealed that students’ QoE in any situation was mainly determined by motivational factors (intrinsic and extrinsic) and moderately influenced by ease of use during synchronous or quality of content during asynchronous activities. We also found moderate support between technical performance and students’ QoE in both learning environments. However, opposed to existing technology acceptance models that stress the importance of attitude towards use, high school students’ attitude failed to predict their QoE.

How to Cite
Malinovski, T., Vasileva, M., Vasileva-Stojanovska, T., & Trajkovik, V. (2014). Considering high school students’ experience in asynchronous and synchronous distance learning environments: QoE prediction model. The International Review of Research in Open and Distributed Learning, 15(4).
Research Articles