Developing SAMM: A Model for Measuring Sustained Attention in Asynchronous Online Learning

Abstract

There is a strong relationship between sustainability and equality education, as it is emphasized in the United Nations’ Sustainable Development Goals (SDGs). To maintain learning effectiveness, learning attention is a valuable consideration. By continuously monitoring learners’ attention, the teaching and learning process can be measured and adjusted as needed. However, it poses a challenge for measuring attention in online learning environments where all participants do not interact face-to-face. To address this concern, this paper proposes a sustained attention measurement model (SAMM) that establishes attention tests to gauge learners’ sustained attention levels during asynchronous online learning. SAMM presents learners with real-time questions based on course content, collecting both their response time and accuracy. In an experiment conducted over an academic semester, we recruited 213 students from a private Taiwanese university of technology and analyzed their response time and accuracy rate to identify attention patterns in the online learning system. This analysis can provide valuable feedback for instructors to adjust their teaching methods.