4. Analyzing and Comparing Online Learning Experiences through Micro-Level Analytics

  • 发布日期2016-10-08
  • 浏览次数781

Sanghoon Park


Abstract: Learning analytics collects and uses observations of interactions, which allow course instructors to search for the underlying patterns of a student’ learning progress and to accordingly optimize the student’ learning progress at a micro-level. Understanding the online learning experience through the learning analytics approach is essential to inform future pedagogical decisions in online learning design. This paper attempts to define the concept of an online learning experience in three dimensions. In addition, the Experience Sampling Method (ESM) is suggested as a supplement to Web log analysis (WLA) to collect data on cognitive involvement and learning emotion as well as to collect behavioral interaction data. Then, using Clow’s learning analytics cycle as a framework, this paper demonstrates how the identified cognitive, emotional, and behavioral aspects of the online learning experience can be captured and reported in the online learning experience dashboard for each individual student. In addition, the online learning experience data between two courses were compared to find evidence of different learning experiences when courses are designed with different learning tasks. The main finding from this paper is that ESM enables us to capture online learners’ psychological dimensions of learning experiences and provides rich information on each learner’s progress in an online course.


Keywords: Online learning experience, Learning analytics, Web log analysis, Experience sampling method, Online course design



地址:中国·北京·清华大学建筑馆北三层 邮编:100084 清华大学教育技术研究所 版权所有 2012.9