The study of learning is grounded in theories and research. Since learning is complex and not directly observable, it is often inferred by collecting and analysing data based on the things learners do or say. By virtue, theories are developed from the analyses of data collected. With the proliferation of technology, large amounts of data are generated when students learn online. Therefore, researchers not only have data on students’ learning performance, but they also have data on the actions students take to achieve the desired learning outcomes. These data could help researchers to understand how students learn and the conditions needed for successful learning. In turn, the information can be translated to instructional and learning design to support students. The aim of the chapter is to discuss how learning theories and learning analytics are important components of educational research. To achieve this aim, studies employing learning analytics are qualitatively reviewed to examine which theories have been used and how the theories have been investigated. The results of the review show that self-regulated learning, motivation, and social constructivism theories were used in studies employing learning analytics. However, the studies at present are mostly correlational. Therefore, experimental studies are needed to examine how theory-informed practices can be implemented so that students can be better supported in online learning environments. The chapter concludes by proposing an iterative loop for educational research employing learning analytics in which learning theories guide data collection and analyses. To convert data into knowledge, it is important to recognize what we already know and what we want to examine.
|Title of host publication||Utilizing Learning Analytics to Support Study Success|
|Editors||Dirk Ifenthaler, Dana-Kristin Mah, Jane Yin-Kim Yau|
|Number of pages||23|
|Publication status||Published - 2019|