Facilitating self-regulated learning with personalized scaffolds on student's own regulation activities

Abstract

The focus of education is increasingly set on students' ability to regulate their own learning within technology-enhanced learning environments. Scaffolds have been used to foster self-regulated learning, but scaffolds often are standardized and do not do not adapt to the individual learning process. Learning analytics and machine learning offer an approach to better understand SRL-processes during learning. Yet, current approaches lack validity or require extensive analysis after the learning process. The FLORA project aims to investigate how to advance support given to students by i) improving unobtrusive data collection and machine learning techniques to gain better measurement and understanding of SRL-processes and ii) using these new insights to facilitate student’s SRL by providing personalized scaffolds. We will reach this goal by investigating and improving trace data in exploratory studies (exploratory study 1 and study 2) and using the insight gained from these studies to develop and test personalized scaffolds based on individual learning processes in laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). At the moment study 2 is ongoing. The setup consists of a learning environment presented on a computer with a screen-based eye-tracker. Other data sources are log files and audio of students’ think aloud. The analysis will focus on detecting sequences that are indicative of micro-level self-regulated learning processes and aligning them between the different data sources