Exploring the Learning Indicators for Grasping the Learning Processes in a Computer-Based Simulation Learning Environment

Abstract

Considering the rapid development of the learning analytics (LA) field and its unique advantages in data mining in the learning process, we will combine the theories of learning science and geometric-concept development to expand the learning analytics function in current computer-based simulation-assisted learning platforms. We initially conducted statistical analysis to evaluate learners' retention- and application-level performance, and we found that learners with different background variables in the experimental situations showed significant differences; however, we obtained no further explanatory data from the data regarding the learning process. This study preliminary revealed the various learning analytics, then the LA algorithm can be embedded to execute supervised or unsupervised processing mining; investigate the multiple learning indicators, such as engagement levels; and detect the proficiency of the geometric area schema formed as well as the conceptual development levels.