Creatively Opening the Constraints of Learning Analytics in Inclusive, Elementary School-Level STEAM Education

Abstract

Learning analytics has been a topic of great interest to researchers and practitioners over the past decade. The challenge, however, is translating rich data into practice. Moreover, learning analytics in inclusive elementary school settings has been little studied. This article discusses using learning analytics in elementary school STEAM education, focusing on outer space content to address this gap. We adopted a collaborative approach in preservice teacher training with in-service teachers and researchers in the university practice school setting. Fifty-two 11–13-year-old students from the Finnish school context participated in this study. We used a process-oriented approach with sequence and process mining for data analysis. The results showed little to no difference between students with and without pedagogical support. Technologies such as learning analytics in various learning management systems can present both opportunities and challenges for students with support needs. However, this study’s results challenge the assumption that students with support needs in inclusive settings are less able to work independently and find coherent learning strategies in digital learning environments.