Using educational data from teaching and learning to inform teachers' reflective educational design In inquiry-based STEM education

Abstract

Science, Technology, Engineering and Mathematics (STEM) education is recognized as a top school education priority worldwide and Inquiry-based teaching and learning is identified as a promising approach. To effectively engage students in Inquiry tasks, appropriate guidance should be provided, usually by combining digital tools such online labs and modeling tools. This is a cumbersome task for teachers, since it involves manually assessing the type/level of tool-supported guidance to be provided and potentially refining these to meet guidance needs of individual students. In our research we target to investigate how to support this systematic reflection process with educational data analytics methods and tools from both the design and the delivery of Inquiry-based educational designs (IED). The contribution of this paper is to propose a novel “Teaching and Learning” Analytics method and research prototype tool, extending the scope of purely learning analytics methods, to analyze IED in terms of the tool-supported guidance they offer and relate these analyses to students' educational data that are already being collected by existing learning analytics systems, increasing teachers' awareness. A two-layer evaluation methodology positively assessed the capacity of our method to analyze IED and provided initial evidence that the insights generated offer statistically significant indicators that impact students' activity during the delivery of these IED. The insights of this work aim to contribute in the field of cognitive data analytics for teaching and learning, by investigating new ways to combine analyses of the educational design and students' activity, and inform teachers’ reflection from a holistic perspective.