Using learning analytics to support STEAM students’ academic achievement and self-regulated learning

Abstract

Abstract The assessment of students’ academic achievements helps to increase learning effectiveness by encouraging each student to recognise his/her strengths and areas for improvement. To do so, pedagogical activities that encourage direct and frequent evaluation must be considered. This paper focuses on how a learning management system such as Google Classroom (GC) together with learning analytics (LA) can be used to extract and analyse learner’s data from Science, Technology, Engineering, Art and Mathematics (STEAM) course. In addition, we explore how to employ these data to support metacognitive skills such as self-regulated learning (SRL). An explanatory sequential mixed-method design research was used, and two research questions were set, discussed and analysed. Data collection involved 128 participants. Our findings confirmed the potential of using achievement-based grading rubrics data and LA tools to provide empirical evidence of how formative assessment can affect students’ SRL development. While onsite experience has revealed some important initial findings, further research is needed. To validate these results, it will be necessary to perform similar analyses on datasets obtained from other schools and subject areas. Despite the increasing interest in use of LA, there is a scarcity of research on this field for secondary school education.