New Pub: Preserving Privacy in Multimodal Learning Analytics with Visual Animation of Kinematic Data
A recent study has been published that addresses the growing concern of data privacy in multimodal learning analytics (MMLA). The research investigates the potential of using visual animations as an alternative to traditional video recordings for analyzing sensitive data, particularly in educational settings. MMLA involves collecting and analysing data from various sources, including video recordings, to gain insights into learning behaviours and outcomes. However, the use of video can raise significant privacy concerns, especially when it contains identifiable information about individuals. This has led to ethical dilemmas regarding using such data in research. The study, based on the master thesis of Aleksandr Epp, introduces the Kinematic Animation Tool (KAT) to address these privacy issues. This tool allows researchers to visualise kinematic data without relying on video footage, thereby mitigating privacy…