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 risks. The KAT operates in a web browser, making it accessible and user-friendly for researchers in various environments.
The study involved a field experiment where participants annotated data sets using both animations and video recordings to assess the quality of the annotations. The results indicated that the inter-rater agreement between the two methods was high, suggesting that animations can serve as a viable alternative to videos in the data annotation process. This finding is significant as it demonstrates that the quality of data analysis can be maintained while enhancing privacy.
The successful integration of the KAT into existing multimodal data analysis frameworks suggests that researchers can conduct studies without the ethical concerns associated with video recordings. This approach not only protects participants' privacy but also encourages broader participation in MMLA research.
This study provides a valuable contribution to the ongoing discussion about data privacy in research. Demonstrating the effectiveness of visual animations in data analysis offers a practical solution for researchers looking to balance the need for quality insights with ethical considerations. As learning analytics continues to evolve, adopting, like the KAT, may be crucial in promoting responsible research practices.
In summary, visual animations represent a promising advancement in privacy-preserving data analysis, allowing researchers to explore learning behaviours while safeguarding participant information.
Full citation:
Di Mitri, D., Epp, A., Schneider, J. (2024). Preserving Privacy in Multimodal Learning Analytics with Visual Animation of Kinematic Data. In: Casalino, G., et al. Higher Education Learning Methodologies and Technologies Online. HELMeTO 2023. Communications in Computer and Information Science, vol 2076. Springer, Cham. https://doi.org/10.1007/978-3-031-67351-1_45
