Flora Fox: A comprehensive measurement of young learners’ self-regulated learning

Funding period: 2023 to Present Supported by the Jacob’s Foundation

The overall objective of this project is to build Flora Fox, a software for trace-based and comprehensive measurement of secondary students’ self-regulated learning during writing tasks. It employs a mixed-methods approach that utilizes data-driven interviews. This project involves researchers at Monash University (Australia), Michigan State University (U.S.), and the University of Giessen (Germany).

State-of-the-art methods for measuring self-regulated learning (SRL) involve collecting students’ interactions (log files) with a digital platform. Even though trace data may provide deep insights into what learners are doing, this data often cannot explain why learners made particular learning decisions, i.e., it cannot reveal learners’ motivation to engage in particular self-regulated learning processes. To address this methodological gap, we will integrate Flora, a technology-enhanced learning environment embedded into the Moodle Learning Management System with Quick Red Fox (QRF), a mobile application that alerts a researcher to key moments in a learner’s self-regulated learning processes, e.g., the occurrence of a critical SRL behaviour/ sequence of behaviours. In this way, we will develop Flora Fox, an innovative software that integrates Flora and Quick Red Fox (QRF) functionalities to allow for data-driven survey collection of subjective motivational responses as it relates to SRL behaviors to advance our understanding of motivation and its role in self-regulation during learning.

Highlighted Publications on this Work:

1. Zhao, L., Raković, M., Cloude, E. B., Li, X., Gašević, D., & Bardach, L. (2025). The Effect of Sequential Transition of Self-Regulated Learning Processes on Performance: Insights from Ordered Network Analysis. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (pp. 516-526). Association for Computing Machinery.
2. Zhao, L., Raković, M., Lee, G. A., Nuseibah, H., Cloude, E. B., & Bardach, L. (In Press). Capturing Momentary Motivational States Using Detector-driven In-situ Questionnaire. In E. G. Blanchard, G. Chen, M. Chi, and S. Isotani (Eds.), Late Breaking Results Proceedings in the 17th International Conference on Artificial Intelligence in Education (AIED 2026). Springer Nature Switzerland.