The FLoRA Engine: Using Analytics to Measure and Facilitate Learners' own Regulation Activities

TL;DR

The paper addresses the challenge of helping learners regulate their own learning in technology-enabled environments by introducing FLoRA, an open-source engine that unobtrusively collects fine-grained trace data, maps actions to SRL processes via a Bannert-based trace parser, and delivers adaptive, analytics-informed scaffolding. The three-module architecture—Instrumentation Tools, Trace Parser, and Scaffolding—enables real-time measurement of SRL during tasks and integration with LMS platforms like Moodle. Key contributions include a suite of instrumentation tools, a theory- and data-driven trace parsing workflow, and an adaptive scaffolding module offering both generalised and personalised supports across five SRL-related scaffolds, validated through think-aloud studies and real-world case deployments. The work demonstrates practical potential for scalable SRL assessment and facilitation in diverse educational settings, with future directions including richer data modalities (video, AI writing assistants) and AI-powered agents to further enhance SRL support.

Abstract

The focus of education is increasingly set on learners' ability to regulate their own learning within technology-enhanced learning environments (TELs). Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to self-regulate their learning productively, as they typically need to navigate a myriad of cognitive, metacognitive, and motivational processes that SRL demands. To address these challenges, the FLoRA engine is developed to assist students, workers, and professionals in improving their SRL skills and becoming productive lifelong learners. FLoRA incorporates several learning tools that are grounded in SRL theory and enhanced with learning analytics (LA), aimed at improving learners' mastery of different SRL skills. The engine tracks learners' SRL behaviours during a learning task and provides automated scaffolding to help learners effectively regulate their learning. The main contributions of FLoRA include (1) creating instrumentation tools that unobtrusively collect intensively sampled, fine-grained, and temporally ordered trace data about learners' learning actions, (2) building a trace parser that uses LA and related analytical technique (e.g., process mining) to model and understand learners' SRL processes, and (3) providing a scaffolding module that presents analytics-based adaptive, personalised scaffolds based on students' learning progress. The architecture and implementation of the FLoRA engine are also discussed in this paper.

Figures (14)

• Figure 1: The user interface of the FLoRA integrated into Moodle LMS
• Figure 2: The architecture of the FLoRA engine
• Figure 3: Annotation tool
• Figure 4: Annotation search tool
• Figure 5: Timer tool