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.

翻译：教育日益关注学习者在技术增强学习环境（TELs）中自我调节学习的能力。先前研究表明，自我调节学习（SRL）能够提升学习成效。然而，许多学习者难以有效进行自我调节学习，因为这通常需要他们协调SRL所涉及的复杂认知、元认知与动机过程。为应对这些挑战，我们开发了FLoRA引擎，旨在帮助各类学习者提升SRL技能，成为高效的终身学习者。FLoRA整合了多个基于SRL理论并融合学习分析（LA）技术的学习工具，以提升学习者对不同SRL技能的掌握程度。该引擎通过追踪学习任务中的SRL行为，提供自动化支架以帮助学习者有效调节学习过程。FLoRA的主要贡献包括：（1）创建非侵入式工具，密集采集细粒度、时序化的学习行为轨迹数据；（2）构建轨迹解析器，运用LA及相关分析技术（如过程挖掘）对学习者的SRL过程进行建模与解析；（3）提供支架模块，根据学习进度呈现基于分析技术的自适应个性化支架。本文同时探讨了FLoRA引擎的架构设计与实现方案。