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  1. Rapid technological advances and the increasing number of students in Southeast Asian nations present a difficult challenge: how should schools adequately equip teachers with the right tools to effectively teach Computational Thinking, when the demand for such teachers outstrips their readiness and availability? To address this challenge, we present the SAGE reference architecture: an architecture for a learning environment for elementary, middle-school and high-school students based on the Scratch programming language. We synthesize research in the domains of game-based learning, implicit assessments, intelligent tutoring systems, and learning conditions, and suggest a teacher-assisting instructional platform that provides automated and personalized machine learning recommendations to students as they learn Computational Thinking. We discuss the uses and components of this system that collects, categorizes, structures, and refines data generated from students’ and teachers’ interactions, and also facilitates personalized student learning through: 1) predictions of students’ distinct programming behaviors via employment of clustering and classification models, 2) automation of aspects of formative assessment formulations and just-in- time feedback delivery, and 3) utilization of item-based and user-based collaborative filtering to suggest customized learning paths. The proposed reference architecture consists of several architectural components, with explanations on their necessity and interactions to foster future replications or adaptations in similar educational contexts. 
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