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The new wave of educational technologies (EdTech) is revolutionizing digital education but faces challenges with the complexities of multimodal human interactions in computer-based learning environments (CBLEs). Researchers are investigating multimodal learning analytics (MMLA) as a comprehensive approach to analyzing and supporting students. However, the integration of MMLA into scalable and automated learning environments is difficult because of the absence of standardized solutions for reliable multimodal data collection and analysis. Current MMLA systems are limited in their compatibility with modern web technologies and infrastructure for browser and Internet-of-Things (IoT) integration. To address these challenges, we introduce SyncFlow, an open-source platform offering scalable, robust cloud infrastructure for automated MMLA deployments. This paper presents an end-to-end application of SyncFlow, demonstrating its integration with AI-powered CBLEs and illustrating its capabilities. SyncFlow bridges critical gaps in MMLA data collection and processing, supporting scalable and impactful CBLEs in real-world settings. 

The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in version control, and multiuser collaboration support, DeepForge promotes reproducibility and ease of access and enables remote execution of machine learning pipelines. The tool currently supports TensorFlow/Keras, but its extensible architecture enables easy integration of additional platforms.