Multimodal Learning Analytics (MMLA) expands traditional learning analytics into the digital and physical learning environment, using diverse sensors and systems to collect information about education in more real-world environments. Challenges remain in making these technologies practical for capturing data in authentic learning situations. With the advent of readily accessible powerful artificial intelligence that includes multimodal large language models, new opportunities are available. However, few approaches allow access to these technologies, and most systems are developed for specific environments. Recent work has begun to make toolkits with access to collecting data from sensors, processing, and analyzing, yet these tools are challenging to integrate into a system. This paper introduces OpenMMLA, a toolkit approach that provides programming interfaces for harnessing these technologies into an MMLA platform with prebuilt pipelines, including the audio analyzer, indoor positioning, and video frame analyzer, offering multimodal data collection and visualizations and analytics. The paper provides an initial evaluation of the functionalities of the toolkit in data capturing and the implemented pipelines' performances.

This paper shares the challenges facing today's augmented reality (AR) smartphone applications, particularly in the realm of localization and tracking failure. Our research identifies limitations in current vision-based landmarks such as QR codes and AprilTags, commonly used to aid in localization, and the drawbacks of LiDAR integration in variable lighting conditions, compromising AR's accuracy and functionality. We also examine the constraints of Inertial Measurement Units (IMU) on movement speed, highlighting its impact on the dynamic performance of AR applications. Based on our extensive 316 experimental cases for 113 hours, including 34 case studies with 17 subjects in 2 sites, this paper presents the field with a nuanced analysis of the failure modes inherent in smartphone-based AR localization. We further explore a prototype solution which fuses ultra-wideband (UWB)-based sensing with the vision-based systems to alleviate these failure modes. Our approach addresses the immediate challenges of AR localization and opens avenues for future research and development in creating more spatially aware and interactive digital worlds. All of our demonstration videos, code, and datasets are available here1.

Collaborative learning has been qualitatively analyzed by learning science researchers to enhance learning performance. A quantitative analysis system supports the existing qualitative analysis of collaborative learning. We propose an Internet of Things (IoT) system comprising business-card-type badges, radio-over-fiber (RoF)-based synchronization, and a collaboration analysis algorithm to support collaborative learning analytics in different venues (e.g., offline, online, and hybrid). We showed that our proposed system quantitatively supports qualitative analysis of collaborative learning in different environments.