An Architecture for Capturing and Presenting Learning Outcomes using Augmented Reality Enhanced Analytics

Manjeet Singh, Shaun Bangay, Atul Sajjanhar

Abstract: Augmented Reality (AR) applications have the capabilities to collect a range of sensor data that is relevant to educators. We propose a platform for using AR to enhance learning analytics by 1) Capturing information from a range of sources including directly from sensors but also from interactions and outcomes within the AR experience. 2) Deriving metrics from this sensory data to provide five categories of measure representing the quality of the learning experience: namely (a) Learning Analytics, (b) Interaction Analytics, (c) Spatial Analytics, (d) Sensory Analytics and (d) Emotion Analytics. 3) Presenting real-time information as analytics to teachers directly as an information overlay using an AR view within the classroom and customised to each student. 4) Closing the student-teacher-student feedback loop so that analytics information feeds directly into teaching in addition to assessment after the event. We evaluate the feasibility of this architecture by 1) Providing a proof-of-concept demonstration showing that the required data can be collected on the targeted platforms. 2) Identifying relevant educational metrics and relating these to the sensor data being collected. 3) Creating educational augmented reality applications and validating these with learners. 4) Identifying teacher requirements with respect to the use of analytics dashboards. The proposed architecture ensures that teachers can differentiate teaching support for each student based on individual needs across the range of learning needs.