An Architecture for Capturing and Presenting Learning Outcomes Using Augmented Reality Enhanced Analytics
2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)(2022)
Deakin Univ
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.
MoreTranslated text
Key words
Augmented reality,analytics,education,architecture
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined