Abstract

Experimental data acquisition and statistical data analysis are core components in photonics undergraduate curriculum. Although it focuses on experimental data, the content is usually delivered by a lecture-based format. This disconnect is partially due to the contents are delivered at the early years of the program when experimental data acquisition techniques have not yet been introduced. In a second-year data acquisition and applied statistics course, we have designed an experiential learning module that covers the fundamental content of data acquisition and statistical analysis. This module has a single physical experimental setup that is continuously measuring environmental parameters (temperature, humidity, light, imaging, etc.) with a set of multi-modality sensors in an Internet-of-things (IoT) big data platform. Different types of sensors measuring the same parameters are also used for cross-validation purposes. The data is streamed to a cloud computing platform, allowing each student to acquire their own subset of data, then perform processing and analysis. The capability of remote access to a physical sensing experiment provides the students with hands-on learning opportunities on a managed complex data acquisition system. The platform provides a set of powerful visualization tools to allow a multi-dimension view of complex data streams (e.g. time-lapse of statistical distribution). Such IoT data acquisition platform allows key concepts to be demonstrated, applied, and tested including error propagation, distribution and test of distribution, correlation and cross-validation, data rejection, and signal processing. This experiential learning module has been demonstrated to be more effective in achieving related learning objectives through quantitative graduate attribute measurements as well as qualitative feedback.

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