Key guidelines

What are your looking at ?

Photo by Niv Singer on Unsplash

As we owe privacy to each participant to the extent that everyone’s privacy is interwoven with that of everyone,  we aim to not bias the sincerity of observed behaviours. We assume there that this individual’s protection is a good way to strengthen the global gathered information (e.g. interactions, relatedness).
For this purpose, we propose four key guidelines as the core of the Teaching Lab design:

  1. We develop an ethics-by-design approach: beyond their common contribution in terms of signal processing, machine learning techniques will be used to anonymize at their source attendees’ data and filter any item likely to alter the confidence of the protagonists in the instructional flow. Thus, the Teaching Lab obfuscates all participants, while focusing on meaningful events and constructs (gaze, teacher cognitive load, hand, deictic, gesture, non-personal devices, voice activity and overlap, prosody).
  2. We promote a global versus local methodology: starting from a dual teacher’s egocentric point-of-view versus students’ global point-of-view perceived by the classroom, students’ behaviours will never be individually traceable, the machine learning being limited to restore a globalized picture of behaviours’ occurrence. In other words, our data reports about the whole classroom but ignore individuals.
  3. Finally, machine learning will be intentionally constrained to a delayed feedback, filtered by the research team, excluding any real-time monitoring or ad hominem reports. Our aim is only to support interactors’ possible professional development (students included), by studying and feedbacking the factors reinforcing or weakening attention in class.
  4. No high-stake decision, no staff performance monitoring, no student insulation will be made possible through the Teaching Lab. Following the University of Edinburgh’s Learning Analytics Principles and Purposes, we contend that data and algorithms can contain and perpetuate biases, and that they never provide the whole picture about humans’ capacities or likelihood of success.

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