Balancing human-human and human-computer interaction

A fundamental challenge in implementing personalized learning is in determining just how much it should be personal—or interpersonal, to be more specific. Carlo Rotella highlights the tension between the customization afforded by technology and the machine interface needed to collect the data supporting that customization. He narrows in on the crux of the problem thus:

For data to work its magic, a student has to generate the necessary information by doing everything on the tablet.

That invites worries about overuse of technology interfering with attention management, sleep cycles, creativity, and social relationships.

One simple solution is to treat the technology as a tool that is secondary to the humans interacting around it, with expert human facilitators knowing when and how to turn the screens off and refocus attention on the people in the room. As with any tool, recognizing when it is hindering rather than helping will always remain a critical skill in using it effectively.

Yet navigating the human-to-data translation remains a tricky concern. In some cases, student data or expert observations can be coded and entered into the database manually, if worthwhile. Wearable technologies (e.g., Google Glass, Mio, e-textiles) seek to shorten the translation distance by integrating sensory input and feedback more seamlessly in the environment. Electronic paper, whiteboards, and digital pens provide alternate data capture methods through familiar writing tools. While these tools bring the technology closer to the human experience, they require more analysis to convert the raw data into manipulable form and further beg the question of whether the answer to too much technology is still more technology. Instructional designers will always need to evaluate the cost-benefit equation of when intuitive human observation and reflection is superior, and when technology-enhanced aggregation and analysis is superior.

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