A new column from the latest Connective Issues newsletter by Bohyun Kim, our new Associate Director of Library Applications and Learning Systems.

Notable Tech Trends: Quantified Self, Learning Analytics, and Big Data

One of the recent major technology trends to note is ‘Quantified Self.’ According to this year’s Horizon Report Higher Ed edition, “Quantified Self describes the phenomenon of consumers being able to closely track data that is relevant to their daily activities through the use of technology” (p.46). This trend is enabled by the wearable technology devices – such as Fitbit and Google Glass – and the Mobile Web. Wearable technology devices automatically collect personal data. Fitbit, for example, keeps track of one’s own sleep patterns, steps taken, and calories burned. The Mobile Web serves as the platform that stores and presents such personal data collected by those devices. Using these devices and the resulting personal data, we get to observe our own behavior in a much more extensive and detailed manner. Any meaningful pattern emerging from such observation can lead to a better way to improve ourselves.

Quantified Self is a notable trend not because it involves an unprecedented technology but because it gives us a glimpse of what our daily lives will be like in the near future, in which many of the emerging technologies — the mobile web, big data, wearable technology — will come together in full bloom.

Learning Analytics can be thought of as the application of ‘Quantified Self’ to education. It is being explored at various institutions, though it is at an early stage (see “How Learning Analytics Are Being Used in Education” by Katie Lepi in Edudemic for examples). By collecting and analyzing the data about student behavior in online courses and other learning environments, Learning Analytics aims at improving student engagement, providing more personalized learning experience, detecting learning issues, and determining the behavior variables that are the significant indicators of student performance.

The rise of “Big Data” raises a serious concern about privacy and security. Students, faculty, and researchers in higher education implicitly trust the systems developed by or in use at their institutions. But how the data kept and shared at those systems are used and accessed should be made as transparent as possible. In the area of clinical data, there is already a notable movement which aims at fostering the mutually beneficial collaboration between the patients who own their personal health data and the researchers who can analyze such data to generate new insights and knowledge in a more transparent manner. See “Citizens as Partners in the Use of Clinical Data” by John Wilbanks at O’Reilly Data Blog.