Auto-Analytics: Self-Tracking Tools Students May Find Useful

Auto Analytics and its Potential for Students and Higher Ed

We live in a time poor society, where students and employees are concerned with how much they can accomplish in a short period of time, and where methods for increasing productivity are often stated as action plans and goals. There’s an emerging trend known as auto-analytics that provides automated tools for feedback about performance and productivity, which allows individuals to take corrective actions as needed. While the use of auto-analytics is catching on in organizational settings, it also appears to be a resource that students can use to monitor and increase their effectiveness, which in turn may boost their engagement and performance in class.  

Auto-Analytics: Emerging Trend

Analytics by definition is a method of logical analysis. Auto-analytics is a consumer trend used to describe automated self-tracking and analysis through the use of technology. A New York Times article The Data-Driven Life brought to light the premise that, as a society, many of us are already used to tracking ourselves in many different ways. The first two examples cited included keeping track of our body weight or checkbook balance. Social networking has also allowed many people to post updates or personal status reports to a wide audience, which is another form of self-tracking. As another example, an application called Foursquare provides a tracking tool. There are over one million registered users and the app allows them to check in and report their location via websites such as Facebook. Auto-analytics builds upon these forms of self-tracking by adding a technological component, such as software or wearable devices.

Within The Data-Driven Life, it indicated that with the use of auto analytics the “process of self-tracking becomes both more alluring and more meaningful.” One reason for this is that those who post updates through social networking websites often want more to share and report, which can be accomplished through automated tools that provide feedback and analyses. Auto-analytics have been developed as computer software programs, apps, and devices. The devices that can be worn are integrated with a website to provide productivity analyses. The website Quantified Self provides a list of the most popular self-tracking, auto-analytical tools, which includes Fitbit. It provides users with a small device to wear that tracks physical activities and even sleep patterns. The following video explains how Fitbit is used, which will provide an example of how auto analytics are being developed for personal use.
 

Auto-Analytics in Organizations

A recent article in the Wall Street Journal Employees, Measure Yourselves, posed the following statement about organizational employees: “suppose they could tell how much an afternoon workout boosts their productivity, or how much a stressful meeting raises their heart rate,” or “they could get a breakdown of how much time they spend actually working on their computer, as opposed to surfing the Web.” The purpose of collecting this type of data is to determine what behaviors result in effective performance and which ones need to be changed. It would provide a means of self-improvement through real-time feedback.

In Five Good Reasons to Champion Auto-Analytics in Your Organization, it was noted that there are four primary trends driving the increased adoption rate of auto-analytics include “mobile phones, social media, cheap electronic sensors, and cloud computing.” People are more connected with technology than ever before, which is driving the growth of new products. Because technology is so readily available and accessible, people can use auto-analytics to gather data about the productivity throughout the day and receive immediate feedback. One tracking tool that may be useful is RescueTime, which collects data about the time spent on websites and apps, which will allow employees to monitor and adapt their approach to work, especially if their productivity is down.

Auto-Analytics for Students?

Within the field of higher education there is a similar analytical tool being utilized, called learning analytics. It’s a data mining technique that institutions and educators use to gain insight about their students. The NMC Horizon Report: 2012 Higher Ed Edition notes that “the larger promise of learning analytics, however, is that when correctly applied and interpreted, it will enable faculty to more precisely understand students’ learning needs and to tailor instruction appropriately far more accurately and far sooner than is possible today” (p. 23). This is a form of auto-analytics as technology is utilized to gather data and provide summative feedback.  

Within a report released on April 10, 2012 by the U.S. Department of Education Office of Educational Technology, Big Data – Avalanche? Flood? Tsunami? What does big data mean for educators?, it addressed data mining through the use of learning analytics. The report suggested that instructors need this type of data and they “must have near-real-time access to easy-to-understand visual representations of student learning data at a level of detail that can inform their instructional decisions” (p. 42). The use of learning analytics was discussed only as a tool to be utilized by educators to collect data about students.

As an educator, I believe it would be beneficial for students to explore the use of auto-analytic tools so they have a method of assessing their productivity. The tools that have been proposed for use in organizations would also translate well for students’ use. For example, instead of writing goals to “be more productive” or “improve their performance” students would have tools and techniques to collect data and provide analyses about factors such as the use of their time, and visually report the results in real time. When students receive these reports they can take corrective actions as needed. This would provide a more interactive approach to learning and could help to address the needs of self-directed learners who want to be involved in the learning process.

Keep an eye out for further developments as auto-analytics are implemented for gauging employee performance in the workplace. If successful, it may lead to application and use in higher education.

Have you used an auto-analytical tool? Share your experience and feedback via Twitter @DrBruceJ.

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