Discussion: The data-gathering capacity of wearable devices, including smartphones, combined with cloud-based analytics, has proven itself in clinical health applications (e.g. cardiac recovery) and for personal improvement (e.g. FitBit devices). Data can extend beyond health indicators such as movement, pulse, etc., to behaviours, attention, sociability, etc. The two-fold benefit of such Quantified Self concepts is that the data can generate live and long-duration ‘mirrors’ (where the wearer is offered actionable intelligence about themselves) and ‘portraits’ (where actionable intelligence is available to helping professionals, such as physicians or teachers).
523 Inspiration: What is the future for ‘quantified learners’? For example, poor diet and lack of sleep limit learning ability, so what range, combinations and dashboards of self-measurement feedback could help us become better learners?
Data analysis is good for making future decisions considered by various bodies- students, data collectors, teachers or parents.
These “quantifying learners” are growing with the data trends by looking at them regularly. They commonly refer to them for everything (e.g. workout, location tracing of friends etc.) Also the meta-data about their own data is getting noticed by looking at the time-stamps or device used to sign in. With this huge data, it becomes quite clear to judge the activity. This helps them to have self reflection about their routines, workouts or even screen time.
I have experienced that young adults don’t realize their device addiction generally but after looking at the screen time and the type of activities performed on different apps mirrors their habits and it definitely helps them to be careful of their actions.
As an educators, I practiced with students to have a look at their digital footprints (purpose of the device usage) and make comparison with their age-group and make better choices. This data is evident and students can compare different datapoints over a time period to make positive change in their device usage, health statistics or sleep hours.
I like this post as it is opening a new door to imagine new era through data science.
Personally, I look at the data statistics to make better decision next time.
But I am also concerned about the privacy and data manipulations by the data collecting companies. What is the next interpretation of these digital informatics. Are they going to build more entertainment apps or are they going to enhance the features of the apps to support these vulnerable individuals.
I find the idea of the quantified learner compelling both in how it connects both to my professional work in post-sec student learning, and to my own experiences as a neurodivergent student. I know firsthand how motivating it can be to track patterns and habits in an effort to “optimize” particular behaviours. For example, I am much more motivated to get my steps in or sleep for eight hours in order to maintain a streak or compete on a leaderboard than I am by simply knowing the physical benefits of doing these things regularly. I am interested in how similar approaches could motivate particular learning behaviours. This has particular relevance to my work with Supplemental Instruction (SI), where we aim to help students become more intentional and reflective about how they learn. Quantified-self tools could allow students to recognize patterns in their studying, attention, participation, or use of learning strategies and experiment with what works best for them.
However, there is an important distinction between data that empowers students and data that institutions collect about students. I recognize the value of the “mirror” (the tools and output that give learners greater insight into themselves and support metacognition and self-regulated learning) but am often anxious about the “portrait,” and how institutions and private business will use the data to continue to sell us new tools towards this purpose. I wear a smartwatch daily and track the metrics, I also spend money on productivity and habit tracker apps. I understand how these tools give learners greater insight into themselves and support metacognition and self-regulated learning, but I know that there’s a false sense of productivity-maxing that can lead learners, especially those who already feel they are on the back foot and trying to compensate for perceived lacking, to thinking the solution lies in the next purchase of a device. For me, the potential of this frontier lies in maintaining learner agency, with measurement serving as just one of many supports for reflection and motivation.
Observation and documentation are already central to my work in Early Years education. We build a holistic understanding of young learners by observing everything from communication and social development to attention, motor skills, knowledge and understanding. Personally, I also voluntarily quantify parts of my life through Garmin, Strava and sleep data, particularly when I was training for an Ironman 5150. In that context, the data acts as a mirror: I use it to understand myself and decide how to respond.
What interests me is what happens when that mirror becomes a portrait created and interpreted by someone or something else. As an educator, my observations are contextual, open to interpretation and strengthened through collaboration with other educators, families and, in developmentally appropriate ways, students themselves. What happens when mobile intelligence continuously collects behavioural, physiological and learning data and begins inferring attention, engagement, ability or need? It is not difficult for me to imagine a future where even young learners arrive at school wearing devices capable of continuously collecting some of this data. Quantification could support reflection, agency and growth, but it could also reduce learners to what a system can measure and infer. I am interested in how we ensure these increasingly sophisticated representations are used with learners, rather than simply about them.
I see two opposing sides to this type of data collection. On one hand, it could help students understand their habits and bodily patterns in order to develop self-regulation and metacognitive skills. For example, students could use real-time data to see how factors such as their diet or sleep directly influence their focus, thus helping them make better choices for their well-being.
Conversely, this data could also be used in harmful ways. For example, the AI headbands that were introduced in China to measure students’ attentiveness created concerns about children’s autonomy and mental health. Constant monitoring could increase anxiety or encourage performative learning, especially for neurodivergent students. This frontier was one of my top three choices because I am very interested in how the concept of “quantifying the learner” will impact the ethics of student data/privacy in schools.