Some learners face barriers to learning in relation to anxiety and irregular sleep patterns. What if we were able to use data using a quantified self model to predict how a learner is going to perform at school on a particular day? By using this data, we can individualize lesson plans for learners each day to maximize that learner's capacity to learn. In this forecasting project, I will look at how some non-invasive biomarker data techniques could be combined and used in the future, and include features such as sleep monitoring, to predict how well a student will learn even before they step into the classroom that day.
What are Biomarkers? The term “biomarker”, a portmanteau of “biological marker”, refers to a broad subcategory of medical signs – that is, objective indications of medical state observed from outside the patient – which can be measured accurately and reproducibly (Strimbu, K., & Tavel, J. A., 2010). A biomarker is, therefore, data that can be used to determine the physiological state of an organism.
The amount of restful sleep is a great indicator of how well we function in every day life: "Sleep also has an impact on our cognitive functioning and learning ability. Adequate sleep is important for memory recall and information processing" (Harvard Medical School, 2007). Using sleep data alongside biomarker data could provide a wealth of information with regard to how we learn.
The wearable industry is now worth billions of dollars annually (Forbes, 2018) as people collect more data about themselves for self-improvement. Samsung has recently released a smartphone patent for monitoring body fat. If there are plans to monitor body fat through smartphone sensors, perhaps we can also look at monitoring anxiety levels and blood sugar levels through smartphone biomarker sensors. This data could then be used to inform a teacher of how a student is going to perform at school that day.
How could the technology work? Modern smartphones have a number of different sensors now, including light, magnetometer, GPS, etc. With smartphone technology, advances in the last few years, such as fingerprint and facial recognition technology, along with patents such as the Samsung idea above, in the future, we will probably see additional sensors being included on a smartphone. Perhaps the microphone will double as an exhaled breath biomarker sensor. No doubt, right now, you are scrolling down this page using your thumb, if on a mobile device; perhaps the organic light emitting diode (OLED) touch panels on the smartphones of the future will be able to analyze perspiration from the tip of your thumb for sweat diagnostic biomarkers.
Exhaled breath biomarker: A sensor picking up on exhaled breath could ascertain in real-time the health of that individual: "The technology works by detecting changes in electrical resistance or conductance as gases pass over sensors built on top of 'microhotplates,' tiny heating devices on electronic chips" (Purdue University, 2010).
Sweat diagnostic biomarker: Sweat analysis could offer a constant insight into what is happening in the body. Sweat releases cytokines (proteins released by the immune system), including interleukin 6 (IL-6). IL-6 has the same concentration in sweat as in blood: "This means doctors could use sweat to diagnose a wide variety of physical and mental stresses" (Heikenfeld, 2012).
Potential Case Study: Amanda had a terrible sleep which has resulted in her anxiety levels increasing. International Statistical Classification of Diseases and Related Health Problems (ICD-10) defines anxiety "as feelings of apprehension, motor tension such as fidgeting or muscle tension, and autonomic overactivity such as lightheadedness or sweating" (World Health Organization [WHO], 1993). The smartphone that Amanda uses collects data related to anxiety and is collated from the time she leaves school to when she arrives the next day. The data is wirelessly transmitted to her teacher. When Amanda enters the school building, the lesson plan and subject schedule has already been determined for her for that day based on her data. For example, if Amanda feels particularly anxious and fatigued, then a math test scheduled for that day could be pushed back, and a tactile science subject, or perhaps an energetic physical education lesson, could be planned instead.
Ethics. The collected data on the smartphone would send data to a cloud server for analysis. After analysis, lesson planning findings are sent back to the student and the classroom teacher. As a result, there are privacy concerns about how this data is stored and used. Further, who owns the data? Is it the company providing the technology? Is it the school? Or is it the student? Many stakeholders would be involved in this process, so there are numerous ethical considerations around confidentiality of health data and single purpose of use.
Autonomy. What happens when teachers and learners lose autonomy? The Biomarker data app in this case would become the "director of learning" telling the learners which lessons are most appropriate and at what time. As Smith claims, "Supportive engagement of learners’ existing autonomy (by the teacher) can be seen as an important basis for its progressive development; indeed, the notion that learners have the power and right to learn for themselves is seen by many proponents as a fundamental tenet" (Smith, R. 2018). However, would we lose this progressive development aspect of education in using this technology?
Security of data. "The release of this or other biometric information could put users at permanent risk and create significant legal exposure for the company that loses the data" (CSO, 2019). The security of this data is paramount because of its biological nature. The loss of data of this kind could be catastrophic to the individual. Stolen data could be used for illegal activity, such as identity theft and other, more sinister abuses.
Conclusion. In the future, we will probably continue to use biometric data for self-improvement; however, the introduction of biomarker data may provide us with information that helps us with informed planning. This informed planning may extend to education where we can "listen" to our body on a whole different level. The ethical, privacy and security considerations are crucial in order for the public to fully trust such a shift and transformation of practice.
References:
Grob, N. M., Aytekin, M., & Dweik, R. A. (2008). Biomarkers in exhaled breath condensate: a review of collection, processing and analysis. Journal of breath research, 2(3), 037004.
Harvard Medical School (2007). Sleep, Learning, and Memory. Retrieved from http://healthysleep.med.harvard.edu/healthy/matters/benefits-of-sleep/learning-memory
Heikenfeld, J. (2014, October 22). Sweat Sensors Will Change How Wearables Track Your Health. Retrieved from https://spectrum.ieee.org/biomedical/diagnostics/sweat-sensors-will-change-how-wearables-track-your-health
Jansen, K. L., Fortenberry, K. T., & Clark, M. S. (1970, January 01). Anxiety and Its Measurement. Retrieved from https://link.springer.com/referenceworkentry/10.1007/978-1-4419-1005-9_938
Kim, Kim, & Baek. (2015, July 10). METHOD AND APPARATUS FOR MEASURING BODY FAT USING MOBILE DEVICE. Retrieved from https://patentscope.wipo.int/search/en/detail.jsf;jsessionid=7D71A258666FB110597E4DBF5690BB3B.wapp1nC?docId=WO2015102156&tab=DRAWINGS&office=&prevFilter=&sortOption=Pub Date Desc&queryString=FP:(DP:([07.07.2015 TO 31.07.2015]) AND PA:samsung)&recNum=2&maxRec=73
Korolov, M. (2019, February 12). What is biometrics? And why collecting biometric data is risky. Retrieved from https://www.csoonline.com/article/3339565/what-is-biometrics-and-why-collecting-biometric-data-is-risky.html
Purdue University. (2010, December 29). 'Breathalyzers' may be useful for medical diagnostics. ScienceDaily. Retrieved March 12, 2019 from www.sciencedaily.com/releases/2010/12/101228141700.htm
Smith, R (2008) Learner autonomy, ELT Journal, Volume 62, Issue 4, October 2008, Pages 395–397, Retrieved from: https://doi.org/10.1093/elt/ccn038
Strimbu, K., & Tavel, J. A. (2010). What are biomarkers?. Current opinion in HIV and AIDS, 5(6), 463-6.
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