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Data Sensing and Visualization Systems
Data sensing refers to the acquisition of information about an object, subject, or phenomenon using sensor technologies. Today, sensor technologies are able to measure almost any physical quality—including electromagnetic radiation; emitted or reflected light (e.g., infrared light in eye-tracker systems); temperature, acoustic, electric current, or activity (e.g., heart or brain activity); chemical reactions; forces (e.g., pressure and weight); and position measurements (e.g., speed and acceleration). While sensing technologies allow capturing a vast amount of information, visualization systems help to communicate that information clearly and effectively using graphical representations. This entry first discusses how data sensing technology and visualization systems are used in learning research then discusses the various types of systems and how they are used. It concludes by discussing how the data obtained by these systems could be used in instruction.
Data sensing technology and visualization systems help learning researchers to understand phenomena, such as the learning process, by facilitating the understanding of the human element during design and implementation activities. Digital learning environments have started to include sensing devices. Learning researchers apply visualization systems to understand the data gathered while students interact with learning environments. These systems enhance the data collected, allowing researchers to consider two important aspects that occur during the learning process, the cognitive and affective aspects.
These devices help researchers to focus on measuring human mental states and understanding the implication of those states. To infer those mental states diverse measurements are considered, ranging from brain-wave signals and biofeedback readings to facial expression and gesture recognition, as well as posture and pressure sensing technologies.
Neurological Sensors
Electroencephalographically (EEG) sensor, also known as brain-computer interface, is a particular type of physiological instrument that uses brainwaves as an information source; it measures the electrical activity produced by the firing of neurons within the brain over a period of time by use of multiple electrodes placed on the scalp.
The EEG is typically described in terms of neural oscillation. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. Measures of neural oscillation frequency have been related to relaxed mental states (alpha waves), thinking or concentration (beta waves), and consciousness (gamma waves). In addition to this inference, algorithms have been defined to deduce affective states (including excitement, engagement, boredom, meditation, and frustration) using neural activity. As of 2014, the cost of these sensors had decreased considerably and there were a variety of hardware devices on the market for less than $300.
Physiological Sensors
Physiological sensors are instruments that provide information on the activity of physiological functions. The following are descriptions of some of the physiological sensors available:
Skin conductance sensors measure the electrical conductance of the skin, which varies with skin’s moisture level as regulated by the sweat glands, which are controlled by the sympathetic and parasympathetic nervous system. The sensor sends a small amount of current through the body, between two points on a bracelet, and measures the achieved conductance. Skin conductance is an indicator of psychological or physiological arousal.
Heart rate sensors measure the pulses of the heart. This pulse rate can be detected by measuring the tiny electrical changes on the skin that are caused when the heart’s muscle depolarizes during each heartbeat. The heart rate can vary according to the body’s physical needs, but also on anxiety and stress levels.
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