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With the rise in technologies such as facial expression recognition and machine learning, it is now possible to gain a much more accurate insight about student engagement.
FREMONT, CA : While most teachers and experts agree that student engagement is essential for students to reach their full potential, measuring student engagement has always been complex. Apart from asking students to determine their levels of involvement, a completely reasonable method, even though it can be affected by their biases—assessment has always depended on teachers' or outside observers' perceptions.
It is generally acknowledged that with a high level of student engagement in a class or a course, the result is always better. Assessing student participation has always been a difficult task for mentors.
New digital technologies have made measurement much simpler and more accurate. It is particularly true for artificial intelligence, machine learning, and facial expression recognition technologies, which have all revolutionized the way engagement and disengagement signals are identified.
Facial Expression Recognition
Facial expression recognition technology is one of the most promising emerging technology trends that is helping with the challenge of evaluating student engagement.
The technology is based on cameras that are usually placed in the front of the classroom and can detect faces in the room and their expressions, and the meaning behind those expressions.
Teachers will be able to estimate the level of engagement in their class using this technology and detect particular points in time when facial expressions indicate a decrease or increase in engagement.
Artificial intelligence and machine learning are most often associated with virtual assistants or eLearning personalization initiatives in education.
The number of clicks users make while using a device, or the number of words they write is an example of data gathered to analyze student engagement.
The data can be uploaded to the cloud and compared to other similar classes, lessons, or schools utilizing machine learning. Following that, educators can evaluate their students' data and compare it with other students by using these insights.
Some lessons can be recorded, with machine learning technology that can assist teachers in detecting signs of disengagement apart from facial expressions, such as eye-tracking and body language.
Wearable technology and smart devices will offer educators access to heart rate data and other related information in the future, making this method both scientific and reliable.
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