Measuring Student Engagement with Digital Tools

1. Measuring Student Engagement with Digital Tools Most teachers and experts would state that student engagement is mandatory for students to fulfill their potential, but actually measuring student engagement has always been a trickier prospect. Aside from asking students to assess their own levels of engagement – a perfectly valid process, albeit one which is open to being influenced by their biases – measurement has often relied upon the perception of teachers or outside observers. Of course, these perceptions can also be flawed.

2. Thankfully, digital technology has helped to change this, offering ways to gain a much more accurate sense of how engaged students are. Read on to find out how. It is widely accepted that the higher the level of student engagement in a class, or with a course, the better. Yet, actually measuring student engagement has traditionally been a real challenge. After all, simple observation makes it difficult to accurately quantify the level of engagement, and teachers or other observers can easily miss signs of disengagement. Fortunately, modern digital tools like whiteboards have helped to not only make measurement much easier, but also much more reliable too. This is especially true when it comes to artificial intelligence, machine learning and facial expression recognition technology, which have all helped to revolutionize the way the signs of engagement or disengagement can be detected. Facial Expression Recognition One of the most exciting digital technology trends that is helping with the task of measuring student engagement is facial expression recognition technology. In fact, collaborative efforts between ViewSonic and Intel led display to the implementation of precisely this kind of technology into the myViewBoard

3. software platform, providing educators with the necessary information to understand and respond to students’ needs, based on non-verbal cues. The technology works through cameras, typically positioned at the front of the class, which are able to detect not only faces within the room, but the expressions on those faces and what those expressions mean. This then equips educators and academic institutions with the ability to identify, understand and respond to learners’ emotional states. With such technology in place, teachers will be able to identify the level of engagement that exists within their class and even identify specific points in time when facial expressions suggest dips or spikes in engagement. This can then allow educators to adapt to their students’ emotions and adjust their lessons accordingly, either in the moment or in future. Machine Learning The concepts of artificial intelligence and machine learning within education are most commonly linked to virtual assistants, or eLearning personalization efforts. However, they can also play a role in helping to measure how engaged students are in a class. Some of the data that can potentially be collected for the purpose of measuring student engagement includes the amount of clicks users make

4. while using a computer, or the number of words they type. Classroom attendance figures may be tracked over time, or even the number of different students that volunteer answers during a discussion. This data can then be uploaded to the cloud and, using machine learning, comparisons can be made with other similar classes, lessons, or schools. From there, these insights can allow educators to consider how data for their students compares with other students. Changes can then be made to the teaching methods used or the structure of the class. Recording Lessons Some lessons may be able to be recorded, with machine learning technology helping teachers to identify signs of disengagement beyond facial expression recognition, including eye-tracking and body language. In the future, wearable technology and smart devices may also be able to provide educators with access to heart rate data and other similar information, making this process incredibly scientific, but also incredibly accurate. Machine learning technology solutions could also be able to make their own suggestions about what needs to be done to improve the quality of a lesson or to give insight into the types of strategies and techniques that have boosted engagement levels elsewhere.

5. Student Engagement Surveys Course evaluations have been a mainstay within student engagement measurement, and these will often take the form of student surveys. However, they are not a foolproof method, as they often occur at the end of a course. This means that students are having to recall how they felt, rather than answering in the moment. It also means students may feel a sense of relief that a class is over, resulting in them giving overly generous responses. At the same time, student surveys are one of the few ways in which engagement is measured by actually asking students for their thoughts, ensuring they do still have real value. This value can also potentially be increased through the use of digital technology. For instance, classroom management software packages and audience response systems may include options to run surveys in the moment, or can allow teachers to run instant polls. These surveys and polls can be anonymized, in order to encourage students to share their true feelings, and this potentially allows feedback to be almost instantaneous. Alternatively, anonymous surveys can be sent out digitally at the end of each lesson. Final Thoughts

6. In the past, measuring student engagement has largely centered around perceptions, whether from students or classroom observers. However, with the rise of technology like facial expression recognition and machine learning, it is now possible to gain a much more accurate insight into how focused, entertained, enthused and intrigued students really are.