2 min read
15 Oct

/blog/neuroleak-the-brain-universeNeuroLeak and its Team is legally and committed to releasing new science research articles and news from labs, universities, hospitals and news departments around the world. Science articles can cover neuroscience, psychology, AI, robotics, neurology, brain cancer, mental health, machine learning, autism, Parkinson's, Alzheimer's, brain research, depression and other topics related to cognitive sciences. 

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A new machine learning algorithm can accurately predict how long people will spend viewing an exhibit at a museum

In a proof-of-concept study, education and artificial intelligence researchers from North Carolina State University have demonstrated the use of a machine-learning model to predict how long individual museum visitors will engage with a given exhibit. The finding opens the door to a host of new work on improving user engagement with informal learning tools.

In our research interviews and discussion, it is clear that “Education is an important part of the mission statement for most museums,  “The amount of time people spend engaging with an exhibit is used as a proxy for engagement and helps assess the quality of learning experiences in a museum setting. Museum exhibit is not like school – you can’t make visitors take a test.” “If it is possible to determine how long people will spend at an exhibit, or when an exhibit begins to lose their attention, it is also possible to use that information to develop and implement adaptive exhibits that respond to user behavior in order to keep visitors engaged.

To determine how machine-learning programs might be able to predict user interaction times,  researchers closely monitored 85 museum visitors as they engaged with an interactive exhibit on environmental science. Specifically, the researchers collected data on study participants’ facial expressions, posture, where they looked on the exhibit’s screen and which parts of the screen they touched.

The data were fed into five different machine-learning models to determine which combinations of data and models resulted in the most accurate predictions. NeuroLab has the research overview as the data were fed into five different machine-learning models to determine which combinations of data and models resulted in the most accurate predictions. 

“It was found that a particular machine-learning method called ‘random forests’ worked quite well, even using only posture and facial expression data.

Researchers also found that the models worked better the longer people interacted with the exhibit, since that gave them more data to work with. For example, a prediction made after a few minutes would be more accurate than a prediction made after 30 seconds. For context, user interactions with the exhibit lasted as long as 12 minutes. Frankly, this research has pave the way for new approaches to study how visitors learn in museums,” and ultimately, this shows that technology can be use to make learning more effective and more engaging.