Designing a Tool to Address the Depression of Children During Online Education

Advances in communication and information technology have changed the way humans interact. During the COVID-19 pandemic, the technology for communication has caused depression and anxiety, including among children and teens. Depression among children and teens may go unrecognized and untreated, as parents and teachers may have difficulty recognizing the symptoms. COVID-19 has changed traditional learning methods, forcing children to stay home and connect through online education. Although some children may function reasonably well in less-structured environments, many children with significant depression suffer a noticeable change in social activities, loss of interest in an online school, poor online academic performance, or changes in appearance. Home quarantine has affected children's mental health, and it has become challenging for school counselors to predict depression in many children participating in online education. This study aims to design and develop a tool for predicting depression among children aged 7 to 9 years old by recording students' online classes and sending a note to the child's academic file. The idea of needing this tool arose as an output for applying the design thinking approach to the online education website during COVID-19. This inspired the authors to combine the lecture recordings and the prediction of depression into one tool. Image processing techniques are applied to generate the results predicted by the model on the collected videos. The overall accuracy for classifying depressed and not depressed videos is 89%.