Student Emotion Recognition in Computer Science Education: A Blessing or Curse?

Abstract

One of the key skills in the fourth industrial revolution is the ability to program. To attain this skill, many prospective students study for a degree in computer science or a related field. An important skill in computer science is the ability to solve for a particular problem by programming an application. However, some challenges exist that make teaching this skill difficult, which leads to student frustration and a decrease in grades. These challenges can be attributed to a lack of access to appropriate skill-building or disjoint teaching methods that are not applicable to the student, which is especially prevalent with some inexperienced educators. Using teaching methods, which a student cannot relate to can lead to distance between the taught skill and the student. The article aims to address this distance by proposing a model that derives user sentiment with affective computing methods and leveraging the sentiment outcome to support the educator by providing feedback relevant for teaching. The technology will then allow the educator to adjust teaching and provide a more personalized teaching experience cognizant of classroom concepts with a lower level of understanding or that evoke certain emotions. It can also provide an informal assessment of content delivery by using student sentiment to infer whether concepts are well received. The preliminary prototype shows there is value in using assistive technologies in the physical classroom to achieve adaptive student learning. However, the onus is still on the educator to be able to react correctly to compensate for the lack of understanding for it to be an effective tool.