The cognitive states of students in a lecture can give good indications of student concentration and learning, and therefore, modeling them would have a positive impact on their quality of education by enabling the intervention of instructors. In a traditional class, the instructor would assess the students’ level of attention. However, the assessment may not be accurate for a variety of reasons. Additionally, this creates a burden for the instructors. Wearable sensors and signal processing techniques could provide opportunities to assist teachers with this assessment. In this paper, we propose a methodology to model students’ cognitive states by leveraging hand motion captured with smart watches. Following the application of a sequence of signal processing techniques to the raw data, we generate features which describe characteristics of the hand motion in a group of students. The most prominent features are selected for machine learning algorithms. By applying cross validation, the results of experiments on 30 students in two lectures offer accuracies of 98.99% and 95.78% for predictions of ‘interest level’ and ‘perception of difficulty’ on the topics covered during the lectures.

Here are the massive code used for the paper:

Ziwei Zhu, Sebastian Ober, Roozbeh Jafari. Modeling and Detecting Student Attention and Interest Level Using Wearable Computers. IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2017.

The folder here includes matlab analysis code and python machine learning code.