Research on Monitoring and Analyzing System of College Students’ Learning Behavior Based on Internet of Things and Artificial Intelligence

The swift advancement of the Internet of Things (IoT) along with Artificial Intelligence (AI) technologies has changed the face of smart education by allowing constant supervision and evaluation of student learning habits. Nevertheless, the present-day systems still cannot cope with time-series behavioral data processing efficiently and also do not yield correct real-time engagement predictions. This study aims to develop an IoT-based monitoring and analysis system with a Long Short- Term Memory (LSTM) deep. intelligent education learning model to forecast and improve the learning behaviour of the students. environments. The wearable sensors and smart classroom devices were used to gather time-series data. measuring variables of attention duration, Internet activity, keystroke, and physiological parameters. indicators. Pre-processing of data was done with Moving Average Filter to eliminate noises and Min–Max. Normalization through scaling, although features extraction developed behavioural indicators such as study. duration, consistency of concentration and frequency of participation. The optimized LSTM model, written in Python, was trained on sequential data with hyperparameters optimized to tune sequence. length, batch size, and learning rate, and dropout rate. The experimental assessment proved that the prediction accuracy of the proposed model was 98.7