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<p>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% which is better than traditional algorithms like. Artificial Neural Network (ANN), Decision Tree (DT) and Support Vector Machine (SVM). The system generates personalized insights and adaptive feedback to support real-time learning improvement. The suggested IoT-LSTM model is effective to facilitate adaptive, intelligent, and student-centered. predicting engagement behavior and facilitating data-driven educational interventions learning. The model was developed and assessed with an openly available dataset from Kaggle instead of a dataset collected from a real-time IoT deployment. The IoT architecture proposed in this paper is a conceptual and simulation-based framework.</p>

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Research on monitoring and analyzing system of college students’ learning behavior based on internet of things and artificial intelligence

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Jing Zhou,
  • Yinjie Xiao,
  • Yancheng Kuang,
  • Hongshan Lv

摘要

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% which is better than traditional algorithms like. Artificial Neural Network (ANN), Decision Tree (DT) and Support Vector Machine (SVM). The system generates personalized insights and adaptive feedback to support real-time learning improvement. The suggested IoT-LSTM model is effective to facilitate adaptive, intelligent, and student-centered. predicting engagement behavior and facilitating data-driven educational interventions learning. The model was developed and assessed with an openly available dataset from Kaggle instead of a dataset collected from a real-time IoT deployment. The IoT architecture proposed in this paper is a conceptual and simulation-based framework.