A Convolutional Neural Network-Based Predictive Model for Assessing the Learning Effectiveness of Online Courses Among College Students

With the development of artificial intelligence (AI) technology, higher education institutions usually consider both online courses and offline classrooms in the course design process. To verify the effectiveness of online courses, this study designed a deep learning model to analyze the learning behavior of online course users (college students) and predict their final grades. Firstly, our method summarizes several learning features that are used in machine learning models for predicting student grades, including the performance of users (college students) in online courses and their basic information. Based on nutcracker optimization algorithm (NOA), we designed a multi-layer convolutional neural network (CNN) and developed an improved NOA (I-NOA) to optimize the internal parameters of the CNN. Prediction mainly includes two steps: firstly, analyzing users' emotions based on their comments in online course forums. Secondly, predict the final grade based on the user's emotions and other quantifiable learning features. To validate the effectiveness of INOA-Based CNN (I-NOA-CNN) algorithm, we evaluated it using a dataset consisting of five different online courses and a total of 120 students. The simulation results indicate that compared with existing methods, the I-NOA-CNN algorithm has higher prediction accuracy, and the proposed model can effectively predict the learning effect of users.

@article{Zhang2024,
  title     = {A Convolutional Neural Network-Based Predictive Model for Assessing the Learning Effectiveness of Online Courses Among College Students},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xuehui Zhang and Lin Yang},
  doi       = {10.14569/IJACSA.2024.0150951},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150951}
}