使用传统机器学习和深度学习模型对教学内容进行分类
Classification of Pedagogical content using conventional machine learning and deep learning model
摘要：
互联网和大量数字技术的出现带来了许多不同的挑战。在网络上发现了大量的数据，在大多数情况下，这些数据是非结构化和无组织的，这导致了这样一个事实，即使用和操纵这些数据是一个相当困难的过程。由于这一事实，使用不同的机器和深度学习技术进行文本分类已经获得了它的重要性，这改进了这门学科，并使科学家和研究人员对进一步研究更加感兴趣。本文旨在使用两种不同的模型对教学内容进行分类，即传统模型中的K最近邻（KNN）和深度学习模型中的长短期记忆（LSTM）递归神经网络。实验结果表明，KNN模型对教学内容的分类准确率达到92.52%，LSTM模型达到87.71%。
英文摘要：
The advent of the Internet and a large number of digital technologies has brought with it many different challenges. A large amount of data is found on the web, which in most cases is unstructured and unorganized, and this contributes to the fact that the use and manipulation of this data is quite a difficult process. Due to this fact, the usage of different machine and deep learning techniques for Text Classification has gained its importance, which improved this discipline and made it more interesting for scientists and researchers for further study. This paper aims to classify the pedagogical content using two different models, the K-Nearest Neighbor (KNN) from the conventional models and the Long short-term memory (LSTM) recurrent neural network from the deep learning models. The result indicates that the accuracy of classifying the pedagogical content reaches 92.52 % using KNN model and 87.71 % using LSTM model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
