利用学习向量化样本分类的在线学习成绩预测

对网络在线学习者产生的数据进行记录和分析，并为其提供精准化的个性化服务是在线教育发展的重要方面.本文以学习者在平台上产生的日常学习数据为样本，综合其最具代表性的五种影响因子，通过学习向量化神经网络对样本进行分类，得到基于BP神经网络的在线学习成绩预测数据.在模型中采用遗传算法有效优化BP神经网络的权重和阈值，在提高预测精度的同时加快模型的收敛速度.最后与其他两种模型进行对比分析，结果表明：该模型进行预测的结果与真实的成绩分布基本一致，具有很高的可信度，能够为有效的预测学习状态提供决策依据，具有一定的工程应用价值.

Method of Using Learning Vector Classification Samples to Predict Online Achievements

Recording and analyzing the data generated by online learners on the Internet and providing accurate and personalized services is an important aspect of online education. This study takes the daily learning data generated by learners on the teaching platform as a sample, synthesizes its five most representative influencing factors, classifies samples by Learning Vector Quantization (LVQ) neural network, and obtains online learning academic performance prediction data based on BP network. The genetic algorithm is used in the model to effectively optimize the weights and thresholds of the BP network, which accelerates the convergence of the model while improving the prediction accuracy. Finally, compared with the other two models, the results show that the model''s prediction results are basically consistent with the real performance distribution. It has a high degree of credibility and provide a decision-making basis for effective prediction of learning status, which has certain value in engineering application.