基于k-means算法的学生在线学习行为研究

Students′ online learning behavioral research based on k-means algorithm

研究学生在线学习行为需要对海量数据加以分析,为了提高数据分类效果,降低分析误差,基于k-means算法,对学生在线学习行为展开分析.基于x API技术模型采集学习者行为数据;利用k-means算法对在线学习行为数据进行分类,针对在线学习不同阶段,设定在线学习行为评价指标;依靠算法数据,构建学生在线学习行为评估模型.实验结果表明,该研究方法根据评估结果对学生期末成绩进行预测并对成绩进行评级,预测成绩的评级结果与实际成绩评级完全一致,证明了此设计方法能够有效地对学生在线学习行为进行分析,分析结果较为准确,具有一定的可靠性.

Because the study of students′ online learning behavior needs to analyze massive data,in order to improve the data classification effect and reduce the analysis error,analyzes students′ online learning behavior based on k-means algorithm.Collect learner behavior data based on xAPI technology model.The k-means algorithm is used to classify the online learning behavior data,and the online learning behavior evaluation index is set according to the different stages of online learning.Based on the algorithm and data,an online learning behavior evaluation model is constructed.The experimental results show that this research method predicts and grades students′ final grades according to the evaluation results,and the rating results of the predicted grades are completely consistent with the actual grades,which proves that the design method can effectively analyze students′ online learning behavior,and the analysis results are accurate and reliable.