摘要
SPOC是互联网与传统校园教学的有机结合。作为信息化教学平台的一部分,成绩预测模型可为学生相关课程成绩进行合理预测。针对SPOC学生成绩样本数量小的特点,提出一种基于相关向量机的概率式成绩预测方法。结果表明,模型较神经网络等传统数据挖掘方法有更精确的预测性能,有助于师生及时了解掌握知识的程度,提高教学质量,为推广SPOC提供技术支持。
Small Private Online Course （SPOC） combines E-learning and traditional campus teaching. As a module of digital teaching platforms, the grade prediction model is capable of predicting a reasonable grade for students in a course. Since SPOC is featured with its small sample size, a probabilistic grade prediction model is proposed based on Relevance Vector Machine （RVM）. Compared with the data mining methods like neural networks, simulation results show that the RVM exhibits more accurate prediction performance, helping teachers and students to keep abreast of the degree of mastery of knowledge, improve teaching quality, and provid technical supports for the promotion of SPOC.
出处
《苏州科技学院学报（工程技术版）》
CAS
2016年第1期77-80,共4页
Journal of Suzhou University of Science and Technology （Engineering and Technology）
基金
江苏省教改重点项目(2013JSJG063)
江苏省高校自然科学基金项目(15KJB480002)
关键词
相关向量机
成绩预测
SPOC
relevance vector machine
grade prediction
SPOC
作者简介
马洁明（1984-），男，江苏苏州人，讲师，博士，从事人工智能及其应用方面的研究。
