基于短文本情感增强的在线学习者成绩预测方法

当前利用短文本情感信息进行在线学习成绩预测的研究存在以下问题: 1)当前情感分类模型无法有效适应在线学习社区的短文本特征, 分类效果较差; 2)利用短文本情感信息定量预测在线学习成绩的研究在准确性上还有较大的提升空间. 针对以上问题, 本文提出了一种短文本情感增强的成绩预测方法. 首先, 从单词和句子层面建模短文本语义, 并提出基于学习者特征的注意力机制以识别不同学习者的语言表达特点, 得到情感概率分布向量; 其次, 将情感信息与统计、学习行为信息相融合, 并基于长短时记忆网络建模学习者的学习状态; 最后, 基于学习状态预测学习者成绩. 在三种不同类别课程组成的真实数据集上进行了实验, 结果表明本文方法能有效对学习社区短文本进行情感分类, 且能够提升在线学习者成绩预测的准确性. 同时, 结合实例分析说明了情感信息、学习状态与成绩之间的关联.

Short-text Sentiment Enhanced Achievement Prediction Method for Online Learners

Research of online learning achievement prediction based on short text sentiment information has the following problems: 1) Current sentiment classification model cannot effectively adapt to short text features of online learning community, and classification effect is poor; 2) Prediction accuracy of online learning achievements using short text sentiment information has a lot of room for improvement. In view of above problems, this paper proposes a short text sentiment enhanced achievement prediction method. Firstly, short text semantics are modeled from the word and sentence level, and attention mechanism based on learner characteristics is proposed to identify the expression characteristics of different learners. Secondly, sentiment information is fused with statistics and learning behavior information, and use long-short term memory network to model learning state of learner. Finally, learner's grade is predicted based on learning state. Experiments were carried out on real data set composed of three types courses. The results show that our method can effectively classify short texts of the learning community and improve the accuracy of online learners' achievement predictions. At the same time, combined with case analysis, relationship between emotional information, learning status and achievement is explained.