基于深度学习的学生课堂专注度测评方法

Evaluating Student Engagement with Deep Learning

【目的】通过构建有效的专注度表情数据集及设计学生课堂专注度联合评价模型,解决现有学生专注度测评方法存在的缺乏相关表情数据集及模型准确率不高的问题。【方法】基于真实的在线课堂场景进行数据采集,构建适合专注度识别的表情数据集W-AttLe,设计改良的VGG模型对数据集进行评估及专注度表情识别;将表情得分与正脸得分结合构建学生课堂专注度的联合评价模型,计算被检测学生的实际课堂专注度水平得分。【结果】在专注度表情识别上,通过调参优化步骤对识别表情的网络结构进行调整和验证,结果表明所构建的VGG16+Dense+Dropout(lr=1e-5)改良模型在4种对比模型架构中的准确率最高,达到92%以上;在专注度评价上,联合专注度得分较专注度表情单一指标得分对学生专注度的评测更为精准。【局限】在训练模型的过程中没有设计更多的消融研究,未探究更深层次的神经网络。【结论】构建的W-AttLe人脸数据集适用于判别学生课堂专注度;提出的联合专注度评价模型弥补了单一指标模型的不足;提出的知识点测试与理解度自测结合的加权测试方案对联合专注度模型进行了有效验证。

[Objective]This paper constructs an expression data set of engagement degrees and designs a joint evaluation model for students’class engagement.It addresses the issues of lacking relevant expression data sets and the low accuracy of the existing models.[Methods]We collected data based on actual online classes and constructed an expression dataset suitable for engagement recognition.Then,we designed an improved VGG model to evaluate the dataset and recognize student engagement.Third,we combined the expression and face scores to establish a joint evaluation model for students’engagement and calculated the tested students’actual class engagement scores.[Results]We adjusted and verified the network structure through parameter tuning optimization for engagement expression recognition.The improved model VGG16+Dense+Dropout(lr=1e-5)had the highest accuracy among the four compared model architectures,reaching over 92%.The joint engagement score is more accurate for engagement evaluation than the single expression engagement score.[Limitations]We did not include more ablation studies in training the model;more research is needed to explore the deeper neural networks.[Conclusions]The dataset of W-AttLe is suitable for evaluating students’class engagement.The proposed joint engagement evaluation model outperforms the single index model.The proposed weighted test scheme combining knowledge point test and self-test of comprehension degree validates the joint engagement degree model.