Classifying Emotions of Digital Immigrants while using Software Application based on Facial Features

Ramon L. Rodriguez, Mideth B. Abisado, Myron Darrel L. Montefalcon, Jay Rhald Padilla, Elcid A. Serrano, NU-Dasmarinas
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Abstract

In today’s age, the use and access to technology have become essential aspects of every individual. However, digital immigrants, individuals raised before the digital age, need help to cope with this advancement. In this study, the Education Technology Office conducted an onsite learning intervention to teach the digital immigrant faculty members about using Microsoft Applications such as Office365, Microsoft Teams, and Microsoft Forms. The faculty members’ facial expressions during the intervention were recorded using a web camera. Five consented videos of 1 hour were obtained in the data collection process. The video dataset was processed using the OpenFace toolkit to detect the facial features per frame. Afterward, feature selection and extraction of Action units were applied before fitting to the LSTM model. The LSTM model yielded a recognition accuracy of 97.3% on the validation set. In the analysis of facial expressions based on time, it was observed that as time progressed during the learning intervention, there were not many changes in the emotion of the participants. Also, the most frequent emotion recognized in different timestamps (initial, middle, final) part was ’happy,’ which may indicate that the participant has enjoyed the intervention and may suggest that it is effective. While other emotions were also evidently recognized, such as ’sad’ and ’surprised’, which might indicate the difficulty or challenges encountered by the digital immigrants during the learning intervention.
基于面部特征的软件应用对数字移民情绪的分类
在当今时代,技术的使用和获取已成为每个人必不可少的方面。然而,在数字时代之前长大的数字移民需要帮助来应对这种进步。在这项研究中,教育技术办公室进行了现场学习干预,教数字移民教师如何使用微软应用程序,如Office365,微软团队和微软表单。在干预过程中,教师的面部表情被网络摄像机记录下来。在数据收集过程中获得5个1小时的同意视频。使用OpenFace工具包对视频数据集进行处理,检测每帧的面部特征。然后,对动作单元进行特征选择和提取,拟合到LSTM模型中。LSTM模型在验证集上的识别准确率为97.3%。在基于时间的面部表情分析中,我们观察到,在学习干预过程中,随着时间的推移,参与者的情绪并没有太多变化。此外,在不同的时间戳(开始、中间、最后)中,最常见的情绪是“快乐”,这可能表明参与者喜欢干预,可能表明干预是有效的。而其他情绪也被明显识别,如“悲伤”和“惊讶”,这可能表明数字移民在学习干预过程中遇到的困难或挑战。
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