Effectiveness of quizizz on Emotion expression on a social media platform using mobile brain-computer interfaceschool students’ English grammar learning

Abstract

Today, expressing emotions via social media can be done by manually choosing the closest emoji to their emotion from the selection of emojis. No real-time emotion detection is applied to provide the user with a personalized experience. This study explored the possibility, and the readiness, of using mobile Brain-Computer Interface (BCI) to improve user’s experience in social media platforms. To understand how users perceive emotional expression on social media, the study conducted an online survey involving 50 participants. Interview sessions with eight participants followed this to elicit their perspectives on selecting emojis for expressing emotions. A qualitative analysis was applied to analyse the transcript from the interview session. The findings indicated that users preferred to express their emotions indirectly and elaborately, supported by emojis and stickers. An emotion expression model on a social media platform using a mobile brain-computer interface was proposed due to the absence of such a model in facilitating social media integration with BCI. The model consisted of the input, translation from the input and the output. The proposed model was evaluated through experts’ review. Further, to exemplify the usage of the proposed model, a prototype was developed as a platform for users to detect and express their emotions based on their preferences and for emotional engagement. The evaluation of the proposed prototype was divided into two: emotion expression and emotion detection. For emotion detection, the accuracy was evaluated using user rating, and for emotion expression, Retrospective Thinking aloud (RTA) and Usability testing using the System Usability Scale (SUS) form was performed. The evaluation results stated that the user rating accuracy was 87.5 %, and the SUS score was 81.6, which fell on the excellent rating. This work pushes the boundaries of typical BCI into a leisurely usage of emotion detection and expression on social media platforms.