## 2 Literature Review
### Engagement Assessment  
The second-generation real-time engagement assessment was recorded using physiological or neurological sensors. The sensor data analysis method focused on analyzing the data collected by various sensors during the learning process to assess engagement. Commonly used sensors included electroencephalograms, electrocardiograms [25], pressure sensors [26], eye-tracking [27], and skin potentials [28]. The physiological data collected by these sensors can be converted and validated to represent the learners' real-time learning data. However, the wearing of certain devices may cause discomfort and affect thelearning outcomes of the students; thus, third-generation automatic recognition technologies that use advanced computational techniques (e.g., deep learning models) are used to estimate an individual's learning engagement by analyzing facial expressions or body postures [29; 30; 31]. However, the computer vision-based approach has limitations. First, because the process of training the classifier relies on manual judgment, there is no guarantee that errors caused by manual judgment can be excluded; second, a single feature cannot represent the total learning engagement. For example, facial expressions can be used to measure emotional engagement [29] and cognitive engagement [30], whereas skin potential shows a moderate correlation between cognitive engagement and behavioral engagement [28].  
In previous collaborative learning studies, participants' conversations and actions were recorded through video, and their behaviors were coded to analyze their learning performance. Because this study adopts a cooperative learning approach, it is necessary to deal with situations where multiple subjects interact at the same time. Therefore, this study proposes the development of a speech recognition system that records student interactions during collaborative learning through audio recording, which is automatically converted into text by the speech recognition system and encoded with fuzzy comparisons based on a set of keywords. The advantage of using speech recognition is that the learning process can be recorded without affecting the subject's activity, and the collation of research data through speech recognition and AI can significantly improve the accuracy and reduce possible errors of manual recognition. Current studies adopted various deep learning networks, such as a recurrent neural network (RNN) coupled with an attention mechanism, to develop a prompted-enhanced sentiment analysis [32] or aspect-based sentiment system [33].  
However, to compensate for the fact that speech recognition may not represent the full range of engagement, previous studies coded engagement by analyzing participant interactions in video recordings [34]. The coding of engagement in the video was guided by behaviors outlined in the study's definition [35] and those associated with instructional behavioral engagement [36], which identifies whether the participant is actively engaged by observing behavioral actions. In summary, measuring engagement through speech recognition provides an objective measure that does not interfere with student learning and is an efficient and effective measurement tool [25]. A review of past research suggests that it is appropriate to measure learning engagement with physiological signals, such as gestures and speech sounds, and that there is no research that uses speech recognition of text followed by text analysis techniques to provide a measure of learning engagement.