## 3 Research Methodology
### Emotional Engagement Level Calculation  
After systematic word determination using the NLP package, the keywords in each sentence were compared with the sentiment dictionary using the Chinese Sentiment dictionary [38] and NTUSD (National Taiwan University Sentiment Dictionary) [39] to determine the number of positive and negative words in each sentence. The NTUSD is a dictionary of Chinese words published in 2006 and is available free of charge for academic use. The dictionary includes 11,088 semantic words in both simplified and traditional Chinese, including 2812 positive and 8276 negative words. The dictionary is labeled as positive or negative only, with no distinction between intensity or other additional information [40].  
Another emotion dictionary integrates the China HowNet emotional dictionary with other words [39; 41] through web crawling containing a total of 7376 positive words and 12,646 negative words. The original text is in simplified Chinese. After converting it into traditional Chinese, it was used as another emotion dictionary.  
In this study, the sentiment calculation formula [40] was modified. As in Equations (1)-(3), \(N_{c_{i}}\) denotes the negative score of the \(C_{i}\) sentence; \(f_{n_{c_{i}}}\) is the frequency of negative words of the \(C_{i}\) sentence. \(P_{c_{i}}\) denotes the negative score of the \(C_{i}\) sentence; \(fp_{c_{i}}\) is the frequency of positive words of the \(C_{i}\) sentence. Equation (1) calculates the negative score of a sentence in the learner discussion after converting speech to text. Equation (2) calculates the positive score of a learner's sentence. Equation (3) calculates the sentiment score \(S_{c_{i}}\) to present positive or negative intention for the sentence. The sentiment score was then used as indicators of learners' immediate emotional engagement levels.  
\[N_{c_{i}}=\frac{fn_{c_{i}}/\sum_{j=1}^{m}}{fn_{c_{j}}}{fp_{c_{i}}/\sum_{j=1}^{ n}}{fp_{c_{j}}/\sum_{j=1}^{n}}{fn_{c_{j}}} \tag{1}\]  
\[P_{c_{i}}=\frac{fp_{c_{i}}/\sum_{j=1}^{n}}{fp_{c_{j}}}{fp_{c_{i}}/\sum_{j=1}^{ n}}{fp_{c_{j}}/\sum_{j=1}^{n}}{fn_{c_{j}}} \tag{2}\]  
\[s_{c_{i}}=(P_{c_{i}}-N_{c_{i}}) \tag{3}\]  
The emotion corpus is based on the NTUSD (National Taiwan University Sentiment Dictionary), published in 2006, which provides 2812 positive and 8276 negative Chinese words and is widely utilized for academic sentiment analysis in Chinese texts. However, its static nature and potential lack of intensity gradation limited its applicability to our dynamic, education-focused dataset. To address this limitation, we supplemented it with another sentiment dictionary (containing 7376 positive and 12,646 negative words), thereby enhancing coverage and ensuring greater relevance to contemporary language use among students.  
The sentiment calculation formula was modified to align with the objectives of evaluating learners' emotional engagement in STEAM education using text from learning sheets and real-time discussions. The original formula determined emotions using binary classification, labeling them solely as positive or negative. To address this, we made several adjustments: we incorporated the frequency and relative proportion of positive and negative words within each sentence into the calculation, and by setting a threshold value, we used the quantified emotion scores to classify the polarity of the emotions. This approach enables us to assess and infer each student's emotional engagement.