## 4 Discussion
### Familiarity Rating Prediction of Domain Concepts with Tree-Based Regression Models  
Various approaches exist to address familiarity ratings, including expert ratings, corpus-based statistics, and regression-based prediction. However, using expert ratings and constructing a corpus for domain concept familiarity require substantial effort and resources. A way to automatically evaluate domain concept familiarity is still needed. In line with this fact, our findings demonstrate the effectiveness of using domain concepts' lexical features to predict familiarity ratings. Six tree-based regression models were implemented here to estimate domain concept familiarity. The models' R\({}^{2}\) values were consistently low. However, a low R\({}^{2}\) value does not always imply a poor model fit: these values can be quite low for completely linear and noticeably non-linear models [54]. Studies showing inadequate model fits frequently ascribe these results to insufficient model complexity  
Figure 6: SHAP dependency plots for the three most important features from the prediction model. (**a**): SHAP dependency between SI_C1 and SI_C2; (**b**) SHAP dependency between CL and SI_C1; (**c**): SHAP dependency between SN and SI_C2(i.e., to capture complex relationships within data), limited samples, or noise [55; 56]. We identified CatBoost as optimal for predicting domain concept familiarity after comparing the six models' results: with the lowest MAPE and the highest R2 value, CatBoost accurately estimated the target variable--likely due to its symmetric tree growth strategy [47]. The encouraging experimental results strengthen the case for bringing pretrained word embeddings to automatic extrapolation of familiarity ratings, which is conducive to the expansion of psycholinguistic resources. This finding is in line with the findings of a great deal of the previous work in familiarity rating prediction [14; 15; 16; 17; 18]. For example, [PERSON] and [PERSON] implemented a supervised learning algorithm with word2 vec-based representation to collect 350,000 German lemmatized words [15]; [PERSON] and [PERSON] [17] used pretrained word embedding models to predict the familiarity ratings of 85,942 words in the MRC (Machine Readable Dictionary) database. Furthermore, we chose the skip-gram word vectors to capture the semantic information within domain concepts. Our experimental results support the view that the accuracy of extrapolating psycholinguistic ratings using different word vector representations can be informative about the word vector representations themselves [57]. Thus, opportunities exist for fellow scholars to explore the effect of different types of word embedding models on domain concepts' familiarity ratings. Other research could extend our approach to investigate the individual difference in the perception of domain concepts' familiarity within educational context, such as online collaborative learning [1] and language learning [3]. In addition, the performance of familiarity rating models can likely improve via several means: gathering additional data, using advanced feature engineering techniques, and applying optimization algorithms to fine-tune model parameters. For example, [PERSON] et al. increased rating-task accuracy by integrating CatBoost and the sparrow search algorithm [58]. Their work offered valuable insight to enhance CatBoost's performance.