Fig. 4: Machine-learning SVR models predict L2 proficiency. | npj Science of Learning

Fig. 4: Machine-learning SVR models predict L2 proficiency.

From: Language and nonlanguage factors in foreign language learning: evidence for the learning condition hypothesis

Fig. 4

The predictability of L2 (English) HKDSE grades was estimated by the correlation coefficients (cc) between the predicted and the observed language proficiency scores based on tenfold cross-validation with 10,000 iterations. a The importance ranking of all predictors of L2 proficiency, where the x-axis represents the importance value and y-axis represents the variables. The importance value was calculated from tenfold cross-validation with 1,000 iterations. b When all predictors are included in the SVR model to predict L2 (English) HKDSE grades, the distribution of prediction values was significantly different from the null distribution (p < 0.001, Cohen’s d = 7.17). c With only L1 (Chinese) HKDSE grades as the predictor of English HKDSE grades, the distribution of prediction values was also significantly different from the null distribution (p < 0.001, Cohen’s d = 6.45).

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