本研究已完成職能訓練系統，還未取得得施測學習者資料，為了瞭解本研究所使

本研究已完成職能訓練系統，還未取得得施測學習者資料，為了瞭解本研究所使用的演算法CART決策樹分類預測模型可行性，本研究利用Kaggle上的公開學生學業成績數據集資料進行測試，準確率為0.831。

結果 (英文) 1:

This study has been completed functional training system,
has not made too Surveying learner data,
in order to understand the feasibility of forecasting algorithms CART decision tree classification model used in this study,
in this study the use of public data on student academic performance data sets were of Kaggle test,
the accuracy was 0.831.

結果 (英文) 2:

This study has completed the functional training system,
Data for learners who have not yet been tested,
In order to understand the feasibility of the algorithm cart used in this study to determine the tree classification prediction model,
The study was tested using publicly available student academic performance data data data on Kaggle.
The accuracy is 0.831.

結果 (英文) 3:

This study has completed the functional training system,
The data of the tested learners have not been obtained,
In order to understand the feasibility of cart decision tree classification prediction model,
In this study, the data set of open student academic performance on kaggle was used for testing,
The accuracy was 0.831.