本研究將分別探討幾種電腦輔助科技技術在教育上的應用潛力：語音文本識別、電腦輔助翻譯和深度學習卷積神經網路。根據布魯姆的教育目標分類學理論，教育目標可分為三大領域:認知領域、情感領域，和技能領域。本研究通過語音文本識別和電腦輔助翻譯技術對跨文化學習過程中技術準確度的分析，從而提高學生跨文化學習的認知；運用卷機神經網路方法對人臉面部表情準確度的分析，從而有助於教師改進未來教學策略，達到提高學生學習績效的目的。
隨著現代科技的發展，大多數人都擁有自己的電腦移動設備，隨時隨地接入互聯網，因此跨文化的交流與互動變得更加廣泛和活躍。為了解決交流中多國語言障礙問題，我們在本研究中應用語音文本識別(STR)和電腦輔助翻譯(CAT)系統來測試翻譯的準確性和速度。這項研究的目的是：(I)檢驗此方法是否可行，以促進參與者的跨文化理解和跨文化敏感性；(II)探討在STR及CAT過程中所產生的文本的可懂度和準確性；及(III)研究參與者對干預的看法。來自13個國家的21名大學生參加了這項研究。統計分析顯示，使用STR和CAT系統對大多數語言的簡單、日常生活交流是有用的；特別是對於複雜和高級的主題，英語或與英語類似語言的翻譯效果更好。實驗證明跨文化教育可以通過電子學習技術來支援，學習者可以通過網路隨時隨地克服語言障礙進行溝通。
跨文化學習可以在e-learning的環境下進行，而網路學習與面對面教育(傳統教育)的區別之一是情感因素的缺乏。眾所周知，情感在學習過程中起著重要的作用。因此，本研究將採用深度學習的方法來檢測情感，運用卷積神經網路(CNN)對優化後的人臉表情活動區進行分析，從而提高面部表情的識別率。我們在本研究中利用CK資料庫、JAFFE資料庫和NVIE資料庫等常用的公共資料庫對CNN方法預測面部表情的精確度進行評價，採用十折交叉演算法對CNN的效能進行驗證。結果顯示，卷積神經網路對表情具有較好的精度預測，同以往演算法作用相同資料庫對比， 預測CK資料庫表情精確度達到98.5%，JAFFE資料庫和NVIE資料庫分別是98.41%和96.51% 。本研究初步提出虛擬的e-learning學習環境中如何應用CNN進行預測的模型框架。 本研究的局限性是卷積神經網路方法的訓練時間有待進一步改進，未來工作需要在實際的電子學習系統中進行CNN方法的實施與測試，例如實際的網路跨文化學習。

The study introduces two technologies to explore the education potential individually, Speech-to-text recognition with Computer-aided translation, and Convolutional Neural Network. The connection bond between these two technologies mainly relates to the Bloom’s taxonomy: educational cognitive and affective theory, motivated by the development trend of technology.
At present, most people have computing devices and easily access to the Internet, so cross-cultural communication and interaction are more extensive and active. To address the issue of the language barrier, we applied speech-to-text recognition (STR) and computer-aided translation (CAT) systems in the present study to test the translation accuracy and speed. The study aims at (i) to test the feasibility of our approach to facilitate the cross-cultural understanding and the sensitivity of the participants, (ii) to explore the intelligibility and accuracy rates of texts generated during the STR and CAT process, and (iii) to examine the participants’ perceptions of the intervention. We enrolled twenty-one university students who represented thirteen nationalities to participate in this study. After statistical analysis, our finding suggest that it is useful to employ STR and CAT systems for daily life communication in most languages, especially in English or similar language, the systems perform better accurate translation for the complex and advanced topics.
Cross-cultural education can be supported by e-learning technology. With e-learning system, cross-cultural education can take place at any time and at anywhere, as long as there is network around learners. One of the differences between e-learning and face-to-face education (traditional education) is the lack of affective factors. It is well known that emotion plays an important role in learning process, therefore, deep learning method will be employed to detect emotion. The recognition rate can be improved if we use reasonable machine leaning models. Convolutional Neural Network is employed to analyze the optimized facial expression active region in the study. We evaluated the proposed method using public available and widely used databases such as the CK+ database, the JAFFE database and the NVIE database. Ten-fold cross validation is executed for each of the three independent databases to test the performance of the proposed method. The result shows Convolutional Neural Network achieves better accuracy performance compared with the earlier work, the average accuracies on the CK+ database, the JAFFE database and the NVIE database are 98.5%，98.41 & 96.51% respectively. In this study, a model framework of how to use CNN to predict in virtual e-learning environment is proposed. The limitation of the study is the training time for the Convolutional Neural Network method needs to be improved further, and needs to be tested in the real e-learning system such as intercultural e-learning scenario.

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