探討自我學習方式對AI課程學習之影響—以自然語言處理章節為例

隨著AI技術的熱潮興起，世界各國提出各式AI相關教育政策，網路教學資源更是琳瑯滿目。然而AI人才仍舊嚴重不足，因此針對大多數人學生生涯的最後一哩路──大學之中，應如何提升教學品質與學習效率，激起更多學生對AI的理解及興趣是為一大重點。　　本研究針對大學部資訊管理系共80人，以準實驗室實驗法探討自我學習方式與傳統教學方式之於學習成績的差異，並以問卷法就網路使用頻率、運算思維能力、自我學習準備度及學習動機為變數，探討是否對不同教學方式之學習成績有所影響。研究結果顯示，在自我學習與傳統教學模式下，學生的學習成績進步程度並無差異，但是自我學習讓學習更具自主性，因此建議教學者可適度讓學生自學，降低人力與時間成本以利規劃更完善的課程。

關鍵字

AI教育；自我學習；運算思維；學習動機

並列摘要

With the upsurge of AI technology, countries around the world have proposed various AI-related education policies. Online teaching resources are even more dazzling. However, there is still a serious shortage of AI talents. Therefore, for most students, the last mile of their students' careers-in universities, how to improve the quality of teaching and learning efficiency and arouse more students' understanding and interest in AI is a major focus. This research is aimed at 80 people in the Department of Information Management of the University Department, using a quasi-laboratory experiment method to explore the difference in academic performance between self-learning methods and traditional teaching methods. 　The questionnaire method is used to investigate the frequency of internet use, computational thinking ability, self-learning readiness and learning motivation as variables to explore whether it has an impact on the academic performance of different teaching methods. 　The research results show that there is no difference in the degree of progress of students' academic performance under self-learning and traditional teaching models, but self-learning makes learning more autonomous. Therefore, it is recommended that teachers allow students to teach themselves appropriately to reduce manpower and time costs. Plan a more complete course.