課程目的：
本課程的目的在於幫助學生瞭解大數據分析與產業的關係，增強學生在本學程所學與未來就業的連結。產業實務經驗進入課室，減少學用落差，並有助於教學內容的活化與更新。
授課教師與分組：
本課程由資工系、工學院、管理學院、及教育研究所教師共同開課，依照教師研究專長分為6組：學習行為模式分析、學生作答行為分析、學生學習成效預測、個人化學習路徑剖析、學習資源對學習成效的影響探討、學習資源優化建議、以及其他與教育、數位學習相關之議題。學生依興趣選擇專題研究組別。透過不同類型的資料分析，帶領學生由大數據分析來探討相關教育議題；也幫助學生在探討教育議題時，能以合適的數據資料來佐證論點。
上課時間與方式：
本課程上課時間包含暑假與113-1學期。暑假期間進行共同課程，由業界教師到校授課，以及使用教育公開資料庫的專家學者到校分享大數據分析的方法。暑期上課時間約為30小時。
113-1學期開學後，由各組教師帶領與協助學生分組完成專題研究與發表。開學後由各組教師與學生約定討論時間，依時間完成專題進度。113-1學期開學後上課（或與指導老師討論）的時間約為24小時。
成果發表：
在113-1學期末，各組學生需繳交小論文（10頁以內），並進行專題成果發表。本學程將邀請校內外相關專長教師進行評分，並選拔優秀作品投稿發表，並參與全國教育大數據分析競賽。
Course purpose:
The purpose of this course is to help students understand the relationship between big data analysis and industry, and to enhance the connection between what students have learned in this course and their future employment. Industrial practical experience enters the classroom, reducing the learning-application gap and helping to activate and update teaching content.
Teachers and groups:
This course is jointly taught by teachers from the Department of Engineering, the School of Engineering, the School of Management, and the Institute of Education. It is divided into 6 groups according to teachers’ research expertise: analysis of learning behavior patterns, analysis of student response behaviors, prediction of student learning effectiveness, and analysis of personalized learning paths. , discussion on the impact of learning resources on learning effectiveness, suggestions for optimization of learning resources, and other issues related to education and digital learning. Students choose thematic research groups based on their interests. Through different types of data analysis, students are led to explore relevant educational issues through big data analysis; it also helps students to use appropriate data to support arguments when discussing educational issues.
Class time and method:
The class period of this course includes summer vacation and 113-1 semester. During the summer vacation, common courses will be held, with industry teachers coming to the school to teach, and experts and scholars using educational open databases coming to the school to share big data analysis methods. Summer class time is approximately 30 hours.
After the start of the 113-1 semester, teachers from each group will lead and assist students to complete special research and publication in groups. After the semester begins, teachers in each group will agree on a discussion time with the students and complete the topic progress according to the time. 113-1 The time for class (or discussion with the instructor) after the semester starts is approximately 24 hours.
Results published:
At the end of the 113-1 semester, students in each group are required to submit a short paper (within 10 pages) and publish their special results. This course will invite teachers with relevant expertise inside and outside the school to conduct grading, select outstanding works to submit for publication, and participate in the National Education Big Data Analysis Competition.
本課程由分組指導老師指定參考書籍
This course is assigned reference books by the group instructor.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 企業參訪心得企業參訪心得 Enterprise visit experience |
10 | 2 次參訪，每次參訪心得5分 |
| 業界講師演講業界講師演講 Industry lectures |
15 | 3 次演講，每次演講筆記5分 |
| 小組參與貢獻小組參與貢獻 Group participation and contribution |
35 | 小組成員對於彼此參與狀況與貢獻進行互評 |
| 小組期末成果小組期末成果 Final results of the group |
40 | 小組期末成果，包括數據分析呈現(25%)、分析報告(15%)。期末專題報告格式另定。 |
