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 large data analysis and industry, and to enhance the connection between what students learn and their future careers in this course. The experience of industry is to enter the classroom to reduce the gap in learning and help to revitalize and update teaching content.
Teaching teachers and divisions:
This course is jointly organized by teachers from the Department of Labor, Institute of Technology, Institute of Management, and Institute of Education. According to the teacher's research, it is divided into 6 groups: learning behavior pattern analysis, student answering behavior analysis, student learning effectiveness prediction, and personal chemistry learning path analysis , Discussion on the impact of learning resources on learning effectiveness, suggestions on learning resource optimization, and other issues related to education and digital learning. Students choose topic research groups based on their interests. Through different types of data analysis, students are led to large data analysis to explore related educational issues; it also helps students to use appropriate data to prove their opinions when exploring educational issues.
Time and method of class:
The course lasts include summer vacation and 113-1 school periods. During the summer vacation, a shared course is conducted, with teaching from the industry teachers to the school, and expert students using the educational public database come to the school to share large data analysis methods. The summer class time is about 30 hours.
After the start of the 113-1 period, each group of teachers will lead and assist the students to complete the research and publication of topics. After the start of the school, each group of teachers and students will make a discussion time and complete the progress of the topic according to the time. 113-1 The time for classes (or discussions with coaching teachers) after starting school during the school period is about 24 hours.
Results published:
At the end of the 113-1 period, students of each group must submit a short article (within 10 pages) and publish a special result. In this course, relevant senior teachers at home and abroad will be invited to rate, and to subscribe and publish excellent works, and to participate in the national education data analysis competition.
本課程由分組指導老師指定參考書籍
This course is designated by the branch 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 Corporate 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 Talk about industry lecturers |
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。 |
