學習分析在學習管理系統上的應用
Application of Learning Analytics in Learning Management Systems
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 學習分析在學習管理系統上的應用 HA107（光復） 2 節連堂 |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
For a long time, students learning portfolios and learning behaviors were untrackable and mentor/mentee apprenticeship was the only method of creating accurate, timely, and individualized instructional interventions. But nowadays, huge amounts of the tracking data is available from the learning management system (LMS) and leads to the importance of using analytics by the recognition of government stakeholders, education professionals, research institutes, and service providers. This course is designed for graduate students who have little or no previous exposure to Academic Analytics and/or Learning Analytics. After a successful completion of this course, students will be able to: Understand the demands of Learning Analytics at higher education institutions. Identify major methods and examples of Learning Analytics. Evaluate creative ways in data visualization. Judge the quality of Learning Analytics research. Criticize ethical issues in data-driven research such as Learning Analytics. Be motivated to pursue advanced degrees and careers in the new field of educational data sciences. Students without a strong statistical background is not an issue in the class. Moreover, since asynchronous and synchronous course sessions are scheduled, the instructor will also model online teaching for enrolled students.
None.
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Assessed work in this course comprises some small ‘Tasks’ and two major ‘Assignments’. Tasks are short applied activities closely aligned to the module content while Assignments are larger reports that synthesize your learning and independent study. Tasks: 40% Assignment 1: Evaluation of an LA tool 15% Assignment 2: Choose your own LA adventure 35% Attendance and Attitude: 10%
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Getting Started Introduction: What is Learning Analytics 3/6 |
| 第 2 週 | Best Practices (1) 3/13 |
| 第 3 週 | Best Practices (2) 3/20 |
| 第 4 週 | Prominent Topics of Learning Analytics 1 3/27 |
| 第 5 週 | Prominent Topics of Learning Analytics 2 4/3 |
| 第 6 週 | Qingming Festival / 清明節 4/10 |
| 第 7 週 | The role of data science in learning analytics 4/17 |
| 第 8 週 | LAK conference papers 1 4/24 |
| 第 9 週 | LAK conference papers 2 (Asynchronous) 5/1 |
| 第 10 週 | Traditional Analytical Approaches 5/8 |
| 第 11 週 | Analytic tools (1):Weka 5/15 |
| 第 12 週 | Analytic tools (1):Weka 5/22 |
| 第 13 週 | Analytic tools (2):Structured Query Language (SQL) 5/29 |
| 第 14 週 | SQL 6/5 |
| 第 15 週 | SQL Data Visualization (Tableau or Python) 6/12 |
| 第 16 週 | Databased, Open Data, and Institutional research 6/19 |
| 第 17 週 | General discussion |
| 第 18 週 | Final week 7/3 |
1. Lang, C., Siemens, G., Wise, A., & Gašević, D. (2017). Handbook of Learning Analytics. DOI: 10.18608/hla17 (R1 hereafter) 2. Larusson, J. A., & White, B. (2014) (eds.). Learning Analytics: From research to practice. New York: Springer. (R2 hereafter) 3. LAK'19 Companion Proceedings 4. Selected Articles
- 地點
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- 時間
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- 聯絡方式
- tcyang_@nctu.edu.tw