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Key Technology Partnership, Educational Data Mining & Learning Analytics

发布日期:2016年11月29日

物理学院“博约学术论坛”系列报告 第 94 期

Title:Key Technology Partnership, Educational Data Mining & Learning Analytics
报告人:Jurgen Schulte(新葡萄京娱乐场8455与悉尼科技大学间战略合作高级访问学者)
时   间:2016年11月29日星期二 上午10点
地   点:新葡萄京娱乐场8455中心教学楼610

ABSTRACT: There are two parts to this seminar. The first part reports about the objective and expectations of the Key Technology Partnership program between BIT School of Physics and UTS (University of Technology Sydney) Faculty of Science; and opportunities for graduate students to obtain a dual PhD at UTS within the KTP program as part of a research collaboration between researchers at BIT and UTS.
The second part focuses on the opportunities in Educational Data Mining and Learning Analytics. Educational Data Miming develops methods and applies techniques from statistics, machine learning, and data mining to analyze data collected during the process of teaching & learning and its administration. Educational Data Mining has been employed to provide universities with intelligence about the performance of their institution and appropriateness or success of their degree programs and teaching staff. Learning Analytics applies techniques from information science, sociology, psychology, statistics, machine learning, and data mining to analyze data collected during education administration and services, teaching and learning. In contrast to Educational Data Mining which emphasizes on high-level automated system generated responses, Learning Analytics enables human tailoring and takes often a pro-active, and real-time role with direct intervention in educational practices and student learning.

Curriculum Vitae
Jurgen Schulte is recipient of the Australian Government National Citation Award 2016 for his outstanding contributions to university teaching and learning, and recipient of the 2015 University of Technology Sydney Learning and Teaching Award Learning.Futures. His physics background is in applied industrial semiconductor research (Japan), high-performance parallel computing in nanotechnology and engineering (USA), and theoretical nuclear and elemental particle physics (Germany). He is faculty at UTS since over 20 years. His current research interest is in educational data mining, learning analytics and development of authentic learning curricula.

For a list of publications and academic activities, please see:
http://schulte.estate/professional-profile
 

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