OARS: Exploring Instructor Analytics for Online Learning

被引：6

作者：

Bassen, Jonathan ^{[1
]}

Howley, Iris ^{[1
]}

Fast, Ethan ^{[1
]}

Mitchell, John ^{[1
]}

Thille, Candace ^{[1
]}

机构：

[1] Stanford Univ, Williams Coll, Stanford, CA 94305 USA

来源：

PROCEEDINGS OF THE FIFTH ANNUAL ACM CONFERENCE ON LEARNING AT SCALE (L@S'18) | 2018年

关键词：

Learning analytics; real-time systems; instructor-centered design;

D O I：

10.1145/3231644.3231669

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Learning analytics systems have the potential to bring enormous value to online education. Unfortunately, many instructors and platforms do not adequately leverage learning analytics in their courses today. In this paper, we report on the value of these systems from the perspective of course instructors. We study these ideas through OARS, a modular and real-time learning analytics system that we deployed across more than ten online courses with tens of thousands of learners. We leverage this system as a starting point for semi-structured interviews with a diverse set of instructors. Our study suggests new design goals for learning analytics systems, the importance of real-time analytics to many instructors, and the value of flexibility in data selection and aggregation for an instructor when working with an analytics system.

引用

页数：10

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