Learning Analytics for Improved Course Delivery: Applications and Techniques

Tich Phuoc Tran, Tony Jan, S. N. Kew
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引用次数: 1

Abstract

Learning Analytics (LA) is an emerging research field that harnesses the power of data modelling, data mining and visualization to enhance the understanding of teaching and learning as well as supporting the personalization of education. Typical LA applications include dashboards displaying course progress, intelligence reports tracking the use of educational resources, and systems that predict students' academic performance and identify struggling students. In this article, we review the major elements of LA applications, including typical workflows, data types, and approaches to analytics. In addition to the basic reporting tools available in Learning Management Systems (LMSs), the article provides insights into how Machine Learning (ML) can detect the students at risk of failing. Finally, six educational applications in which data analytics helps improve course delivery are discussed.
改进课程交付的学习分析:应用和技术
学习分析(LA)是一个新兴的研究领域,它利用数据建模、数据挖掘和可视化的力量来增强对教学和学习的理解,并支持个性化教育。典型的LA应用程序包括显示课程进度的仪表板,跟踪教育资源使用情况的智能报告,以及预测学生学习成绩和识别困难学生的系统。在本文中,我们回顾了LA应用程序的主要元素,包括典型的工作流、数据类型和分析方法。除了学习管理系统(lms)中可用的基本报告工具外,本文还提供了机器学习(ML)如何检测有失败风险的学生的见解。最后,讨论了数据分析有助于改进课程交付的六种教育应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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