ABSTRACT

This article serves as a reflection on the papers accepted for this special issue. Although 2011 saw the first learning analytics conference, the field draws from a long history of research in associated disciplines such as psychology, statistics, education and computer science. However, the novelty of learning analytics research stems from the access and analysis of large, yet granular, data sources that are generated from student interactions in online activities. The vast array and volume of accessible learning data have promulgated new approaches to understanding and measuring learning. As researchers pursue such algorithmically generated insights, there are parallel challenges to individual ethics and privacy and a broader consideration of the impact of an increased quantification of education. This special issue on the role of learning analytics in open, flexible, and distance learning (OFDL) environments aims to bring alternate perspectives to bear on how such analyses are improving learning while still addressing social and cultural challenges.

摘要

本文是对本期特刊所接受论文的反思。尽管2011年召开了首届学习分析会议，但该领域借鉴了心理学，统计学，教育和计算机科学等相关学科的悠久研究历史。但是，学习分析研究的新颖性来自对在线活动中学生互动产生的大型但细粒度的数据源的访问和分析。可访问的学习数据的种类繁多和数量庞大，为理解和衡量学习提供了新的方法。当研究人员追求通过算法得出的见解时，个人道德和隐私面临着并行的挑战，并且越来越多地考虑增加教育量化的影响。