توضیحات
ABSTRACT
One of the factors leading to the recent emergence of learning analytics is the increasing quantity of analyzable educational data. Considerable quantities of data are now available to scientific researchers through public archives like the Pittsburgh Science of Learning Center DataShop (Koedinger et al., 2010). Mobile, digital, and online technologies are increasingly utilized in many educational contexts. When learners interact with a digital device, data about that interaction can be easily captured or “logged” and made available for subsequent analysis. Papers have recently been published with data from tens of thousands of students. With the continued growth of online learning (Allen and Seamen, 2013) and the use of new technologies for data capture (Choudhury and Pentland, 2003), even greater scope of data capture during learning activities can be expected in the future, particularly as large companies such as Pearson and McGraw-Hill become interested in EDMand Massive Online Open Courses (MOOCs) and providers such as Coursera, edX, and Udacity generate additional data sets for research (Lin, 2012).
INTRODUTION
During the last decades, the potential of analytics and data mining —methodologies that extract useful and actionable information from large datasets–has transformed one field of scientific inquiry after another (cf. Summers et al., 1992; Collins et al., 2004). Analytics has become a trend over the last several years, reflected in large numbers of graduate programs promising to make someone a master of analytics, proclamations that analytics skills offer lucrative employment opportunities (Manyika et al., 2011), and airport waiting lounges filled with advertisements from different consultancies promising to significantly increase profits through analytics. When applied to education, these methodologies are referred to as learning analytics (LA) and educational data mining (EDM). In this chapter, we will focus on the shared similarities as we review both parallel areas, while also noting some important differences. Using the methodologies we describe in this chapter, one can scan through large datasets to discover patterns that occur in only small numbers of students or only sporadically (cf. Baker et al., 2004; Sabourin et al., 2011); one can investigate how different students choose to use different learning resources and obtain different outcomes (cf. Beck et al., 2008); one can conduct fine-grained analysis of phenomena that occur over long periods of time (such as the move towards disengagement over the years of schooling — cf. Bowers, 2010); and one can analyze how the design of learning environments may impact variables of interest through the study of large numbers of exemplars (cf. Baker et al., 2009). In the sections that follow, we argue that learning analytics has the potential to substantially increase the sophistication of how the field of learning sciences understands learning, contributing both to theory and practice.
Year: 2011
Publishe: Ryan S.J.d
By: George Siemens
File Information: English Language/ 19 Page / size:89KB
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سال : 2011
ناشر : Ryan S.J.d
کاری از : George Siemens
اطلاعات فایل : زبان انگلیسی / 19 صفحه / حجم :89KB
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