Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics in American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth.
Data-driven decision making, Big data, Learning analytics, Higher education, Rational decision making, Planning
This article explores how higher education is able to incorporate big data into policy and decision making and the implications of using big data for these purposes. The authors are careful to note that big data should inform decision making and not to strictly replace the knowledge and experience of qualified professionals and educators.
One main benefit of using big data is instructions are able to use live and real-time statistics in their decision-making process instead of delayed statistics. However, as of the time of this article, not enough scientists are trained to analyze and incorporate big data into their studies. Also, as it relates to online courses, there may be a bias effect happening as to what kind of student is taking online courses compared to traditional classroom-based students. As such, big data has greater benefits for online students compared to traditional classroom students.
Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9-20.
|Links to Article||https://scholar.google.com/scholar?hl=en&as_sdt=0%2C50&q=The+evolution+of+big+data+and+learning+analytics+in+American+higher+education&btnG=|
|Publication Type||Journal Article|
|In Publication||Journal of Asynchronous Learning Networks|
|Type of Research||Quantitative, Theoretical|
|Research Design||Mixed methods|
|Intervention/Areas of Study||Active learning, Adaptive learning, Administration, management, and leadership, including accreditation, financial models, and legal, Assessment, Course, program, or institutional culture, Course and program evaluation, Course design, Course organization, Engagement, Feedback, Instructor-student interactions, Multimedia, Social presence, Student readiness, Student support|
|Level of Analysis||Student-level, Instructor-level, Course-level, Program-level, Institutional-level|
|Specific Populations Examined|
|Specific Institutional Characteristics of Interest|
|Specific Course or Program Characteristics|
|Outcome Variables of Interest||Academic achievement or performance, including assessment scores and course grades, Degree attainment, Institutional effectiveness, Instructional effectiveness, Learning effectiveness, Persistence, Program effectiveness, Satisfaction|
|Student Sample Size|