TY - JOUR
T1 - Student-Centered Analytics for Postsecondary Education
AU - Arndt, Timothy
AU - Guercio, Angela
N1 - Arndt, T. & Guercio, A. (2016). Student-centered analytics for postsecondary education. In J. M. Spektor, D. Ifenthaler, D.G. Sampson, & P. Isaias (Eds.) Competencies in teaching, learning, and educational leadership in the digital age. Paper presented at the 11th International Conference on Cognition and Exploratory Learning in the Digital Age 2014, Porto, Portugal, 25-27 October 2014. doi: 10.1007/978-3-319-30295-9_13
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Recently, organizations have begun to realize the potential value in the huge amounts of raw, constantly fluctuating data sets that they generate and, with the help of advances in storage and processing technologies, collect. This leads to the phenomenon of big data. This data may be stored in structured format in relational database systems, but may also be stored in an unstructured format. The analysis of these data sets for the discovery of meaningful patterns which can be used to make decisions is known as analytics. Analytics has been enthusiastically adopted by many colleges and universities as a tool to improve student success (by identifying situations which call for early intervention), more effectively target student recruitment efforts, best allocate institutional resources, etc. This application of analytics in higher education is often referred to as learning analytics. While students of postsecondary institutions benefit from many of these efforts, their interests do not coincide perfectly with those of the universities and colleges. In this chapter, we suggest that postsecondary students will benefit from the use of analytics which are not controlled by the institutions of higher learning—what we call DIY (Do It Yourself) analytics—a set of tools developed specifically to meet the needs and preferences of postsecondary students.
AB - Recently, organizations have begun to realize the potential value in the huge amounts of raw, constantly fluctuating data sets that they generate and, with the help of advances in storage and processing technologies, collect. This leads to the phenomenon of big data. This data may be stored in structured format in relational database systems, but may also be stored in an unstructured format. The analysis of these data sets for the discovery of meaningful patterns which can be used to make decisions is known as analytics. Analytics has been enthusiastically adopted by many colleges and universities as a tool to improve student success (by identifying situations which call for early intervention), more effectively target student recruitment efforts, best allocate institutional resources, etc. This application of analytics in higher education is often referred to as learning analytics. While students of postsecondary institutions benefit from many of these efforts, their interests do not coincide perfectly with those of the universities and colleges. In this chapter, we suggest that postsecondary students will benefit from the use of analytics which are not controlled by the institutions of higher learning—what we call DIY (Do It Yourself) analytics—a set of tools developed specifically to meet the needs and preferences of postsecondary students.
KW - academic analytics; student-centered learning; postsecondary education; learning analytics
UR - https://engagedscholarship.csuohio.edu/bus_facpub/206
U2 - 10.1007/978-3-319-30295-9_13
DO - 10.1007/978-3-319-30295-9_13
M3 - Article
JO - Competencies in Teaching, Learning and Educational Leadership in the Digital Age
JF - Competencies in Teaching, Learning and Educational Leadership in the Digital Age
ER -