Academic analytics is defined as the process of evaluating and analyzing organizational data received from university systems for reporting and decision making reasons (Campbell, & Oblinger, 2007)[1].
For instance, the Bradley review acknowledges that benchmarking activities such as student engagement serve as indicators for gauging the institution's quality (Commonwealth Government of Australia, 2008).
Increased competition, accreditation, assessment and regulation are the major factors encouraging the adoption of analytics in higher education.
Subsequently, higher education leadership are forced to make critical and vital decisions based on inadequate information that could be achieved by properly utilising and analysing the available data (Norris, Leonard, & strategic Initiatives Inc., 2008).
The analytics process is made up of gathering, analysing, data manipulation and employing the results to answer critical questions such as ‘why’.
Academic analytics primarily marries complex and vast data with predictive modelling and statistical techniques to better decision making.
Current academic analytics initiatives are bent to use data to predict students experiencing difficulty (Arnold, & Pistilli, 2012, April).
Consequently, academic analytics can be rooted in data from various sources such as a CMS, and financial systems (Campbell, Finnegan, & Collins, 2006).
Act: The major goal and aim of analytics is to enable the institution to take actions based on the probabilities and predictions made.
The interventions to address problems might be in the form of a personal email, phone call or an automated contact from faculty advisors about study resources and skills, such as office hours or help sessions.
Whereas students will be keen to know academic analytics will affect their grades, faculty members will be interested in finding out how the information and data can be appropriated for other purposes (Pistilli, Arnold & Bethune, 2012).