Analytics in higher education

Academic analytics is defined as the process of evaluating and analyzing organizational data received from <a href="/facts/University/skaJt2Io">university</a> systems for reporting and decision making reasons (Campbell, & Oblinger, 2007)<a href="https://er.educause.edu/articles/2007/7/academic-analytics-a-new-tool-for-a-new-era">[1]</a>. Academic analytics will help student and faculty to track their career and professional paths. According to Campbell & Oblinger (2007), accrediting agencies, <a href="/facts/Government/N4bYL0VC">governments</a>, <a href="/facts/Parent/Y6tuK7qz">parents</a> and <a href="/facts/Student/zJY5rdvh">students</a> are all calling for the adoption of new modern and efficient ways of improving and monitoring student success. This has ushered the higher education system into an era characterized by increased scrutiny from the various <a href="/facts/Stakeholder_(corporate)/yN9L44ac">stakeholders</a>. For instance, the Bradley review acknowledges that <a href="/facts/Benchmarking/7HKqj6aL">benchmarking</a> activities such as student engagement serve as indicators for gauging the institution's quality (Commonwealth <a href="/facts/Government_of_Australia/UAFhWff0">Government of Australia</a>, 2008).
Increased competition, <a href="/facts/Accreditation/VlRgdWSD">accreditation</a>, assessment and regulation are the major factors encouraging the adoption of analytics in <a href="/facts/Higher_education/6GjdtJap">higher education</a>. Although institutions of higher learning gather much vital data that can significantly aid in solving problems like attrition and retention, the collected data is not being analysed adequately and hence translated into useful data (Goldstein, 2005). 
Subsequently, <a href="/facts/Higher_education_leadership/fcuw3aVI">higher education leadership</a> 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). This gives rise to strategic problems. This setback also depicts itself at the <a href="/facts/Tactical_level/gm9zr57y">tactical level</a>. Learning and teaching at institutions of higher education if often a diverse and complex experience. Each and every teacher, student or course is quite different. 
However, <a href="/facts/Learning_management_system/2uk1HBX0">LMS</a> is tasked with taking care of them all. LMS is at the centre of academic analytics. It records each and every student and staff's information and results in a click within the system. When this crucial information is added, compared and contrasted with different enterprise information systems provides the institution with a vast array of useful information that can be harvested to gain a competitive edge (Dawson & McWilliam, 2008; Goldstein, 2005; Heathcoate & Dawson, 2005).
In order to retrieve meaningful information from institution sources i.e. LMS, the information has to be correctly interpreted against a basis of educational efficiency, and this action requires analysis from people with learning and teaching skills. Therefore, a collaborative approach is required from both the people guarding the data and those who will interpret it, otherwise the data will remain to be a total waste (Baepler & Murdoch, 2010). Decision making at its most basic level is based on <a href="/facts/Presumption/YlB9IhvQ">presumption</a> or <a href="/facts/Intuition/Mshu0Vxb">intuition</a> (a person can make conclusions and decisions based on experience without having to do data analysis) (Siemens & Long, 2011). However, a lot of decisions made at institutions of higher learning are too vital to be based on <a href="/facts/Anecdote/tlX9Hlcd">anecdote</a>, presumption or intuition since significant decisions need to be backed by data and facts.

Analytics in higher education open-in-new

Analytics in higher education