Trends in Distance Education and Open Learning: Policy ... - ADEA

14. In memory analyticsIn memory analytics or database is a breakthrough in business intelligence in ERPs wherefor example managers look for instantaneous reports on their smartphones and PDAswithin sub seconds following let say sales transactions for decision making. This technologyis practiced by ERPs vendors such as SAP, and it is proven to boost the computationalpower in reporting services a million time by running the query completely at the level ofthe main memory solely without making use of the primary storage.It has been pointed that in today’s formal settings at mainly in higher educationinstitutions there is tremendous amount of administrative tasks. For instance casemanagement of students is a very fastidious task that requires information throughout thelife cycle of a student. As institutions are more conscious today to have an integratedmanagement information system for efficient administration, it could be beneficial toextend the school ERP or campus ERP to mobile devices.At present, in memory analytics applications are visible only in enterprise systems. Theapplication of this new technology in the field of m-learning is to be investigated andexplored.Analytics (referred to as “business intelligence” by some) is a tool that higher educationcan use to respond to calls for increased accountability and improved outcomes. U.S.higher education has used simple analytics for admissions, building models that use datafrom standardized tests and transcripts to predict which applicants are most likely tosucceed. Applying the same principles to enrolled students, institutions can construct datadrivenmodels that correlate patterns of behavior with student success (e.g., coursegrades, college graduation) and endeavor to identify students who are at higher risk not tocomplete a course or degree. Once at-risk students are identified, a wide range ofproactive steps can be taken to improve their odds of success.The analytics process begins with the identification and collection of data, often fromdisparate sources, within or outside the institution, including course management systems,student information systems, and other data sources. The data are analyzed and predictivemodels constructed that allow the institution to intervene with students who are likely toexperience academic difficulty.For example, a course management system (CMS) (or learning management system) mightcollect highly detailed data about student use of the resources. Correlating these data withoutcomes (e.g., student grades in the course) can reveal patterns that can predict whichstudents are more or less likely to achieve specific levels of success. Based on policies setby the institution, an analytical model can monitor student activity in the CMS and takeparticular actions when certain triggers are activated. If, for instance, the system57