Academic Analytics is the term for business intelligence used in an academic setting. There is an increasing distinction made between academic analytics and traditional BI because of the unique type of information that university administrators require for decision making. – Wikipedia
By leveraging technology, skills, and process improvements to bring data and analysis to bear on both strategic and operational decision-making, an academic analytics approach can empower decision-makers throughout the organization, make clear areas where resource allocations are at odds with priorities, and provide mechanisms to compare performance against professional standards or peer benchmarks.
In addition to alignment with strategic priorities, a successful academic analytics implementation can facilitate the integration of relevant and strategic information. Academic analytics can be seen as a key enabler for moving the an institution forward, as it directly impacts the decision making process at the highest levels of the University.
To obtain transparency and critical insights Academic analytics relies on the extraction of data from one or more systems, typically a course management system/learning management system and a student information system as these two system provide the most critical insight into organizational, faculty and student performance. Some of the systems in today’s marketplace for Higher Education include Oracle, Datatel, Sungard, Jenzabar, Campus Management, Blackboard, Moodle, Sakai, Desire2Learn and eCollege. The central connection of data from these systems into a single logical entity provides the most granular insight possible. This data may be assembled live or stored into a data warehouse for ongoing use.
By creating a centralized source strong correlations can be made from individual user demographics, acceptance and enrollment, academic performance, and financial data and tied to real time activities, participation and content interactions
Once appropriate connectors are deployed into each system centralized data models can create an efficient means for assembling and constructing meaningful correlations that can be utilized as a baseline for executive and stakeholder insights. Role based dashboards and visualizations can then provide individuals with the intelligence they require to make strategic and effective decisions.
By deploying a centralized reference architecture in the cloud and consolidating expertise around the various systems, data models and visualizations the Gilfus Education Group aspires to provide academic analytics capabilities to a diverse set of academic institutions.
Factors such as student recruitment and admission, teaching load, graduation rates, staff turnover, generated funds, and proposal-to-award ratios all affect a university’s performance. However, few higher education institutions are able to capture and report their many data points on all levels. A digital dashboard is a management tool for setting and measuring expectations at every organizational level, with easy-to-understand charts and reports of the status of progress throughout the year.
Such tools help users
How do you deliver dashboards that end users will adopt and use?
That was the gist of the insightful and humorous presentation titled “Dashboards to Die For” delivered by John Rome, at the Association for Institutional Research Conference (http://www.airweb.org) this year in Chicago. You’d be hard-pressed to find a bigger advocate of dashboards in higher education than Arizona State University’s John Rome.
Like many academic institutions ASU already had a load of data around campus. It simply needed a way to make it valuable to various constituents. ASU needed a flexible development tool and design principles to put the icing on the cake. John chose a dashboard tool which enabled his team to create education dashboards quickly by pulling academic analytics data from any system, including the data warehouse. John then consulted a few dashboard and design experts, including Edward Tufte, Stephen Few, and Wayne Eckerson.
Higher Ed Analytics and Dashboards in Action
Here are a few of John’s recommendations:
Developing a deploying an academic analytics initiative requires expertise across a number of critical areas.
7 Things You Should Know About Analytics”, EDUCAUSE 7 Things You Should Know series. April 2010.
Arnold, Kimberly E. “Signals: Applying Academic Analytics”, EDUCAUSE Quarterly, Volume 33, Number 1, 2010.
Campbell, John P. “Academic Analytics: A New Tool for a New Era” ELI Web Seminar, October 8, 2007. Audio and slides from the presentation.The session will be based on an article [PDF 601 KB] published in the July/August 2007 EDUCAUSE Review by John Campbell, Peter DeBlois, and Diana Oblinger.
Dawson, Shane, Liz Heathcote, and Gary Poole. “ Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience” International Journal of Educational Management Volume 24, Number 2, 2010, pages 116-128.
Goldstein, Philip J. with Richard N. Katz, “Academic Analytics: The Uses of Management Information and Technology in Higher Education”, ECAR Research Study Volume 8, 2005. The following chapters specifically discuss using learning analytics to increase student retention and monitor student academic success.
Kunnen, Eric J. and John Fritz, “Using Analytics to Intervene with Underperforming College Students”. EDUCAUSE Learning Initiative, Annual Conference, January 20, 2010. This is a video recording of a presentation.
Norris, Donald, Linda Baer, Joan Leonard, Louis Pugliese, and Paul Lefrere “Action Analytics: Measuring and Improving Performance That Matters in Higher Education”, EDUCAUSE Review, Volume 43, Number 1, January/February 2008.
Norris, Donald, Linda Baer, Joan Leonard, Louis Pugliese, and Paul Lefrere “Framing Action Analytics and Putting Them to Work”, EDUCAUSE Review, Volume 43, Number 1, January/February 2008.
Oblinger, Diana G. and John P. Campbell, “Academic Analytics” EDUCAUSE White Paper, October 2007.