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• W2274334677 abstract "In days of doing more with less and public outcries for accountability, higher education organizations are constantly assessing and evaluating how to meet demands of both internal and external stakeholders. There is a technology-based solution that is gaining momentum within higher education that appears to have possibilities in helping colleges and universities evaluate and assess student learning. I attended 2011 WCET (WICHE--Cooperative for Educational Technologies) conference held in Denver, Colo., where there was a buzz in air about analytics. Being a first-time conference attendee, it did not take long for me to recognize that WCET is one of think tanks for emerging technologies and distance learning in U.S. Analytics in higher education are commonly referred to as academic analytics or learning analytics. The Society for Learning Analytics Research (SOLAR) distinguishes between two types of analytics. SOLAR defines academic analytics as the improvement or organizational processes, work flows, resource allocation, and institutional measurement through use of learner, academic, and institutional data. Academic analytics, akin to business analytics, are concerned with improving organizational effectiveness (2011, p. 4). They define learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and environments in which it occurs. Learning analytics are largely concerned with improving learner success (2011, p. 4). The 2011 Horizon Report defines learning analytics as, the interpretation of a wide range of data produced by and gathered on behalf of in order to assess academic progress, predict future performance, and spot potential issues 0ohnson, Smith, Willis, Levine, & Haywood, 2011, p. 27). The report goes on to say that, the goal of learning analytics is to enable teachers and schools to tailor educational to each student's level of need and ability (para. 27). In following discussion, I will emphasize learning analytics, with its focus on learning. The premise behind learning analytics is that colleges and universities collect large amounts of data and information within individual college courses and academic programs as a whole, but do not always have capabilities to analyze data collected in a manner that produces useful information for data-driven decision-making. Learning analytics applications have capabilities to mine data, analyze it through statistical analysis, and produce reports that show patterns, trends, and exceptions (EDUCAUSE, 2010). Some touted benefits of analytics for colleges and universities are that it can be used to provide institutions with predictive views of upcoming challenges, both for institution and for students and resulting data-driven decisions can support optimal use of both economic and pedagogical resources while offering a structure for improved educational opportunities (EDUCAUSE, 2010, para. 4). An example discussed in EDUCAUSE report reflected on critical first year of college, when are making transition from high school to college. Learning analytics can be used to determine when and where falter during this critical time and help institutions determine where to best invest resources to ensure student success. Technology-based solutions such as learning analytics generate concerns for organizations. The downside of learning analytics is that there is some concern of legality and ethics surrounding data collected and analyzed, in areas of privacy, security, and ownership. Another concern is how conclusions are drawn from information provided. Some are concerned that analysis provided could allude to student profiling. EDUCAUSE has addressed this concern by reflecting on an institution's irresponsibility of not taking action based on useful information available. …" @default.
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• W2274334677 date "2012-03-22" @default.
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• W2274334677 title "Technology Review: The Possibilities of Learning Analytics to Improve Learner-Centered Decision-Making" @default.
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