In this blogpost I identify the educational technology area / topic I am most interested in exploring and why. I will share what I know about this topic and how this technology can enhance your current and future leadership position.

Educational Technology area / topic: I’m interested in exploring Learning Analytics because I may be asked to write grants to fund the implementation of this technology. As a result of this preliminary review of Learning Analytics, I’ve discovered Academic Analytics which is also of interest. Both are briefly discussed and defined in this blogpost.

Learning Analytics

Learning Analytics is one type of big data. Driven by the explosion of available data from internet, computers and mobile device and applications (apps) users leaves a digital footprint (Long & Siemens, 2011). The quantity, speed, scale and types of this digital data calls for methods beyond linear analysis used up to this point. The McKinsey Global Institute (a business research group) defines big data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze” (Manyika, 2011).

In higher education, digital student records capture student test scores and final grades. Student activity streams are captured by student cards, sensors, and mobile devices. In addition, the development of learning management systems manage and monitor student progress in online courses. (Long & Siemens, 2011). Learning analytics is ‘the measurement, collection, analysis and reporting of data about learnings and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs” (Long & Siemens, 2011).

Academic Analytics

A related concept, Academic analytics is the application of business intelligence concepts in the educational setting at the institutional, regional, national and international levels (Long & Siemens, 2011). Academic analytics “…could be thought of as the practice of mining institutional data to produce ‘actionable intelligence’’ (Campbell, DeBlois, & Oblinger, 2007).

 Blog#3 Long & Siemens Table 1

Why is it important?

Analytics or the use of learner produced data is potentially transformative – improving teaching learning organizational efficiency and decision making and thereby support systemic change (Long & Siemens, 2011). Analytics tools can help students and instructors “better understand the learning process and take action to improve course outcomes” (Educause, 2011).


References

Campbell, J. P., & Oblinger, D. G. (2007). “Academic Analytics: A new tool for a new era” [HTML Document]. Retrieved from: http://net.educause.edu/ir/library/pdf/PUB6101.pdf

Educause (2011). 7 things you should know about … First Generation Learning Analytics [HTML Document] Retrieved from: http://educause.edu/eli

Long,  P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education [HTML Document]. Retrieved from http://net.educause.edu/ir/library/pdf/ERM1151.pdf

Manyika, J. (2011). Big Data: The next frontier for innovation, competition and productivity, McKinsey Global Institute [HTML Document]. Retrieved from: http://www.mckinsey.com/mgi/

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