Learning Analytics (LA): In a Nutshell
Learning analytics evaluates data so that stakeholders can determine how to improve learning outcomes measured by grades, retention, or completion. LA collects and analyzes student data in order to look for correlations between student activities and learning outcomes. However, this can be a challenging process because of the various expert perspectives regarding defining learning analytics.
A Few Definitions of Learning Analytics
Currently, there are many definitions of learning analytics because there is no expert agreement due to differences in perspectives regarding processes of implementation and levels of success. Below are a few definitions to consider:
– LAK ‘11 states that LA is “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”
– EDUCAUSE’s Next Generation learning initiative states that LA is “the use of data and models to predict student progress and performance, and the ability to act on that information.”
– Elias (2011) describes LA as “an emerging field in which sophisticated analytic tools are used to improve learning and education.”
– Siemens (2010) views LA as “the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning.”
– Johnson et al. (2011) defines LA as “the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues.”
Learning Analytics: In Conversation
This video is an introduction to learning analytics pertaining to educational settings. The conversation takes the spirit of the definitions into consideration.
Model of Learning Analytics: The Spirit of the Definitions at Play
This model is designed to assist the stakeholders in the planning for learning analytics in order to measure learning effectiveness. This model is also designed to help stakeholders remain focused on the learning issues that are most important to them. This model is not the only approach or framework that works to measure learning effectiveness, but it does reflect the spirit of the definitions of learning analytics, which is useful to the stakeholders.
Questions to Consider (Weighing the Effectiveness of Learning Analytics)
– How do activities in learning environments correlate to learner success?
– How can learning analytics be used to identify and to promote effective learning?
– What insights into learning analytics data are most useful, and who should be using them?
– How do learners make decisions about how to spend their limited time and attention?
– What types of frameworks promote learning?
– Acquire knowledge of learning analytics
– Recognize the complexities involved in learning analytics
– Evaluate the effectiveness of learning analytics
– Understand learning analytics in education
We would like to thank you for taking the time to participate. Enjoy!