We were provided a tool called Learning Analytics: Tool Matrix and our activity is to add to it. The tool identifies the following five areas:
- Data Cleansing/Integration
- Prior to conducting data analysis and presenting it through visualizations, data must be acquired (extracted), integrated, cleansed and stored in an appropriate data structure. Given the need for both structured and unstructured data, the ideal tools will be able to access and load data to and from data sources including RRS feeds, API calls, RDMS and unstructured data stores such as Hadoop.
- Statistical Modeling
- There are three major statistical software vendors: SAS, SPSS (IBM) and R. All three of these tools are excellent for developing analytic/predictive models that are useful in developing learning analytics models. This section focuses on R. The open source project R has numerous packages and commercial add-ons available that position it well to grow with any LA program. R is commonly used in many data/analytics MOOCs to help learners work with data. We opted for Tableau during week 1 & 2 due to ease of use and relatively short learning curve.
- Network Analysis
- Network Analysis focuses on the relationship between entities. Whether the entities are students, researchers, learning objects or ideas, network analysis attempts to understand how the entities are connected rather than understand the attributes of the entities. Measures include density, centrality, connectivity, betweenness and degrees.
- Linked Data
- If Tim Berners-Lee vision of linked data (http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html) is successful in transforming the internet into a huge database, the value of delivering content via courses and programs will diminish and universities will need to find new ways of adding value to learning. Developing tools that can facilitate access to relevant content using linked data could be one way that universities remain relevant in the higher learning sector.
- Visualization
- The presentation of the data after it has been extracted, cleansed and analyzed is critical to successfully engage students in learning and acting on the information that is presented.
My next focus will be to identify key tools within each area.
Definition of Learning Analytics
ReplyDeleteSome definitions of Learning Analytics:
Diaz (2012) defines Learning Analytics as analytics is the use of data, statistical analysis, and explanatory and predictive models to gain insights and act on complex issues.
Siemens (2011) defines learning analytics as the measurement, collection, analysis, and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs.
Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people’s learning.
—Wikipedia
Learning analytics is the use of data and models to predict student progress and performance, and the ability to act on that information.
—Next Generation Learning Challenges
My definition
I will define Learning analytics as the use of appropriate tools to measure, collect, analyse and report data about the learner, learning process and the role played by the stakeholders in the learning process for predicting learner success and informing the stakeholders on how to guide learners for success.
Insight they can provide to educators and learners.
Learning Analytics has the potential to strongly support the metacognitive dimension of learning, encouraging the learner to assume more responsibility for his or her learning.
Learning Analytics can also be a resource for instructors, providing information in real time about how students are faring.
Sorry for the delay Kwaku, I had to approve your comment. Thanks for sharing!
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