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.
