THE LEARNING ANALYTIC CYCLE

The learning analytic cycle follows this order:

1. Collection and acquisition of the data: The collection of the data depends on the intended purpose such as marketing, advising, learning or for administrative purposes. So the data can be obtained from sources such as student information systems, learning information systems, student cards, social media sites, student success systems, etc.

2. Storage: The collected data has to be stored in a secure environment or data repository for safe keeping, privacy and confidentiality.

3. Cleaning of data: The stored data has to be cleaned since it may be structured or unstructured. This can be done using various cleaning tools.

4. Integration: The cleaned data has to be integrated into the selected analytic tool such as Tableau, R, Google Analytic, etc.

5. Analysis: The data is then analyzed and a concept is developed. The type of analysis done depends on the questions being asked or patterns sought for. For example, the course sequence taken, level of preparedness, important teaching and learning variables, social networks, pre-university profiles, or patterns of seeking help. This helps to make various predictions. This analysis can be done using tools and techniques such as SNA, NLP, etc.

6. Representation and Visualization: The output can be generated in various visualizations and dashboards.

7. Action: Once the needed information is obtained, it is now time to implement some actions such as interventions for failing students, improvement interventions, guiding students, alert/warning systems, or optimization of the event under consideration.