Competency 2.1: Describe the learning analytics data cycle.

Learning Analytics Cycle

In order for us to get all this, we will need a good tool to follow up the procedures or cycle and also to process all the data we want to analyze. It’s also important to have an specific target and data collection source accessible to gather the information.

DATA SOURCES

• Student information systems
• Learning management systems
• Use of university services
• Student cards
• Social media
• Student success system (CRM)
• Any interaction between learner and institution

DATA REPOSITORIES

QUESTIONS PATTERNS SOUGHT

• Course sequencing
• Social Networks
• Preparedness
• Important teaching and learning variables
• Pre-university profiles
• Help-seeking

Steps of the Analytics Cycle

Data Sources

• Datamarts
• LSM
• SIS
• Sensors
• Manual entry

Collecting Data 

In our particular case, or/and depending on the purpose this data may be use to:

• Marketing
• Advising
• Faculty Impact
• Learning
• Administration
• Institutional Research

Storage 

In many cases data goes to an application which will help to work with, meaning this will keep all that information and it must be secure. 

Why should secure data storage? Data contains value and personal information of all those individuals participating in the process, this data may come directly from any of the tools we can use to get it such as manual entry or social media; and still it is our responsibility to keep and handle this information the most secure way.

Cleaning and Integration 

In some cases data will have some variables you may not use in the analysis also this step will help you to structure and unstructured. Once your data is clean the integration will be easier, also the multiple datasets and/or formats are going to be clear.

Analysis

It’s the detailed examination of the elements or structure of the data, here is where we are going to set the different questions and queries to get the information desire, depending on the analysis we are developing in this particular learning analytic data we might get information such:

• Student perspective
• Desired student perspective
• Tracking students 
• Reflect on own learning
• Reflect on the learning of others
• Predict outcome
• Personalization for students
• Content adaption
• Personalized track
• Marketing for an specific group of students
• Etc.

Representation & Visualization

Information should not be present in .cvs or regular excel sheet format, in order to manipulate and interpreter this data and its results, it should be present in a dynamic or interactive way where you can modify the different variables to obtain different results according to the question you are making.

Action 

Setting dynamic to the data presentation will allow administrators to not just to get different sceneries of what is and what could happens but it also will help you to take decisions based on the risk and predictions you may get according to the answers you get from the trends, needs, factors etc you may see in your analytic study.

Closing the cycle properly will give us all this tools to work to improve, personalize, to study in a clear and better way. The benefits are for both sides, institution and the students.