Definitions of Learning Analytics
- Learning analytics 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 .
- Use of data and algorithms to improve student engagement, retention and overall by changing student and staff behavior .
Categories and types of Learning Analytics
- Learning analytics data is the set of information collected ;
- about the student,
- the learning environment,
- the learning interactions, and
- the learning outcomes.
- 3 Types of Learning Analytics ;
- Descriptive Analytics – Understanding the Past
- Predictive Analytics – Predict What Might Happen
- Prescriptive Analytics – Prescribe Solutions for Various Outcomes
- As a result of data analysis, we can obtain two types of learning analytics :
- Descriptive learning analytics: These types of analytics are reactive. They allow understanding of the past and, based on this understanding, influence the future.
- Predictive learning analytics: These types of analytics are proactive. They influence the present and, therefore, improve ongoing learning processes.
Common Characteristic of Learning Analytics
- There are three crucial elements involved in this definition 
- Data: Data is the primary analytics asset. Data is the raw material that gets transformed into analytical insights.
- Analysis: Analysis is the process of adding intelligence to data using algorithms.
- Action: Action is the most important aspect of the definition. Taking action is the ultimate goal of any learning analytics process. The results of follow-up actions will determine the success or failure of our analytical efforts.
Learning Analytic Consideration
- Collect as much useful data as possible and as less sensitive data as required .
- At the end of the day, it is about improving students’ learning experiences and making sure funds are used in the areas that best contribute to student development.
- There are many benefits of using learning analytics, as follows :
- Increase retention and performance: Learning analytics may be used to reduce dropout rates and increase students’ performance. Having the right insights allow for performing proactive tutoring and intervention.
- Improve content and course quality: Learning analytics may be used to discover content consumption patterns, understand content quality issues, and provide personalized learning experiences (adaptive learning).
- Proactively drive success: Learning analytics may be used to identify and promote success factors as well as to understand students’ pathways leading to graduation (curriculum design).
- Allocate costs efficiently: Learning analytics may help in discovering which resources work and which don’t. Selective investment strategies may well be designed based on our analytics.
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 Learning Analytics & Knowledge https://tekri.athabascau.ca/analytics/
 HESPA christine couper presentation. Paul Bailey. https://www.slideshare.net/paul.bailey/hespa-christine-couper-presentation
 Learning Analytics 2018 – An updated perspective https://www.iadlearning.com/learning-analytics-2018/
 Three https://learning.riptidesoftware.com/blog/types-of-learning-analytics/