Review: Learning Analytics

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 [1][2].
  • Use of data and algorithms to improve student engagement, retention and overall by changing student and staff behavior [3].

 

Categories and types of Learning Analytics

  • Learning analytics data is the set of information collected [4];
    • about the student,
    • the learning environment,
    • the learning interactions, and
    • the learning outcomes.

 

  • 3 Types of Learning Analytics [5];
    • 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 [4]:
    • 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 [4]
  • 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 [4].
  • 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.

 

Conclusion

  • There are many benefits of using learning analytics, as follows [4]:
    • 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.

 

Reference:
[1] Learning analytics, Wikipedia. https://en.wikipedia.org/wiki/Learning_analytics , https://tekri.athabascau.ca/analytics/
[2] Learning Analytics & Knowledge https://tekri.athabascau.ca/analytics/
[3] HESPA christine couper presentation. Paul Bailey. https://www.slideshare.net/paul.bailey/hespa-christine-couper-presentation
[4] Learning Analytics 2018 – An updated perspective https://www.iadlearning.com/learning-analytics-2018/
[5] Three https://learning.riptidesoftware.com/blog/types-of-learning-analytics/