How It Works:

For instance, a wealth of student data, including attendance, grades, and participation, are collected and analyzed using the technology of big data to form early intervention systems. Predictive analytics algorithms then recognize the patterns associated with a learner at risk for poor performance or dropping out.  (Siemens, 2013).

Effects:

1.Proactive support: Teachers can take early action, using individualized solutions like extra materials or counseling.

2. Higher Retention Rates: Early intervention systems have been utilized by institutions such as Georgia State University to increase. (Venit, 2016).

3. Enhanced Decision-Making: Administrators can allocate resources more effectively, focusing on areas of greatest need.

Media and Resources:

1. Video Overview: How Predictive Analytics Improves Student Outcomes https://www.youtube.com/ 
2. Case Study: Venit, E. (2016). The Predictive Analytics Revolution at Georgia State University. EAB. Retrieved from link.
3. Research Article: Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. DOI.

References

Barrett, H. (2007). Researching electronic portfolios and learner engagement: The REFLECT initiative. Journal of Educational Technology & Society, 10(1), 1–8.

Guskey, T. R. (2010). Formative assessment: The contribution of assessment for learning to competency-based education. Educational Assessment, Evaluation and Accountability, 22(2), 117–125.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. 

Venit, E. (2016). The Predictive Analytics Revolution at Georgia State University. EAB. Retrieved from https://www.eab.com