Competency 7.4: Describe how models might be used in Learning Analytics research, specifically for the problem of assessing some reasons for attrition along the way in MOOCs.

One particular model described by Dr. Carolyn talks about how certain properties of discussion correlate to dropout in MOOC. It explores how analyses of sentiment predict attrition over time (Sentiment however was found to be the least consistent and weakest indicator for dropouts). Refer the article below:

#### Survival Modeling:

Survival model is a regression model that captures the changes in probability of survival over time. It captures the probability at each time point and it is measured in terms of hazard ratio which indicates how much more or less likely a student is to drop out. If Hazard ratio>1, the student is significantly more likely to drop out in the next time point.

Sentiment analysis in MOOC forums looked at Expressed sentiment and Exposure to sentiment. The four independent variables Individual Positivity, Individual Negativity, Thread Positivity and Thread Negativity were used to calculate the dependent variable Dropout. The effects were relatively weak and inconsistent across courses.

Some factors that may contribute to student attrition like student’s prior motivation, skill set/ knowledge in the area, previous experience in learning MOOCs are difficult to capture. We can link different analysis methods like social network analysis, text mining, predictive modeling and survey data analysis to try to get the complete picture of an individual student for more consistent results.