Time-on-Task Estimation with Log-Normal Mixture Model
This addresses the need for accurate time-on-task estimation in online education, but it is incremental as it applies an existing statistical model to a new domain.
The paper tackles the problem of estimating a user's time-on-task in online learning environments by developing a method that uses only timestamps and accounts for individual differences, tested on data from HarvardX MOOCs with open-source R code.
We describe a method of estimating a user's time-on-task in an online learning environment. The method is agnostic of the details of the user's mental activity and does not rely on any data except timestamps of user's interactions, accounting for individual user differences. The method is implemented in R (the code is open-source) and has been tested in the data from a large sample of HarvardX MOOCs.