Modelling and Using Response Times in Online Courses
This research addresses improving learner outcomes in online courses like MOOCs by identifying response time patterns for targeted interventions.
The study validated that response times in online courses follow a log-normal distribution and found that users who take longer to answer questions are more likely to complete the course, show higher engagement, and achieve better grades.
Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes some time to provide a second answer. Here we study the distribution of such "response times." We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses. Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement, and achieve a higher grade. This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage "fast" users to slow down.