Modelling and Using Response Times in Online Courses

TL;DR

This paper analyzes response times in online courses, confirming a log-normal distribution and linking longer response times to higher engagement and success, informing potential intervention strategies.

Contribution

It validates the log-normal model for response times in online courses and connects response time patterns to learner engagement and achievement.

Findings

Response times follow a log-normal distribution.

Longer response times correlate with higher course completion.

Longer response times associate with higher grades.

Abstract

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.