2 research outputs found
Predicting Student Performance Based on Online Study Habits: A Study of Blended Courses
Online tools provide unique access to research students' study habits and
problem-solving behavior. In MOOCs, this online data can be used to inform
instructors and to provide automatic guidance to students. However, these
techniques may not apply in blended courses with face to face and online
components. We report on a study of integrated user-system interaction logs
from 3 computer science courses using four online systems: LMS, forum, version
control, and homework system. Our results show that students rarely work across
platforms in a single session, and that final class performance can be
predicted from students' system use.Comment: Published in the International Conference on Educational Data Mining
(EDM 2018
What will you do next? A sequence analysis on the student transitions between online platforms in blended courses
Students' interactions with online tools can provide us with insights into
their study and work habits. Prior research has shown that these habits, even
as simple as the number of actions or the time spent on online platforms can
distinguish between the higher performing students and low-performers. These
habits are also often used to predict students' performance in classes. One key
feature of these actions that is often overlooked is how and when the students
transition between different online platforms. In this work, we study sequences
of student transitions between online tools in blended courses and identify
which habits make the most difference between the higher and lower performing
groups. While our results showed that most of the time students focus on a
single tool, we were able to find patterns in their transitions to
differentiate high and low performing groups. These findings can help
instructors to provide procedural guidance to the students, as well as to
identify harmful habits and make timely interventions