Using Particle Swarm Optimization Approach for Student Engagement Measurement
Keywords:
Student Engagement Measurement, Writing, Innovative Methodologies in Learning, Advanced Educational Technologies, Particle Swarm OptimizationAbstract
Measuring Student Engagement is a difficult task. Previous
research has used a cloud-based writing platform, Google Docs, which
can store a number of document revisions with timestamps.
Engagement measurement algorithm has taken the advantages of each
timestamp in a revision and calculated how much time the student
spent on a writing task. However, the parameters passed to the
algorithm were fixed and hard to determine, for example, how much
time means fully engaged or partially engaged. In this paper, we
proposed a new student engagement measurement algorithm based on
a computational intelligence approach, Particle Swarm Optimization
technique, to find the optimized parameters for the engagement
measurement algorithm. In the study, the proposed algorithm measures
the engagement of two groups of students in two different writing
activities (long-term and short term writing activities) carried out in our
cloud-based writing platform. The study results show that the
correlations between the engagement measurement and student self-report are high. In addition, it indicates that this approach is robust to
measure student engagement in both long-term and short term
activities.
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Copyright (c) 2015 Ming Liu, Yuqi Wang, Hua Liu, Shujun Wu, Chang Li
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