Pattern Discovery from Student Feedback: Identifying Factors to Improve Student Emotions in Learning
Abstract:Interest in (STEM) Science Technology Engineering
Mathematics education especially Computer Science education has
seen a drastic increase across the country. This fuels effort towards
recruiting and admitting a diverse population of students. Thus the
changing conditions in terms of the student population, diversity
and the expected teaching and learning outcomes give the platform
for use of Innovative Teaching models and technologies. It is
necessary that these methods adapted should also concentrate on
raising quality of such innovations and have positive impact on
student learning. Light-Weight Team is an Active Learning Pedagogy,
which is considered to be low-stake activity and has very little or
no direct impact on student grades. Emotion plays a major role in
student’s motivation to learning. In this work we use the student
feedback data with emotion classification using surveys at a public
research institution in the United States. We use Actionable Pattern
Discovery method for this purpose. Actionable patterns are patterns
that provide suggestions in the form of rules to help the user achieve
better outcomes. The proposed method provides meaningful insight
in terms of changes that can be incorporated in the Light-Weight team
activities, resources utilized in the course. The results suggest how
to enhance student emotions to a more positive state, in particular
focuses on the emotions ‘Trust’ and ‘Joy’.
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