  We implemented active learning pedagogy in teaching and learning an
introductory course of linear algebra at the tertiary level. We adopted a
flipped classroom approach for several semesters and collected students'
perceptions regarding the pedagogy. A questionnaire is distributed and
collected at the end of the semester, and it collects students' demographics,
including gender, their year of study, their major, and their expected grade.
The students were asked to respond to ten statements on a five-item Likert
scale, five of them gauging their opinion and attitude toward flipped classroom
pedagogy, whereas the other five regarding the instructor. Using a machine
learning algorithm of the support vector machine (SVM) classification, we
investigated whether there are observable variations in the survey response
when stratified by gender difference. We seek a hyperplane that best divides
this dataset into two distinct classes based on gender differences. After
implementing the principal component analysis (PCA) for reducing the data
dimensionality, we observed that there exists a discernible hyperplane that
segregates the responses based on gender difference. This implies that SVM has
learned to capture and represent the underlying gender-related characteristics
within the dataset. Furthermore, this finding suggests that we need to tailor
our approach when implementing flipped classroom pedagogy because different
gender learns differently.
