Text mining analysis on students’ expectations and anxieties towards data analytics course

Abstract

The lack of understanding of course expectations and student anxieties in a Data Analytics course creates a significant gap in the practice of teaching and learning the course. This study investigated course expectations and anxieties of college students in Data Analytics courses offered over three semesters. A total of 2,893-word essays from 91 students were analyzed using text mining methods to achieve this goal. It was discovered that students understood the course but only associated its application to the field of business. Using hierarchical cluster analysis, students’ course expectations were classified into three themes: the goal of data analytics; the skills that would be acquired in the course; and the application of data analytics. Sentiment analysis disclosed that the students had apprehensions about the course because of its complex, meticulous, and mathematical nature. There were more positive than negative notions about the course. Implications of the findings and future work are offered.

Keywords

• clustering
• educational data mining
• hierarchical clustering
• sentiment analysis
• text mining