LAUSR

Contribution: Knowledge of students’ Habits of Mind in a signal processing course, and a method for education research. The method identifies factors that may influence students’ performance, but are not evident when analyzing agglomerated data; it is an alternative to the traditional case study method as it derives the cases after applying a clustering approach. Background: Habits of Mind refer to mathematical, logical, and attitudinal modes of thinking required for students of science, mathematics, technology, and engineering to become effective problem solvers, capable of transferring such modes to new contexts. These are particularly relevant in a signal processing course in which students must learn to address engineering problems using tools and techniques previously acquired in an abstract context (mathematics). Research Questions: 1) What are the different Habits of Mind patterns exhibited by the students? 2) Are some of these patterns associated with differences in course grades? Methodology: A qualitative method is combined with random signal modeling and machine learning. Students’ work is first annotated manually based on a custom-built rubric of Habits of Mind. Data is modeled and clustered to obtain statistically significant patterns of Habits of Mind corresponding to divisions of the students into groups. Findings: The data obtained suggests that the student group is inhomogeneous in terms of their Habits of Mind, and this inhomogeneity is associated with grade differences. In particular, the course grade is found to be dependent on inhomogeneity based on at least two Habits of Mind: 1) computation and estimation and 2) values and attitudes.