Meaning Making Of The Mathematics In Engineering: The Case Of Linear Models In Statistical Signal Processing

Meaning making of the mathematics involved in engineering problems can boost students’ learning, in general. Zooming in to a particular engineering course in signal processing, called Estimation, Detection, and Classification, given to 3rd-year students at NTNU, the potential for meaning making has been investigated using a mix of directed and summative content analysis methods for the specific content Linear models. The findings show that an attempt is made to present the linear model-based estimators in reduced complexity, i.e., without detailed, rigorous proofs that demand solid prior knowledge and concept image from the learner. The 18-page chapter is dominated by advanced mathematical symbols from different mathematical concepts with higher cognitive demanding tasks and activities, which can increase complexity in meaning making. Four types of representations (context, verbal, symbols, and graphs) and multimodal approaches (writing and mathematical symbols) are used to create the potential for meaning making to the user. Symbolic representation dominates the pages creating a higher extraneous cognitive load on the learner. Whereas examples and contexts contribute to lowering the complexity in the potential for meaning making of the mathematics in the chapter. This preliminary study does not include the instructors' and students’ active meaning making processes yet.