Measuring connections: Engineering students’ cognitive activities and performance on complex tasks

Abstract

This Research Full Paper presents a study using brain monitoring technology to measure and compare engineering students' performance on complex tasks that require systems thinking and connecting knowledge from different domains. The study used an electroencephalograph (EEG) and self-report data to investigate students' cognitive load and performance when completing concept mapping and listing tasks related to complex issues like food security and water availability. The study was designed to test two hypotheses: first, concept maps allow individuals to organize their thoughts using a networked or systems thinking framework, and thus will result in a more complete and holistic response than listing tasks; and second, creating a concept map is a more complex cognitive process and thus students will experience greater cognitive load during concept mapping tasks than listing tasks. Twenty-seven students at a mid-size public university participated in the study, which is an adequate size for EEG data analysis. For each participant, over forty pieces of data were recorded, including: demographic data, responses to the Revised Systems Thinking Scale, order effects, EEG performance variables, NASA-TLX scores, listing task metrics, and concept map scores. The paper presents and discusses quantitative results related to three questions: (1) do students perform better on listing or concept mapping tasks? (2) do students exert more mental effort (cognitive load) for listing or concept mapping? (3) how did performance compare across different direct and self-report measures? The ultimate goal of this research is to create learning approaches that enhance students' cognitive resources to meet and exceed the requirements of working within the sustainable design paradigm. More broadly, we expect that using neuroeducation measures to triangulate results with other qualitative and quantitative assessments could provide powerful evidence for the effectiveness of different learning interventions aimed at improving applications of engineering knowledge.