Exploring the Prototypical Definitions of Intelligent Engineers Held by Irish and Swedish Higher Education Engineering Students.

Males are generally overrepresented in higher education engineering. However, the magnitude of this variance differs between countries and engineering fields. Evidence associated with the field-specific ability beliefs hypothesis suggests that perceptions of intelligence held by actors within engineering affects the engagement of underrepresented groups. This study examined perceptions of an intelligent engineer held by undergraduate and postgraduate engineering students in Ireland and Sweden, countries selected based on their levels of female representation in engineering education. It was hypothesised that there would be a significant difference in perceptions between countries. A survey methodology was employed in which a random sample of Irish and Swedish university students completed two surveys. The first asked respondents to list characteristics of an intelligent engineer, and the second asked for ratings of importance for each unique characteristic. The results indicate that an intelligent engineer was perceived to be described by seven factors; practical problem solving, conscientiousness, drive, discipline knowledge, reasoning, negative attributes, and inquisitiveness when the data was analysed collectively, but only the five factors of practical problem solving, conscientiousness, drive, discipline knowledge and negative attributes were theoretically interpretable when the data from each country was analysed independently. A gender × country interaction effect was observed for each of these five factors. The results suggest that the factors which denote intelligence in engineering between Irish and Swedish males and females are similar, but differences exist in terms of how important these factors are in terms group level definitions. Future work should consider the self-concepts held by underrepresented groups with respect to engineering relative to the factors observed in this study.

A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight.

Whilst investigating student performance in design and arithmetic tasks, as well as during exams, electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capacity to mark events in the data, and to explain other variables relating to performance outcomes. This paper aims to address these limitations, and to support the utility of wearable EDA sensor technology in educational research settings. These aims are achieved through use of a bespoke time mapping software which identifies key events during task performance and by taking a novel approach to synthesizing EDA data from a qualitative behavioral perspective. A convergent mixed method design is presented whereby the associated implementation follows a two-phase approach. The first phase involves the collection of the required EDA and behavioral data. Phase two outlines a mixed method analysis with two approaches of synthesizing the EDA data with behavioral analyses. There is an optional third phase, which would involve the sequential collection of any additional data to support contextualizing or interpreting the EDA and behavioral data. The inclusion of this phase would turn the method into a complex sequential mixed method design. Through application of the convergent or complex sequential mixed method, valuable insight can be gained into the complexities of individual learning experiences and support clearer inferences being made on the factors relating to performance. These inferences can be used to inform task design and contribute to the improvement of the teaching and learning experience.