Analyzing Studentsxe2x80x99 Mental Models of Technical Systems Successful innovation processes require communication between different disciplines, such as mechanical and electrical engineering or software development. However, such communication is hampered by differences in individual mental models. This paper presents two studies that investigate mental models of engineering students. The studies apply SART and card sorting to analyze mental models and were conducted at the beginning of their academic education to understand influences on disciplinespecific mental models that are observed in practice. The studies indicate that mental models of engineering students are typically based on structural properties of the machines. A second study identified nine dimensions that classify mental models of students. Future work that facilitates the analysis of mental models on a larger scale is proposed. The aim of this work is to understand these differences and inform educational mechanisms that teach mental model flexibility and perspective change.

Exploring Faculty and Student Frameworks for Engineering Knowledge Using an Online Card Sorting Platform In this study we investigate how faculty and students think about engineering using a technique new to engineering education: card sorting. In card sorting participants sort stimuli (cards) into groups, in the process revealing how they categorize information. Here we examine how both engineering faculty (n23) and firstyear undergraduate students (n62) categorize engineering scenarios. We found engineering faculty sort based on crossdisciplinary engineering activities rather than engineering disciplines. This is a surprising result as our educational frameworks are based around disciplines, and yet they are not the primary way in which faculty think. Firstyear students, on the other hand, showed little consensus on how to sort the scenarios.  As a part of this paper we unveil an online card sorting platform Collection and Analysis of Research Data for Sorting (CARDS). CARDS allows researchers to create card sorting tasks, distribute them to participants for remote data collection, and analyze quantitative results.