Designers frequently utilize engineering equipment to create physical prototypes during the iterative concept generation and prototyping phases of design. Currently, evaluating designers' efficiency during prototype creation is a manual process that either involves observational or survey based approaches. Real-time feedback when using engineering equipment has the potential to enhance designers' efficiency or mitigate potential injuries that may result from incorrect use of equipment. Toward an automated approach to addressing these challenges, the authors of this work test the hypotheses that (i) there exists a difference in designers' comfort levels before and after they use a piece of engineering prototyping equipment and (ii) a machine learning model predicts the level of comfort a designer has while using engineering prototyping equipment with accuracies greater than random chance. It has been shown that the level of comfort that an individual has while completing a task impacts their performance. The authors investigate whether automatic tracking of designers' facial expressions during prototype creation predicts their level of comfort. A study, involving 37 participants using various engineering equipment, is used to validate the approach. The support vector machine (SVM) regression model yielded a range of R squared values from 0.82 to 0.86 for an equipment-specific model. A general model built to predict comfort level across all engineering equipment yielded an R squared value of 0.68. This work has the potential to transform the manner in which design teams utilize engineering equipment toward more efficient concept generation and prototype creation processes.

Assessment and feedback play an instrumental role in an individual’s learning process. Continued assistance is required to help students learn better and faster. This need is especially prominent in engineering laboratories where students must perform a wide range of tasks using different machines. One approach to understanding how students feel towards using certain machines is to assess their affective states while they use these machines. Affective state can be defined as the state of feeling an emotion. The authors of this work hypothesize that there is a correlation between students’ perceived affective states and task complexity. By adopting the Wood’s complexity model, the authors propose to assess how the correlations of perceived affective states of students change while they perform tasks of different complexity. In this study, each student performs a “hard” and an “easy” task on the same machine. Each student is given the same tasks using the same materials. Knowledge gained from testing this hypothesis will provide a fundamental understanding of the tasks that negatively impact students’ affective states and risk them potentially dropping out of STEM tracks, and the tasks that positively impact students’ affective states and encourage them to engage in more STEM-related activities. A case study involving 22 students using a power saw machine is conducted. Perceived affective states and completion time were collected. It was found that task complexity has an effect on subjects’ affective states. In addition, we observed some weak correlation between some of the perceived affective states and laboratory task performance. The distribution of correlation between affective states may change as the tasks change. With the knowledge of the relationship between task complexity and affective states, there is the potential to predict students’ affective states before starting a given engineering task.