An Initial Exploration of Engineering Students’ Emotive Responses to Spatial and Engineering Statics Problems

Abstract

Her multiple roles as an engineer, engineering educator, engineering educational researcher, and professional development mentor for underrepresented populations has aided her in the design and integration of educational and physiological technologies to research 'best practices' for student professional development and training. In addition, she is developing methodologies around hidden curriculum, academic emotions and physiology, and engineering makerspaces.

A Multimodal Exploration of Engineering Students' Emotions and Electrodermal Activity in Design Activities

“…Similarly, Villanueva and colleagues have explored EDA and emotions during engineering exams in a statics course (Villanueva, ; Villanueva, Call, & Goodridge, ; Vallanueva, Valladares, & Goodridge, ). Approximately 20 sophomore engineering students were asked to complete an emotions self‐report while being tested on problems based on cutting planes and spatial ability (Call, Goodridge, Villanueva, Wan, & Jordan, ; Ruesch et al, ; Villanueva et al, ; Villanueva, Call, & Goodridge, ) as they wore EDA sensors and encephalography (EEG) monitors in a laboratory setting.…”

“…Similarly, Villanueva and colleagues have explored EDA and emotions during engineering exams in a statics course (Villanueva, ; Villanueva, Call, & Goodridge, ; Vallanueva, Valladares, & Goodridge, ). Approximately 20 sophomore engineering students were asked to complete an emotions self‐report while being tested on problems based on cutting planes and spatial ability (Call, Goodridge, Villanueva, Wan, & Jordan, ; Ruesch et al, ; Villanueva et al, ; Villanueva, Call, & Goodridge, ) as they wore EDA sensors and encephalography (EEG) monitors in a laboratory setting. The preliminary findings suggested that the order of test problems may influence the activation of students' prefrontal cortex and EDA arousal (Call et al, ; Ruesch et al, ; Villanueva et al, ), a response which paralleled changes in negative emotions (e.g., pride; unpublished findings, Villanueva et al, ).…”

Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review

“…For example, Collins et al [ 58 ] used machine learning (support vector machine) with a classification accuracy of 95.67% to detect movement artifacts. Finally, an extra signal measured by an accelerometer built-in in some EDA devices was also used to detect movement artifacts [ 36 , 59 , 71 ].…”

“…Six studies normalized their EDA signal to account for individual differences in the EDA signal. Two of these studies combined normalization with cleaning [ 52 , 71 ], and one study combined normalization, filtering, and cleaning [ 64 ]. Both studies of Villanueva et al [ 70 , 71 ] used normalization through range correction.…”

“…Two of these studies combined normalization with cleaning [ 52 , 71 ], and one study combined normalization, filtering, and cleaning [ 64 ]. Both studies of Villanueva et al [ 70 , 71 ] used normalization through range correction. This technique takes into account the minimum and maximum amplitude levels.…”