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Electroencephalogram (EEG) alpha power (8–13 Hz) is a characteristic of various creative task conditions and is involved in creative ideation. Alpha power varies as a function of creativity-related task demands. This study investigated the event-related potentials (ERPs), alpha power activation, and potential machine learning (ML) to classify the neural responses of engineering students involved with creativity task. All participants performed a modified alternate uses task (AUT), in which participants categorized functions (or uses) for everyday objects as either creative, nonsense, or common. At first, this study investigated the fundamental ERPs over central and parietooccipital temporal areas. The bio-responses to understand creativity in engineering students demonstrates that nonsensical and creative stimuli elicit larger N400 amplitudes (−1.107 mV and −0.755 mV, respectively) than common uses (0.0859 mV) on the 300–500 ms window. N400 effect was observed on 300–500 ms window from the grand average waveforms of each electrode of interest. ANOVA analysis identified a significant main effect: decreased alpha power during creative ideation, especially over (O1/2, P7/8) parietooccipital temporal area. Machine learning is used to classify the specific temporal area data’s neural responses (creative, nonsense, and common). A k-nearest neighbors (kNN) classifier was used, and results were evaluated in terms of accuracy, precision, recall, and F1- score using the collected datasets from the participants. With an overall 99.92% accuracy and area under the curve at 0.9995, the kNN classifier successfully classified the participants’ neural responses. These results have great potential for broader adaptation of machine learning techniques in creativity research.

 

Unmanned Aerial Systems (UAS) have become popular in the past two decades because of their key role in numerous military applications, which range from aerial support of troops involved on the battlefield to surveillance and border patrol. The versatility of UAS platforms make it extremely appealing for several civilian applications, and considerable cost reduction for critical components has made this technology a powerful resource for private operators. In this paper we present a collaborative effort with the objective of establishing a competitive UAS educational program at the Rose State College (RSC, a two-year institute) and creating a pipeline to develop a UAS workforce in Oklahoma. The approach modified freshman and sophomore aerospace and mechanical engineering courses at RSC to incorporate UAS design into applicable courses. Experiential learning opportunities involving UAS are included through class projects. Modifying the “Introduction to Aerospace Engineering” course at the University of Oklahoma (OU, 4-year institute) and applying the theoretical concepts learned in class to real examples involving UAS. UAS platforms are not considered as mere special cases, but will be given proper attention both in class and through dedicated homework assignments and projects. We also investigate pipeline of students from RSC to OU. Many of the RSC students attending selected undergraduate classes at OU decide to continue their education by pursuing a bachelor’s degree in engineering. This positive trend is encouraged by providing UAS students at RSC to perform undergraduate (UG) research at OU. This paper presents different activities to establish curriculum and collaboration between the two institutions to support Oklahoma’s workforce. 

Classical mechanics courses are taught to most engineering disciplinary undergraduate students. Due to the recent advancements of multiscale analysis and practice, necessary reforms need to be investigated and explored for classical mechanics courses to address the materials’ mechanics behaviors across multiple length scales. This enhanced understanding is needed for engineering students to consider materials more broadly. This paper presents a recent effort for the development of a multiscale materials and mechanics experimentation (M3E) module that can be potentially implemented in undergraduate mechanics courses, including Statics, Dynamics, Strength of Materials, and Design of Mechanical (Machine) Components. The developed education module introduces the concepts of multiscale materials behavior and microstructures in the form of micro and macro-scales. At the micro-scale, both 3D printed aluminum and cold-rolled aluminum samples were characterized using scanning electron microscope. Microstructures, including grains, grain boundaries, dislocation, precipitates, and micro-voids, were demonstrated to students. At the macro-scale, experiments following ASTM standards were conducted and full strain fields carried by all the samples were analyzed using digital image correlation method. The experimental data were organized and presented to the students in the developed M3E module. The implementation of the developed module in undergraduate mechanics classes allows students to not only visualize materials behavior under various load conditions, but also understand the reasons behind classical mechanics properties. To assess the effectiveness of the developed M3E education module, an evaluation question was developed. Students are required to classify key mechanics, materials, and processing concepts at both micro and macroscales. More than 40 fundamental concepts and keywords are included in the tests. The study outcomes and effectiveness of the M3E education module will be reported in this paper. 

Engineering programs, in general, do not explicitly address the need to enhance divergent thinking. To a certain extent this is due to a lack in knowledge on the cognitive and neural mechanisms underlying divergent thinking, and creative ideation more generally. We hypothesize that we can help enhance our students’ divergent thinking and creative processing outcomes by investigating the impacts of carefully selected methods and tools enabled by developments in the robust analysis of engineering ideation performance, and neurocognitive responses to creativity. In this paper, we present an experiment using the Event-Related brain Potentials (ERP) technique and creative language use (funded by Core R&D Programs). More specifically, we collected ERP responses to literal, nonsense, and novel metaphorical sentences that were either referring to engineering knowledge or general knowledge, testing engineering and non-engineering students. Following Rutter et al. [1], sentences differed in verb only and had been classified in prior sentence norming studies as highly unusual and highly appropriate (novel metaphors), low unusual and highly appropriate (literal sentences), and highly unusual and low appropriate (nonsense sentences). Participants read sentences while their EEG was recorded, and after reading the sentence made judgments about its unusualness and appropriateness. The findings indicate that prior knowledge modulates novel metaphor processing at the stage of lexico–semantic access, indexed by the amplitude of N400 component. Specifically, N400 amplitudes to novel metaphorical sentences are significantly reduced and pattern with literal sentences in engineers; in nonengineers, by contrast, we observed increased N400 amplitudes to novel metaphorical sentences that pattern with anomalous sentences. This mirror effect on the N400 corroborates recent findings demonstrating a strong impact of prior experience and expertise on meaning ambiguity resolution, which may in turn have implications for creative cognition. 

Investigations of creativity have been an intriguing topic for a long time, but assessing creativity is extremely complex. Creativity is a cornerstone of engineering disciplines, so understanding creativity and how to enhance creative abilities through engineering education has received substantial attention. Fields outside of engineering are no stranger to neuro-investigations of creativity and although some neuro-response studies have been conducted to understand creativity in engineering, these studies need to map the engineering design and concept generation processes better. Using neuroimaging techniques alongside engineering design and concept generation processes is necessary for understanding how to improve creative idea generation and creativity studies in engineering. In this paper, a survey is provided of the literature for the different neurological approaches that have been used to study the engineering design process and creative processes. Also presented are proposed strategies to apply these neurological approaches to engineering design to understand the creative process in greater detail. Furthermore, results from a pilot study investigating neuro-responses of engineers are presented. 

Assessing creativity is not an easy task, but that has not stopped researchers from exploring it. Because creativity is essential to engineering disciplines, knowing how to enhance creative abilities through engineering education has been a topic of interest. In this paper, the event related potential (ERP) technique is used to study the neural responses of engineers via a modified alternative uses task (AUT). Though only a pilot study testing two participants, the preliminary results of this study indicate general neuro-responsiveness to novel or unusual stimuli. These findings also suggest that a scaled-up study along these lines would enable better understanding and modeling of neuroresponses of engineers and creative thinking, as well as contribute to the growing field of ERP research in the field of engineering. 

Creativity is the driver of innovation in engineering. Hence, assessing the effectiveness of a curriculum, a method, or a technique in enhancing the creativity of engineering students is no doubt important. In this paper, the process involved in quantifying creativity when measured through the alternative uses task (AUT) is explained in detail. The AUT is a commonly used test for divergent thinking ability, which is a main aspect of creativity. Although it is commonly used, the processes used to score this task are far from standardized and tend to differ across studies. In this paper, we introduce these problems and move towards a standardized process by providing a detailed account of our quantification process. This quantification process takes into consideration four commonly used dimensions of creativity: originality, flexibility, fluency, and elaboration. AUT data from a preliminary case study were used to illustrate how the AUT and the quantification process can be used. The study was performed to understand the effect of the stereotype threat on the creativity of 25 female engineering students. The results indicate that after the stereotype threat intervention, participants generated more diverse and original ideas. 

This paper presents the development and preliminary implementation of a multi-scale material and mechanics education module to improve undergraduate solid mechanics education. We experimentally characterize 3D printed and conventional wrought aluminum samples and collect structural images and perform testing at the micro- and macro- scales. At the micro-scale, we focus on the visualization of material’s grain structures. At the macro-scale, standard material characterization following ASTM standards is conducted to obtain the macroscopic behavior. Digital image correlation technology is employed to obtain the two-dimensional strain field during the macro-scale testing. An evaluation of student learning of solid mechanics and materials behavior concepts is carried out to establish as baseline before further interventions are introduced. The established multi-scale mechanics and materials testing dataset will be also used in a broad range of undergraduate courses, such as Solid Mechanics, Design of Mechanical Components, and Manufacturing Processes, to inform curricular improvement. The successful implementation of this multi-scale approach for education is likely to enhance students’ understanding of abstract solid mechanics theories and establish links between mechanics and materials concepts. More broadly, this approach will assist advanced solid mechanics education in undergraduate engineering education throughout the country. 

This paper presents the development and preliminary implementation of a multi-scale material and mechanics education module to improve undergraduate solid mechanics education. We experimentally characterize 3D printed and conventional wrought aluminum samples and collect structural images and perform testing at the micro- and macro- scales. At the micro-scale, we focus on the visualization of material’s grain structures. At the macro-scale, standard material characterization following ASTM standards is conducted to obtain the macroscopic behavior. Digital image correlation technology is employed to obtain the two-dimensional strain field during the macro-scale testing. An evaluation of student learning of solid mechanics and materials behavior concepts is carried out to establish as baseline before further interventions are introduced. The established multi-scale mechanics and materials testing dataset will be also used in a broad range of undergraduate courses, such as Solid Mechanics, Design of Mechanical Components, and Manufacturing Processes, to inform curricular improvement. The successful implementation of this multi-scale approach for education is likely to enhance students’ understanding of abstract solid mechanics theories and establish links between mechanics and materials concepts. More broadly, this approach will assist advanced solid mechanics education in undergraduate engineering education throughout the country.