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1. An active learning approach to teach distributed forces using augmented reality with guided inquiry

   https://doi.org/10.1002/cae.22703  

   Giancaspro, James W. ; Arboleda, Diana ; Kim, Nam J. ; Chin, Seulki J. ; Britton, Jennifer C. ; Secada, Walter G. ( December 2023 , Computer Applications in Engineering Education)

   Mastering the concept of distributed forces is vital for students who are pursuing a major involving engineering mechanics. Misconceptions related to distributed forces that are typically acquired in introductory Physics courses should be corrected to increase student success in subsequent mechanics coursework. The goal of this study was to develop and assess a guided instructional activity using augmented reality (AR) technology to improve undergraduate engineering students' understanding of distributed forces. The AR app was accompanied by a complementary activity to guide and challenge students to model objects as beams with progressively increasing difficulty. The AR tool allowed students to (a) model a tabletop as a beam with multiple distributed forces, (b) visualize the free body diagram, and (c) compute the external support reactions. To assess the effectiveness of the activity, 43 students were allocated to control and treatment groups using an experimental nonequivalent groups preactivity/postactivity test design. Of the 43 students, 35 participated in their respective activity. Students in the control group collaborated on traditional problem‐solving, while those in the treatment group engaged in a guided activity using AR. Students' knowledge of distributed forces was measured using their scores on a 10‐item test instrument. Analysis of covariance was utilized to analyze postactivity test scores by controlling for the preactivity test scores. The treatment group demonstrated a significantly greater improvement in postactivity test scores than that of the control group. The measured effect size was 0.13, indicating that 13% of the total variance in the postactivity test scores can be attributed to the activity. Though the effect size was small, the results suggest that a guided AR activity can be more effective in improving student learning outcomes than traditional problem‐solving.

    

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2. Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events

   Buckwalter, Grace Chhin ( December 2021 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   The Temple University Hospital EEG Corpus (TUEG) [1] is the largest publicly available EEG corpus of its type and currently has over 5,000 subscribers (we currently average 35 new subscribers a week). Several valuable subsets of this corpus have been developed including the Temple University Hospital EEG Seizure Corpus (TUSZ) [2] and the Temple University Hospital EEG Artifact Corpus (TUAR) [3]. TUSZ contains manually annotated seizure events and has been widely used to develop seizure detection and prediction technology [4]. TUAR contains manually annotated artifacts and has been used to improve machine learning performance on seizure detection tasks [5]. In this poster, we will discuss recent improvements made to both corpora that are creating opportunities to improve machine learning performance. Two major concerns that were raised when v1.5.2 of TUSZ was released for the Neureka 2020 Epilepsy Challenge were: (1) the subjects contained in the training, development (validation) and blind evaluation sets were not mutually exclusive, and (2) high frequency seizures were not accurately annotated in all files. Regarding (1), there were 50 subjects in dev, 50 subjects in eval, and 592 subjects in train. There was one subject common to dev and eval, five subjects common to dev and train, and 13 subjects common between eval and train. Though this does not substantially influence performance for the current generation of technology, it could be a problem down the line as technology improves. Therefore, we have rebuilt the partitions of the data so that this overlap was removed. This required augmenting the evaluation and development data sets with new subjects that had not been previously annotated so that the size of these subsets remained approximately the same. Since these annotations were done by a new group of annotators, special care was taken to make sure the new annotators followed the same practices as the previous generations of annotators. Part of our quality control process was to have the new annotators review all previous annotations. This rigorous training coupled with a strict quality control process where annotators review a significant amount of each other’s work ensured that there is high interrater agreement between the two groups (kappa statistic greater than 0.8) [6]. In the process of reviewing this data, we also decided to split long files into a series of smaller segments to facilitate processing of the data. Some subscribers found it difficult to process long files using Python code, which tends to be very memory intensive. We also found it inefficient to manipulate these long files in our annotation tool. In this release, the maximum duration of any single file is limited to 60 mins. This increased the number of edf files in the dev set from 1012 to 1832. Regarding (2), as part of discussions of several issues raised by a few subscribers, we discovered some files only had low frequency epileptiform events annotated (defined as events that ranged in frequency from 2.5 Hz to 3 Hz), while others had events annotated that contained significant frequency content above 3 Hz. Though there were not many files that had this type of activity, it was enough of a concern to necessitate reviewing the entire corpus. An example of an epileptiform seizure event with frequency content higher than 3 Hz is shown in Figure 1. Annotating these additional events slightly increased the number of seizure events. In v1.5.2, there were 673 seizures, while in v1.5.3 there are 1239 events. One of the fertile areas for technology improvements is artifact reduction. Artifacts and slowing constitute the two major error modalities in seizure detection [3]. This was a major reason we developed TUAR. It can be used to evaluate artifact detection and suppression technology as well as multimodal background models that explicitly model artifacts. An issue with TUAR was the practicality of the annotation tags used when there are multiple simultaneous events. An example of such an event is shown in Figure 2. In this section of the file, there is an overlap of eye movement, electrode artifact, and muscle artifact events. We previously annotated such events using a convention that included annotating background along with any artifact that is present. The artifacts present would either be annotated with a single tag (e.g., MUSC) or a coupled artifact tag (e.g., MUSC+ELEC). When multiple channels have background, the tags become crowded and difficult to identify. This is one reason we now support a hierarchical annotation format using XML – annotations can be arbitrarily complex and support overlaps in time. Our annotators also reviewed specific eye movement artifacts (e.g., eye flutter, eyeblinks). Eye movements are often mistaken as seizures due to their similar morphology [7][8]. We have improved our understanding of ocular events and it has allowed us to annotate artifacts in the corpus more carefully. In this poster, we will present statistics on the newest releases of these corpora and discuss the impact these improvements have had on machine learning research. We will compare TUSZ v1.5.3 and TUAR v2.0.0 with previous versions of these corpora. We will release v1.5.3 of TUSZ and v2.0.0 of TUAR in Fall 2021 prior to the symposium. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation’s Industrial Innovation and Partnerships (IIP) Research Experience for Undergraduates award number 1827565. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] I. Obeid and J. Picone, “The Temple University Hospital EEG Data Corpus,” in Augmentation of Brain Function: Facts, Fiction and Controversy. Volume I: Brain-Machine Interfaces, 1st ed., vol. 10, M. A. Lebedev, Ed. Lausanne, Switzerland: Frontiers Media S.A., 2016, pp. 394 398. https://doi.org/10.3389/fnins.2016.00196. [2] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Frontiers in Neuroinformatics, vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [3] A. Hamid et, al., “The Temple University Artifact Corpus: An Annotated Corpus of EEG Artifacts.” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1-3. https://ieeexplore.ieee.org/document/9353647. [4] Y. Roy, R. Iskander, and J. Picone, “The NeurekaTM 2020 Epilepsy Challenge,” NeuroTechX, 2020. [Online]. Available: https://neureka-challenge.com/. [Accessed: 01-Dec-2021]. [5] S. Rahman, A. Hamid, D. Ochal, I. Obeid, and J. Picone, “Improving the Quality of the TUSZ Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1–5. https://ieeexplore.ieee.org/document/9353635. [6] V. Shah, E. von Weltin, T. Ahsan, I. Obeid, and J. Picone, “On the Use of Non-Experts for Generation of High-Quality Annotations of Seizure Events,” Available: https://www.isip.picone press.com/publications/unpublished/journals/2019/elsevier_cn/ira. [Accessed: 01-Dec-2021]. [7] D. Ochal, S. Rahman, S. Ferrell, T. Elseify, I. Obeid, and J. Picone, “The Temple University Hospital EEG Corpus: Annotation Guidelines,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/tuh_eeg/annotations/. [8] D. Strayhorn, “The Atlas of Adult Electroencephalography,” EEG Atlas Online, 2014. [Online]. Availabl 

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3. Develop and evaluate an augmented reality posture training tool to promote work safety

   https://doi.org/10.1177/1071181320641496  

   Chen, Ken ; Perera, Gimantha ; Li, Li ; Xu, Xu ; Chen, Karen B ( December 2020 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Conventional work posture training tools included pamphlets, one-time training orientation, and/or videos. These tools did not always yield satisfactory training outcomes, and the incident rate of work-related musculoskeletal disorders did not substantially lower. In this research, modern augmented reality (AR) technology was leveraged to deliver interactive, holistic, whole-body visual information to convey safe work postures. The developmental procedure followed DMAIC by first defining specifications of training content, which led to the development of the training tool, including 3D reconstruction of a virtual instructor and building of user interface based on user-centered framework. This AR training tool was measured and analyzed through usability evaluation, and quantitative and qualitative data were obtained for cross-validation and usability issue source identification. Findings revealed the utility of 3D reconstruction of a virtual instructor and practicality of adopting conventional usability evaluation method for AR user interface usability evaluation. Feedback from the usability evaluation via questionnaire, think aloud, and post-task open-ended responses are employed to iteratively design the next version of the AR posture training tool. 

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4. Setting the stage for co-creation: Using workshops to scaffold interdisciplinary research, collaboration, and community building

   Kershaw, T.C. ; Tripathy, S.T. ; Liu, H. ; Chandra, K. ( June 2023 , Proceedings of the 2023 ASEE Annual Conference & Exposition)

   Co-creation in academe can take multiple forms. In this research, the co-creation focus is on collaboration between faculty and graduate students to develop educational modules. This activity is designed to improve graduate education and prepare students for conducting graduate research. In previous work presented at ASEE 2022, we discussed benefits and challenges of participating in the co-creation process. This current paper focuses on how we took lessons from our first year and transformed them into a structure to better support interdisciplinary research, collaboration, and community building. We will discuss how we supported the process of co-creation by developing a series of workshops to scaffold student learning. Scaffolds are instructional methods and interventions that are designed to foster skill development by allowing for interactions between what students already know and what they have yet to learn. These workshops were designed using the tenets of the gold standard project-based learning (PjBL). The PjBL framework is itself a scaffold that is designed to build research competencies. Specifically, to introduce a challenging problem or question, we created multiple technical overviews of the cyber-physical system theme of interest that would constitute the eventual educational modules. We scaffolded sustained inquiry by developing a workshop using techniques from the Right Question Institute, and also through a workshop about crafting your message for different audiences. To support the PjBL idea of authenticity, we developed a workshop about core values to help students connect personally to their project topics. To further support collaboration and community building, we developed a workshop to introduce ideas of interdisciplinary collaboration, including developing community agreements and recognizing and responding to microaggressions. Periodic reinforcements of these topics were incorporated as students progressed in their co-creation project. We assessed how students applied these topics through student reflections. Scaffolding students’ learning helped to address co-creation challenges that were expressed by our pilot group, including not understanding the goals of the project and not feeling connected to the research. Observational data of the current groups suggests that students have better understanding of the co-creation process and are collaborating more effectively than our pilot group students, and focus group data confirmed these observations. We also collected feedback from students about the workshops to evaluate what is effective about them and what can be improved. Students felt skills taught in the workshops such as how to prioritize research questions, construct messages for specific audiences, and perform literature searches and reviews, were all effective and useful as they worked on their projects. For improvement, they suggested clearer objectives and more workshops that focus on technical aspects of the project work would be helpful. 

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5. Exploring perspectives and experiences of diverse learners' acceptance of online educational engineering games as learning tools in the classroom

   Cook-Chennault, Kimberly ; Villanueva, Idalis ( January 2020 , Conference proceedings Frontiers in Education Conference)

   This Research Full paper focuses on perceptions and experiences of freshman and sophomore engineering students when playing an online serious engineering game that was designed to improve engineering intuition and knowledge of statics. Use of serious educational engineering games has increased in engineering education to help students increase technical competencies in engineering disciplines. However, few have investigated how these engineering games are experienced by the students; how games influence students' perceptions of learning, or how these factors may lead to inequitable perspectives among diverse populations of students. Purpose/Hypothesis: The purpose of this study was to explore the perceptions, appeal, and opinions about the efficacy of educational online games among a diverse population of students in an engineering mechanics statics course. It was hypothesized that compared to majority groups (e.g., men, White), women of color who are engineering students would experience less connections to the online educational game in terms of ease of use and level of frustration while playing. It is believed that these discordant views may negatively influence the game's appeal and efficacy towards learning engineering in this population of students. Design/Method: The Technology Acceptance Model (TAM) is expanded in this study, where the perspectives of women of colour (Latinx, Asian and African American) engineering students are explored. The research approach employed in this study is a mixed-method sequential exploratory design, where students first played the online engineering educational game, then completed a questionnaire, followed by participation in a focus group. Responses were initially analyzed through open and magnitude coding approaches to understand whether students thought these educational games reflected their personal culture. Results: Preliminary results indicate that though the majority of the students were receptive to using the online engineering software for their engineering education, merely a few intimated that they would use this software for engineering exam or technical job interview preparation. A level-one categorical analysis identified a few themes that comprised unintended preservation of inequality in favor of students who enjoyed contest-based education and game technology. Competition-based valuation of presumed mastery of course content fostered anxiety and intimidation among students, which caused some to "game the game" instead of studying the material, to meet grade goals. Some students indicated that they spent more time (than necessary) to learn the goals of the game than engineering content itself, suggesting a need to better integrate course material while minimizing cognitive effort in learning to navigate the game. Conclusions: Preliminary results indicate that engineering software's design and the way is coupled with course grading and assessment of learning outcomes, affect student perceptions of the technology's acceptance, usefulness, and ease of use as a "learning tool." Students were found to have different expectations of serious games juxtaposed software/apps designed for entertainment. Conclusions also indicate that acceptance of inquiry-based educational games in a classroom among diverse populations of students should clearly articulate and connect the game goals/objectives with class curriculum content. Findings also indicate that a multifaceted schema of tools, such as feedback on game challenges, and explanations for predictions of the game should be included in game/app designs. 

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