Reviewerly/exHarmony huggingface dataset:
Clustering of Animation View Times in an Interactive Textbook for Material and Energy Balances

Abstract Data science tools can help elucidate trends from clickstreams and other interactions generated by students actively using interactive textbooks. Specifically, data generated when using animations, which are multi-step visuals with text captions, will be presented in this work. Each animation step divides content into appropriate chunks, and so aligns with tenets of cognitive load theory. Both the quantity and timing of students' clicks record provide large data sets when examining students across hundreds of animations and multiple cohorts. Specifically, an interactive textbook for a chemical engineering course in Material and Energy Balances will be examined and build upon data presented previously. While most of the previous data focused on very high reading completion rates (>99% median) compared to traditional textbooks (20-50%), a deeper examination of how long students take when watching animations will be explored. With over 140 unique animations and tens of thousands of completed views over five cohorts, a spectral clustering algorithm applied to students' animation view times distinguished several types of animation watching behavior as well as monitor changes in this animation watching behavior over the course of a semester. After examining different numbers of clusters, two or three clusters in each chapter captured the animation usage. These clusters usually correspond to a group of students who watched animations at 1x speed (longer), another group who watched at 2x speed (shorter), and a third group, when present, who watched irregularly, including skipping animations. Overall, more students belonged to the belonged to the cluster with longer view times, with 63% of students aggregated over all cohorts and chapters compared to 35% of students in the cluster with shorter view times. The remaining 2% of students belonged to the irregular cluster, which was present in less than one quarter of the chapters. Many students stayed in the same cluster between chapters, while a smaller fraction switched between the longer and shorter clusters.
Utilizing Virtual Reality to Fortify Professional Skills in Engineering Education

Several skills are needed and, most of the time, are required by the accreditation bodies as part of the preparation for entering the professional practice of engineering.Academic institutions require vision, planning, and time and budget allocation to achieve these objectives.Students are required to produce solutions to meet the specific consideration.For that reason, practical experience in engineering principles is the key to enhancing student knowledge in engineering education.Virtual reality (VR) technology can be implemented to simulate the real workplace and for educational purposes.Applying VR can encourage students to advance their understanding of the course content.And also, the student should be able to actively engage in the learning process.Implementing this technology makes it possible to improve the effectiveness of education by simplifying what is normally challenging to understand in physical classrooms.In the case of the exploitation of energy resources to differentiate the operation of offshore oil and gas rigs, VR technology can be applied to enhance the understanding and visualization of the requirement and components of the types of equipment.This paper presents real-world petroleum facilities to the classroom with the help of VR technology in the Middle East campus.As part of the innovative teaching methods in engineering courses, the main objective of applying VR is to overcome the challenge of the limited real trips, improve interest, and augment the student learning experience by applying innovative teaching methods.VR technology connects formal and informal learning processes with actual experiences.Several engineering concepts can be easily introduced thru the technology.Feedback from students shows their excitement and engagement in the learning process.Also, it supports promoting an active learning process for the new generation and engineering education.
Undergraduate Research in Quantum Computing: Lessons Learned from Developing Student Researchers

At Loyola she has taught all levels of lectures and labs
Survey of Research in Engineering Librarianship, 2015-2019

Abstract This work-in-progress research study aims to examine what research was conducted pertaining to engineering librarianship from 2015-2019 (pre-pandemic) with a particular focus on methodology. Peer-reviewed articles published between 2015-2019 were surveyed to identify who is performing research in engineering librarianship, what research methods are being used, if the studies are repeatable, and if the methods chosen are appropriate for the research question under investigation. Each paper that matched the inclusion criteria was examined and information was extracted relating to authorship, journal published in, research design, method, and analysis, as well as research question and instrument (when relevant). Simple statistical analysis will be conducted to derive patterns around research papers in engineering librarianship during this 5-year period. This paper will report the in-progress results of this comprehensive literature review.
Cybersickness in Virtual Reality: The Role of Individual Differences, Its Effects on Cognitive Functions and Motor Skills, and Intensity Differences during and after Immersion

Background: Given that VR is used in multiple domains, understanding the effects of cybersickness on human cognition and motor skills and the factors contributing to cybersickness is becoming increasing important. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills. Methods: 30 participants, 20–45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR. During immersion, they were exposed to a roller coaster ride. Before and after the ride, participants responded to the CSQ-VR and performed VR-based cognitive and psychomotor tasks. After the VR session, participants completed the CSQ-VR again. Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness. Pupil dilation emerged as a significant predictor of cybersickness. Experience with videogaming was a significant predictor of cybersickness and cognitive/motor functions. Cybersickness negatively affected visuospatial working memory and psychomotor skills. Overall the intensity of cybersickness’s nausea and vestibular symptoms significantly decreased after removing the VR headset. Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness. Pupil dilation appears to be a cybersickness biomarker. Cybersickness affects visuospatial working memory and psychomotor skills. Concerning user experience, cybersickness and its effects on performance should be examined during and not after immersion.
The State of the Practice Integrating Security in ABET Accredited Software Engineering Programs

Abstract Within the software engineering discipline, concerns related to security continue to grow. Since the early 2000's, the number of cyberattacks against deployed software systems has significantly grown. In 2014, recognizing this concern, a modification to the ABET EAC program accreditation criteria for software engineering was made, explicitly requiring topical coverage of security for accredited programs. Since taking effect in 2016, all programs in software engineering have been required to demonstrate appropriate coverage of the topic as part of the accreditation process. While the criteria requires that the topic of security be covered, the implementation has been left open to individual programs. This article serves two purposes. First and foremost, it provides an updated status on the demographics of accredited software engineering programs. In doing so, it also provides a snapshot of the state of the practice of how security is integrated into program curricula by analyzing the 37 domestic ABET accredited bachelors' programs in software engineering. The article will identify at a high level the topics that are covered in the programs, as well as provide an overview of other aspects of the institutions which impact the depth and breadth of security coverage available to undergraduate students.
Decision support model to leverage extended reality technologies to augment manufacturing education

Amit Jariwala develops and maintains industry partnerships to support experiential, entrepreneurial, and innovative learning experience within the academic curriculum of the school.He is a Woodruff School Teaching Fellow and strives to enhance education by developing classes, workshops, and events focused on implementing hands-on, collaborative learning through solving real-world problems.He directs the operations of the Institute-wide Georgia Tech Capstone Design Expo, which highlights projects created by over 2000 Georgia Tech seniors graduating students on an
Board 423: Using Agile Principles for Cohort Building in a Graduate Software Engineering Program

This report describes an approach to building a cohort of students in a graduate software engineering program supported by the Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) Program of the National Science Foundation.We used many agile principles for building and sustaining the cohort, which is scaffolded around the students' academic studies and their simultaneous work on an externally sourced software development project.We discuss how the agile principles were applied in practice in this S-STEM project, how they helped build a cohesive student cohort, and how
A deep learning approach for the comparison of handwritten documents using latent feature vectors

Abstract Forensic questioned document examiners still largely rely on visual assessments and expert judgment to determine the provenance of a handwritten document. Here, we propose a novel approach to objectively compare two handwritten documents using a deep learning algorithm. First, we implement a bootstrapping technique to segment document data into smaller units, as a means to enhance the efficiency of the deep learning process. Next, we use a transfer learning algorithm to systematically extract document features. The unique characteristics of the document data are then represented as latent vectors. Finally, the similarity between two handwritten documents is quantified via the cosine similarity between the two latent vectors. We illustrate the use of the proposed method by implementing it on a variety of collections of handwritten documents with different attributes, and show that in most cases, we can accurately classify pairs of documents into same or different author categories.
WIP: Toward a Free-Body Diagram Mobile Application

No skill is more important for a student of mechanics than the ability to draw a complete and accurate free-body diagram (FBD).A good FBD facilitates proper accounting of forces when writing the balances that lead to governing equations in statics, solid mechanics, and dynamics.Because this skill is essential, educational approaches that improve the ability of students to draw correct FBDs are critical for maximizing the potential of the next generation of engineers.Traditionally, learning to draw FBDs involves classroom instruction followed by homework practice consisting of problems drawn from a textbook.Homework as practice does not serve all students well, because it does not scaffold the process of drawing FBDs in terms of distinct tasks (e.g., isolating the body, considering support reactions) nor does it offer immediate feedback, which students often need to avoid falling into the same error repeatedly.To address these shortcomings, we embarked on the design, implementation, and testing of a mobile application (app) that offers an alternative venue for FBD practice.The app provides students with asynchronous opportunities for training, varied tasks that target specific FBD issues, and several levels of immediate feedback.We hypothesize that the gamified environment and puzzle-based gameplay will improve student skill and self-efficacy in drawing FBDs, particularly for women, who may feel less confident in their spatial skills.Data collected to describe student experiences may also provide additional insight into how to improve FBD instruction generally.In this paper, we detail the process for designing and implementing the app and provide initial data regarding student impressions and use.The app was piloted in Fall 2022 in a large Introduction to Statics course as a non-graded study activity; all students except one (n=97) participated in an evaluation of its design features and user experiences.Approximately half (54%) of students indicated they had played half or more of the available games.When commenting about how the FBD app did, or did not, help their learning, 49% of respondents appreciated that the app allowed additional opportunities for practice.Students used these opportunities to further develop several skills, such as visualizing the system and setting up accurate diagrams, which strengthened their confidence and reviewed key concepts.While describing the value of practicing through the app, 21% of students called out how the app provided feedback.They specifically mentioned the positive experiences of receiving feedback that is immediate, that explains boundary connections, and that deepens learning after mistakes are made.These and other findings from the pilot study are discussed with corresponding next steps for development.
Research Data Sharing in Engineering: A Report on Faculty Practices and Preferences Prior to the Tri-Agency Policy

Abstract The Tri-Agencies Council of Canada that includes the Natural Sciences and Engineering Research Council of Canada (NSERC) is implementing its Research Data Management (RDM) Policy in the Spring of 2023 [1]. The policy requires Canadian post-secondary institutions to develop an Institutional RDM Strategy to support and guide researchers funded by one or more of the Tri-Agencies. Researchers will be required to provide a Data Management Plan (DMP) and deposit their research data into a repository at the time of publication in order to fulfill funding obligations. This paper describes the survey results conducted at a U15 research institution in Canada asking engineering faculty about their research data sharing practices and preferences. The purpose of the survey was to answer the following questions: 1. How well prepared are engineering researchers for data deposit, 2. Are engineering researchers willing to share their data, and 3. What barriers exist for sharing engineering data? Results demonstrate knowledge of and acceptance of open access (OA) practices but when it comes to data, engineering researchers are more reluctant to share their data widely and may need guidance on RDM best practices. Subject librarians can prepare to provide assistance to faculty and educate students by gaining an understanding of engineering data management and sharing behaviors. Faculty may benefit from RDM support through all stages of the data life cycle and students may benefit from RDM literacy introduced into their curriculum. The described survey results in this paper aim to help the subject librarian identify where they might best offer support for faculty and students. [1] Government of Canada, "Tri-Agency Research Data Management Policy," Mar. 15, 2021. https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy (accessed Oct. 28, 2022).
A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.
The Development, Assessment, and Advancement of a Student-Centered Cyber Risk Management Course

Managing risk is a key component to achieving a strong cyber security posture.Practitioners must consider a combination of human, technology, and policy-based factors when establishing their postures.To help students understand these interdependencies, we designed and developed a course to apply, analyze, synthesize, and evaluate these in a real-world environment.Students have gained an appreciation for various technical and managerial concepts related to cyber security and demonstrated the knowledge gained by developing submissions to the annual Maritime Cyber Risk Symposium student poster contest.This paper describes the multi-year developmental journey in standing up a Cyber Risk Management course linked to National Security Agency/Department of Homeland Security National Center of Academic Excellence in Cyber Education knowledge units, ABET outcomes for cybersecurity computing programs, and the National Institute of Standards and Technology framework and guidance.It includes studentcentered individual classroom learning, project-based team learning, and team development of scholarly work for submission and external evaluation.The efficacy of this approach after two offerings is based upon student feedback, ABET and NCAE assessment, and external results.The paper concludes with how we see the course evolving as the cyber systems major evolves.Teaching cyber-focused concepts in an interdisciplinary manner is critical and, in that spirit, the teaching of this course is moving from the School of Engineering and Cyber Systems to the School of Leadership and Management.
Mobile-assisted language learning and pronunciation instruction: A systematic literature review

Abstract In recent years, learners worldwide have expressed increasing interest in mobile learning and the utilization of mobile devices for language learning purposes. However, few research synthesis studies have focused on pronunciation development in relation to employing MALL (mobile-assisted language learning). This review article contributes to the discourse on how mobile learning aids in acquiring and practicing EFL (English as a foreign language) pronunciation since it is this language system that is both a crucial and frequently neglected aspect of English language learning. With the emergence of e-generation, it is necessary to investigate the influence of MALL on pronunciation acquisition. This paper provides a systematic literature review of the findings of 15 empirical studies published between 2015 and 2022, which is based on PRISMA 2020. The aim of the review is threefold: to investigate the effect of mobile devices on L2 learners’ pronunciation performance, examine learners’ attitudes toward employing mobile learning for pronunciation practice and development, and identify which mobile devices and apps are present in the process. The results indicate that overall, mobile learning exerts a beneficial impact on L2 pronunciation and that participants in the reviewed studies demonstrate positive attitudes toward MALL. Moreover, smartphones appear to be at the center of attention as the primary mobile device, and it was revealed that a variety of mobile apps and platforms have been employed in the studies. The article concludes with pedagogical implications and recommendations for potential research exploration in the future.
Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.
Pushing Ethics Assessment Forward in Engineering: NLP-Assisted Qualitative Coding of Student Responses

Abstract Recent headlines have featured large language models (LLMs), like ChatGPT, for their potential impacts throughout society. These headlines often focus on educational impacts and policies. We posit that LLMs have the potential to improve instructional approaches in engineering education. Thus, we argue that as an engineering education community, we should aim to leverage LLMs to help resolve challenges in engineering education. This study takes up one aspect of instructional design: valid assessment of students' learning outcomes in engineering ethics. In this study, we present a method for engineering educators to implement NLP in open-ended ethics assessments (here, written responses to an ethics case scenario). Grading such open-ended responses has challenges: it requires a non-trivial time commitment and attention to consistency. To mitigate these challenges, we developed an NLP approach based on open-source, transformer-based LLMs. We applied and evaluated our NLP approach for coding students' responses to an open-ended ethics case scenario in a first-year engineering course. The results showed that our NLP approach labeled 380 out of 472 sentences accurately. Conversely, only 8% (37 out of 472 responses) were inaccurately labeled. Overall, our NLP approach provides a step toward analyzing written responses to scenario-based assessments in a scalable manner. However, it is not perfect. One current downside of our NLP approach is that it requires a large upfront time investment in setting up the system. Our future work aims to lower that barrier to entry, thereby making it more accessible to a larger group of potential users.