Reviewerly/exHarmony huggingface dataset:
WIP: Toward a Free-Body Diagram Mobile Application

Authors:
raw_author_name: Andrew Sloboda
raw_affiliation_string: Bucknell University; Worcester Polytechnic Institute

Abstract: 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

Authors:
raw_author_name: Sarah Parker
raw_affiliation_string: University of British Columbia, Vancouver

Abstract: 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

Authors:
raw_author_name: Pankaj Khatiwada
raw_affiliation_string: Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
raw_author_name: Bian Yang
raw_affiliation_string: Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
raw_author_name: Jia-Chun Lin
raw_affiliation_string: Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway

Abstract: 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

Authors:
raw_author_name: William Randall

Abstract: 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

Authors:
raw_author_name: Rastislav Metruk
raw_affiliation_string: Faculty of Humanities, Department of English Language and Literature, University of Žilina, Žilina, Slovakia

Abstract: 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

Authors:
raw_author_name: Xiaojun Li
raw_affiliation_string: State Grid Sichuan Electric Power Corporation Metering Center, China
raw_author_name: PeiDong He
raw_affiliation_string: State Grid Sichuan Electric Power Corporation Metering Center, China
raw_author_name: KeLi Liu
raw_affiliation_string: State Grid Sichuan Electric Power Corporation Metering Center, China
raw_author_name: ShuYu Deng
raw_affiliation_string: State Grid Sichuan Electric Power Corporation Metering Center, China
raw_author_name: LI Xiao
raw_affiliation_string: State Grid Sichuan Electric Power Corporation Metering Center, China

Abstract: 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

Authors:
raw_author_name: Matthew James
raw_affiliation_string: Virginia Polytechnic Institute and State University
raw_author_name: Andrew Katz
raw_affiliation_string: Virginia Polytechnic Institute and State University

Abstract: 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.
Performing a Knowledge Audit Within a South African Chemical Manufacturer

Authors:
raw_author_name: Hanlie Smuts
raw_affiliation_string: University of Pretoria, South Africa

Abstract: Despite the increased efforts of organisations to implement knowledge management (KM) initiatives, many fail. Performing a knowledge audit before embarking on KM activities increases the probability of success. In this interpretive case study, the authors applied a modified version of the knowledge audit methodology developed by Perez-Soltero et al. to a South African (SA) veterinary medicine, fine chemical, and pharmaceutical manufacturer. The authors engaged members of the organisation in focus group sessions and individual interviews to identify knowledge assets related to core processes within the organisation. They used the data from the focus groups and individual interviews to identify and articulate many of the knowledge assets at the core of the organisation's current success. In addition, the process of conducting a knowledge audit and making the steps explicit while adjusting for context, may inform researchers and practitioners in terms of knowledge audit approach.
Board 174: Stakeholder Views in Building a Sustainable Engineering Learning Ecosystem: Afterschool Green Energy, Robotics, and Automation (Work in Progress)

Authors:
raw_author_name: Matthew Aldeman
raw_affiliation_string: Illinois State University
raw_author_name: Jin Jo

Abstract: Abstract This research was part of the first year of a National Science Foundation funded project aimed at promoting high school students' interest in green energy, robotics, automation and post-secondary engineering and technology study. High school teachers, undergraduate majors in STEM areas, and community based non-profit organizations were involved in this afterschool engineering program for high school students with the goal of broadening participation among minoritized groups in engineering and engineering technology. This study investigated how these different stakeholders' views aligned and diverged about (1) the characteristics of STEM engagement, and (2) the factors that influence the development of engineering identities. The purpose of this investigation was to uncover the relationships between community members' viewpoints, community assets, and the positionality of the project personnel.
Data Mesh Meets Blockchain

Authors:
raw_author_name: Abdulaziz Almaslukh
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Abdulmajeed Alameer
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Fahad Alkadyan
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Nasser Allheeib
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Abdulaziz Alhadlag
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Yazeed Alabdulkarim
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Abstract: Abstract Effective dataset management is crucial for enterprises to make informed decisions and remain competitive. However, centralized dataset management approaches often result in poor scalability, unclear governance, inaccessible data silos, and duplication of efforts. This paper proposes a distributed blockchain-based framework inspired by the data mesh architecture to address these challenges. Our proposed framework leverages blockchain’s decentralized nature to enable efficient and transparent dataset sharing across enterprise business domains. By turning datasets into digital assets and business domains into peers, our framework utilizes blockchain smart contracts to allow business domains to view, request, and share datasets. In this paper, we describe the details of our framework, and we analyze it from scalability, accessibility, security, and data governance perspectives. To validate our framework, we provide a proof-of-concept implementation with a publicly available source code.
Predicting Team Function Using Bayesian and Cognitive Diagnostic Modeling Approaches

Authors:
raw_author_name: Jeong Hin Chin
raw_affiliation_string: University of Michigan
raw_author_name: Jing Ouyang
raw_affiliation_string: University of Michigan
raw_author_name: Robin Fowler
raw_affiliation_string: University of Michigan
raw_author_name: Gongjun Xu
raw_affiliation_string: University of Michigan

Abstract: Abstract Team-based learning is commonly used in engineering introductory courses. As students of a team may be from vastly different backgrounds, academically and non-academically, it is important for faculty members to know what aid or hinder team success. The dataset that is used in this paper includes student personality inputs, self-and-peer-assessments of teamwork, and perceptions of teamwork outcomes. Using this information, we developed several bayesian models that are able to predict if a team is working well. We also constructed and estimated Q-matrices which are crucial in explaining the relationship between latent traits and students' characteristics in cognitive diagnostic models. The prediction and diagnostic models are able to help faculty members and instructors to gain insights into finding ways to separate students into teams more effectively so that students have a positive team-based learning experience.
The Dilemma of the Copyrights of Artificial Intelligence

Authors:
raw_author_name: Mohamad Albakjaji
raw_affiliation_string: Prince Sultan University, Saudi Arabia

Abstract: Artificial intelligence (AI) and intellectual property (IP) share some key similarities, such as uncertainty in predictions, processing a massive amount of data, and machine learning. Yet, they also differ from each other. This paper provides background information on how these two domains have evolved over time. It also highlights how Saudi Arabia's IP system differs from those of other countries. Furthermore, this article explores the relationship between AI and IP and their application in copyright. This study is significant as it helps identify the challenges and opportunities that AI presents with respect to IP in terms of copyright. Finally, this article makes recommendations that will help protect both AI and IP.
Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

Authors:
raw_author_name: Zhanchuan Cai
raw_affiliation_string: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China

Abstract: Reversible data hiding (RDH) is a technique that embeds secret data into digital media while preserving the integrity of the original media and the secret data. RDH has a wide range of application scenarios in industrial image processing, such as intellectual property protection and data integrity verification. However, with the increasing prevalence of color images in industrial applications, traditional RDH methods for grayscale images are inadequate to meet the requirements of image fidelity. This paper proposes an RDH method for color images based on channel reference mapping (CRM) and adaptive pixel prediction. Initially, the CRM mode for a color image is established based on the pixel variation correlation between the RGB channels. Then, the pixel local complexity context is adaptively selected using the CRM mode. Next, each pixel value is adaptively predicted based on the features and characteristics of adjacent pixels and reference channels, and then data is embedded by expanding the prediction error. Finally, we compare seven existing RDH algorithms on the standard image dataset and the Kodak dataset to validate the advantages of our method. The experimental results demonstrate that our approach achieves average peak signal-to-noise ratio (PSNR) values of 63.61 and 60.53 dB when embedding 20,000 and 40,000 bits of data, respectively. These PSNR values surpass those of other RDH methods. These findings indicate that our method can effectively preserve the visual quality of images even under high embedding capacities.