This study aimed to design, develop, and evaluate a virtual reality (VR)-based training module for additive manufacturing (AM), specifically Powder Bed Fusion (PBF) 3D printing, that simultaneously delivers declarative and procedural knowledge in classroom settings. While VR has shown promise in teaching procedural and declarative knowledge separately, few studies have assessed integrated VR training modules in real classroom environments, especially in AM education. A VR training system with four sub-modules: tutorial, learning, training, and testing, was developed in Unity 3D. Twenty-five university students completed the module over three days using Meta Quest 3 headsets. Efficacy was assessed using the Simulator Sickness Questionnaire (SSQ), Presence Questionnaire (PQ), System Usability Scale (SUS), and NASA Task Load Index (NASA-TLX). MANCOVA and ANCOVA were used to analyze demographic and experiential predictors of outcomes. The average SSQ score was 7.19, indicating low simulator sickness. SUS scores improved from 59.00 (SD = 15.78) to 64.60 (SD = 16.89) post-exposure. PQ scores increased from 74.80 to 79.16. NASA-TLX results, assessed post-lab visit, showed low physical demand (M = 14.0, SD = 8.291562), high performance (M = 72.0, SD = 23.048861), and a significant reduction in frustration (M = 35.4, SD = 27.985115) after real-world exposure. Video game experience significantly predicted usability both before (p = 0.047) and after the lab experience (p = 0.045). The VR-based AM module demonstrated feasibility, acceptable usability, and cognitive engagement, supporting its value for integrated learning in classroom settings. This study offers a replicable framework for implementing immersive, usability-driven VR tools to enhance technical education in manufacturing and STEM.

Pro-bono community clinics often operate under severe resource constraints while serving a high volume of patients. In such environments, inefficient facility layouts can increase unnecessary practitioner movement, leading to fatigue, workflow disruption, and lost time. Optimizing clinic layout is therefore critical for improving operational efficiency. Bluetooth Received Signal Strength Indicator (RSSI) measurements collected from mobile devices offer an inexpensive and lightweight alternative to dense sensor deployments for analyzing practitioner mobility in low-resource settings. However, when sensor density is low, RSSI datasets are frequently affected by missing values, largely due to signal scanning frequencies governed by mobile operating system policies. This paper investigates two imputation strategies for addressing missing values in RSSI data to reconstruct practitioner mobility paths using a newly developed path reconstruction algorithm. Reconstructed trajectories are evaluated against both algorithm-generated reference paths and human-recorded ground truth using Levenshtein accuracy, reflecting the sequential nature of movement paths. Results from two case studies show that, without imputation, reconstructed paths collapse into trivial sequences with low accuracy (approximately 17%). Nearest-neighbor imputation yields moderate improvements, achieving accuracy between 36% and 62%, while timestamp-based imputation consistently produces the most coherent trajectories and the highest accuracy, ranging from 62% to 68%. These findings demonstrate that temporally informed imputation provides a more reliable foundation for Bluetooth RSSI-based mobility tracking in sparse indoor deployments.

Data Science is an essential concept for the twenty-first-century workforce. As a result, the need to incorporate core data-intensive skills into nearly every discipline has recently gained increased attention. This paper details our experiences developing a framework where CS and non-CS faculties collaborate on developing contextual data science modules tailored to the needs of multiple disciplines, academic levels, and student and instructor preparedness and integrate them into existing undergraduate courses. A quantitative and qualitative analysis approach is used to explore the following research question: To what extent a common framework be utilized to integrate data science-based learning objectives into multiple science, social, and health science courses to generate more data science-informed graduates? A significant number of underrepresented and minority (URM) students were exposed to data science knowledge and skills throughout this intervention during the last three years. The student performance and survey results show that most students understood the concepts and could use related tools to organize and use data to support their claims and conclusions to a certain extent. Further analysis of instructor perspectives identified commonalities, such as the benefits of incorporating data science concepts into disciplinary contexts through hands-on exercises using real-world data, as well as differences, such as student activity types based on instructor and student preparedness and usage and rigor of data science tools.

Monitoring the indoor mobility of healthcare practitioners is a key factor in improving clinic layout and time management. Traditional methods for tracking practitioners’ indoor mobility rely on subjective self-reports or resource-intensive manual assessments. This paper introduces a novel mobile application designed to automate indoor mobility detection for healthcare practitioners, leveraging smartphone sensors and Bluetooth Received Signal Strength Indicators. The application combines real-time data collection with inter-device data sharing from a swarm of stationary devices to generate detailed mobility profiles. Initial evaluations suggest the application enhances traditional mobility assessments' efficiency and accuracy, offering a scalable and cost-effective solution for healthcare practitioner monitoring. The proposed tool aims to enable clinics with actionable data, improving practitioner efficiency and patient outcomes while reducing overhead associated with manual tracking.

The surge in data science courses highlights an educational shift towards incorporating data skills as a fundamental component of a well-rounded academic curriculum. This growth indicates a recognition of the critical role that data plays in shaping our understanding of complex issues in today's data-driven society. It also underscores the need for data science education to be accessible, representative, and tailored to a wide array of learners and professionals. The work to date to establish a data science foundational knowledge framework represents a pivotal step in formalizing and enhancing data science education. Yet, these frameworks have been largely designed for those pursuing data science careers. The field is in need of a data science framework that focuses on the essential introductory knowledge and skills for non-CS students to build a solid foundation in data science. This paper provides a description of the literature review process and experiences the research team has drawn from to develop such a framework. It also positions this framework for future research in studying effectiveness and alignment to the K-12 space.

In an indoor space, determining a person's mobility patterns has research significance and applicability in real-world scenarios. When mobility patterns are determined, layout optimization can be implemented in indoor spaces to improve efficiency. This research aimed to determine a person's path using Received Signal Strength Indicator (RSSI) data collected from Bluetooth-enabled mobile devices. Mobile app-based mobility detection using Bluetooth RSSI has the advantage of low cost and easy implementation. The research methodology involves developing a Bluetooth RSSI mobility application system to determine the path of a moving mobile device using a vectorized algorithm. The paper presents challenges in creating such a software system, its architecture, the data collection and analysis process, and the results of mobility detection. This research shows that Bluetooth-enabled mobile devices and Bluetooth RSSI data can be used to determine the path in an indoor space with workable accuracy.

It is a well-documented challenge to keep students engaged and motivated in out-of-class activities. More students now have part- or full-time jobs and less time to study. Supporting their academic growth and success requires acknowledging the higher commitments to the jobs while providing appropriate mechanisms to make the best use of their available times. This paper presents a mobile educational platform, Dysgu, that aims to engage students in out-of-class activities. An initial study completed on this platform investigates the role of peer influence to increase student engagement in an early college class. Data indicates students prefer Dysgu for out-of-class activities compared to traditional pen- and paper-based activities. Students noted that peer influence, in the form of scores compared to the rest of the class, was highly motivating. We also observed more on-time submissions when using Dysgu.

Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers’ limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate level online classes on the necessity of an attention tracking tool to understand student engagement during an online class. This paper outlines the design of such a monitoring tool that automatically tracks the attentiveness of the whole class by tracking students’ gazes on the screen and alerts the teacher when the attention score goes below a certain threshold. We assume the benefits are twofold; 1) teachers will be able to ascertain if the students are attentive or being engaged with the lecture contents and 2) the students will become more attentive in online classes because of this passive monitoring system. In this paper, we present the preliminary design and feasibility of using the proposed tool and discuss its applicability in augmenting online classes. Finally, we surveyed with 31 students asking their opinion on the usability as well as the ethical and privacy concerns of using such a monitoring tool.

Generating paths of a mobile device in indoor space by sensing its Bluetooth RSSI value is challenging but has real-world applications. Although Bluetooth RSSI suffers from different factors that limit its usability, this research shows that it can still be used to detect mobility and, over a duration of time, can be used to form paths. This poster presents algorithms that can create a path of a moving mobile device by sensing its RSSI values over time and then presents early results of the algorithm's effectiveness while tracking health practitioners' movement within a community care clinic setting.

Student engagement with out-of-class activities is becoming more difficult as students spend fewer hours outside the classroom studying the content. This research developed a mobile educational platform, Dysgu, to provide students with an optimal learning experience outside of the classroom. Dysgu includes social networking and gamification features to increase student engagement. The platform offers interactive auto-graded assessments to help students practice concepts and take tests. Students can see their scores and a summary of the performance of the rest of the class. We used Dysgu for multiple out-of-class activities at two universities with different student demographics for two semesters. The data shows that students obtain better grades when using Dysgu. We also saw more on-time or ahead-of-time submissions with Dysgu. Survey responses indicated several Dysgu features which students found helpful. We conclude that digital educational platforms should consider features to support scaffolding to master the concept, peer influence to keep students engaged, self-reflection to foster critical thinking, and easy adaption of the platform to reduce faculty workload and improve students’ acceptance of the system.

While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization.

Students' conceptual knowledge about parallelism continues to grow because of the increasing infusion of parallelism topics in all courses, even if they are not dedicated to those topics. Most times, such infusion is through high-level theoretical problems, as listed in the textbooks. However, without providing corresponding real-life instances, such problems might not engage students with the content in such courses. Does providing real-life activities along with theoretical constructs help students to grasp such topics better? As an attempt to explore student comprehension of parallelism in such course scenarios, this paper describes an unplugged assignment, where students were asked to categorize their daily activities into concurrent and parallel tasks. The paper then presents a thematic analysis of student experience using three semesters' worth of student data. The analysis shows that, although students exhibit limited knowledge of parallelism in real-life situations and have numerous misconceptions about parallelism; their participation in such assignments are deliberate, their thinking processes are analytical, their writing skills in expressing such concept is limited and their impressions about such assignments are positive. The paper then presents an effort to enhance student's comprehension and shows how the intervention has improved students' understanding of this critical concept. This study confirms that it is essential to provide real-life analogies and examples to facilitate a greater understanding of parallelism. Because providing only theoretical examples might not engage a diverse body of students in courses that are just trying to expose students to this critical topic.

Keeping students engaged with the course contents between classes is challenging. Although out-of-class activities are used to address this challenge, they have limited impacts on improving student's engagement outside the classroom because of the lack of real-time feedback and progress updates. For this reason, these types of activities are less appealing to the current generation of students who feel the pull of instant gratification more intensely. This paper presents a mobile learning system, named Dysgu, which enables students to work on their out-of-class activities, compare their progress with the rest of the class, and improve their self-efficacy. The goal of Dysgu is to better engage students with out-of-class activities and reduce procrastination in those activities. By using Dysgu, faculty can facilitate and monitor learning even after the students leave the classroom and intervene early when students fall behind their peers.

The majority of Computer Science courses frequently use out-of-class activities to promote skill-building. Yet, the shortcomings of these activities limit their applicability in improving students' skills. This poster presents an innovative mobile-app-based approach to engage students in out-of-class activities. The app - Dysgu - builds on the theories of social learning and enables instructors to break regular out-of-class activities into smaller and interactive entities with instructional scaffolding. Dysgu allows students to compare their progress with their peers and provides personalized notifications and feedbacks to keep students engaged. The goal is to improve student's self-efficacy and engagement in out-of-class activities.

This paper presents an out-of-class active learning environment, called Dysgu. Dysgu presents an innovative approach to out-of-class activities by combining multiple dimensions of best practices from different spectrum of student learning into a coherent idea and delivering such activities with personalization and adaptation. The goal of the Dysgu system is to study the impact of frequent out-of-class activities on student learning and engagement when the students can compare their progress with the rest of the class and where the activities are smaller (in scope) with scaffolding support, are interactive in nature, and delivered via a mobile platform. Initial usability tests and software engineering quality matrices show that the software is easy to use, manage and extend.

In this poster, we describe our effort to develop, pilot, and evaluate a model for infusing data literacy into undergraduate curricula across a variety of disciplines using a modular approach. Our pilot implementation achieved reasonable success in attaining student learning outcomes, enhanced engagement, and interests.

It is believed that if students are well engaged in the learning process within the classroom, they will continue the learning process independently outside the classroom. To facilitate such out-of-class learning, there is a plethora of traditional techniques with a variety of learning theoretical backgrounds. While out-of-class activities based on these techniques have shown to improve a student’s overall quality of learning, traditional activities lack the supervision, instant feedback, and personalization that the current generation of students expects. With the rising cost of college tuition, many of today’s students are working more hours outside of an educational setting and therefore need more supervision and encouragement than their predecessors. These factors make traditional out-of-class activities not effective to achieve the desired level of student learning and engagement outside the classroom. The faculty needs to rethink ways to redesign traditional out-of-class activities to make these activities more effective for this generation of students. This paper presents a review of the literature on and categorization of traditional out-of-class activities. The paper also discusses the results of a survey of what the faculty is doing to engage and continue student learning outside the classroom. Finally, the paper presents a new way of designing and delivering out-of-class activities that have the potential to increase student engagement with the help of instructional scaffolding, interactive activities, and personalization and adaptation.

Big data and cloud computing collectively offer a paradigm shift in the way businesses are now acquiring, using and managing information technology. This creates the need for every CS student to be equipped with foundational knowledge in this collective paradigm and to possess some hands-on experience in deploying and managing big data applications in the cloud. We argue that, for substantial coverage of big data and cloud computing concepts and skills, the relevant topics need to be integrated into multiple core courses across the undergraduate CS curriculum rather than creating additional standalone core or elective courses and performing a major overhaul of the curriculum. Our approach to including these topics is to develop autonomous learning modules for specific core courses in which their coverage might find an appropriate context. In this paper, three such modules are discussed and our classroom experiences during these interventions are documented. So far, we have achieved reasonable success in attaining student learning outcomes, enhanced engagement, and interests. Our objective is to share our experience with the academics who aim at incorporating similar pedagogy and to receive feedback about our approach.

In the United States, pedestrians aged ≤14 suffer the highest percentage of motor vehicle collisions leading to injuries and fatalities. In part to reduce motor-vehicle related crashes, transportation researchers are pursuing the implementation of automated vehicles. Vehicle automation will eventually remove human control from the vehicle, but this may also remove interpersonal communication between pedestrians and human drivers. Therefore, many studies have investigated pedestrians' choice of features on autonomous vehicles (AVs) to facilitate communication of vehicle intention. The inclusion of child populations in these studies has been rare, however. This study investigated pedestrians' understandability of the external features on autonomous vehicles, considering different vehicle sizes and physics (speed and distance), and included both children and adults. The results revealed that children relied entirely on the communicating features of AVs to make their judgement on their safety, thereby adopted a higher risk strategy than their adult counterparts.