This research-to-practice full paper presents and approach to bringing convergence to the undergraduate engineering context. Convergence is the process of integrating a variety of ideas, skills, and methods to create new ideas, skills, and methods in order to address complex, socially relevant challenges like the UN Sustainable Development Goals [1] and the National Academy of Engineering's (NAE) Grand Challenges [2]. In the US, the National Science Foundation (NSF) has been a major driver of convergence related research and has focused on work primarily at the graduate level and beyond. To explore how convergence concepts translate to an undergraduate engineering context this research to practice paper describes a taxonomy that translates convergent knowledge, skills, and mindsets into the domain of undergraduate engineering education. While we do not believe it is reasonable to expect undergraduates to engage with convergence in the same way as graduate students or postdoctoral scholars, we believe that they can develop in areas that will allow them to engage in convergent work later in their careers. This paper first defines convergence and then examines the challenges and opportunities related to developing a student's ability to do convergent work in an undergraduate context. The developed taxonomy outlines the knowledge, skills, mindsets, and structures that support convergent work from the larger research literature, and adapts these to an undergraduate context. The taxonomy is then used to conduct a gap analysis of an undergraduate electrical and computer engineering degree program. This analysis is based on the syllabi. This work was conducted in the context of an electrical and computer engineering department situated in a medium-sized primarily undergraduate liberal arts institution in the mid-Atlantic region. As the challenges and opportunities are similar to but also unique to this institution this work forms a rich case study that can inform similar efforts in other institutions and contexts where a similar gap analysis may be beneficial. The goal of this work is to enable others to analyze an their existing student experience to see what aspects of convergence are currently included.

Recent advances in sensing and computing technology have led to the proliferation of Cyber-Physical Systems (CPS) in safety-critical domains. However, the increasing device complexity, shrinking technology sizes, and shorter time to market have resulted in significant challenges in ensuring the reliability, safety, and security of CPS. This article presents a hybrid knowledge and data-driven approach for designing run-time context-aware safety monitors that can detect early signs of hazards and mitigate them in CPS. We propose a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with two optimization approaches for scenario-specific refinement and integration of STL specifications using data collected from closed-loop CPS simulations. We demonstrate the effectiveness of our approach in simulation using an autonomous driving system (ADS) and two closed-loop artificial pancreas systems (APS) as well as a publicly-available clinical trial dataset. The results show that a safety monitor developed with the proposed approaches demonstrates up to 4.7 times increase in average prediction accuracy (F1 score) over several well-designed baseline monitors while reducing both false-positive and false-negative rates in most scenarios.

Education literature has long emphasized the compounding benefits of reflective practice. Although reflection has largely been used as a tool for developing writing skills, contem-porary research has explored its contributions to other disciplines including professional occupations such as nursing, teaching and engineering. Reflective assignments encourage engineering students to think critically about the impact engineers can and should have in the global community and their future role in engineering. The Department of Electrical and Computer Engineering at a small liberal arts college adopted ePortfolios in a first-year design course to encourage students to reframe their experiences and cultivate their identities as engineers. Our recent work demonstrated that students who create ePortfolios cultivate habits of reflective thinking that continue in subsequent courses within our program's design sequence. However, student ability to transfer reflective habits across domains has remained unclear and encouraging critical engagement beyond the focused scope of technical content within more traditional core engineering courses is often difficult. In this work, we analyze students' ability to transfer habits of reflective thinking across domains from courses within a design-focused course sequence to technical content-focused courses within a degree program. Extending reflection into core courses in a curriculum is important for several reasons. First, it stimulates metacognition which enables students to transfer content to future courses. Second, it builds students' ability to think critically about technical subject matter. And third, it contributes to the ongoing development of their identities as engineers. Particularly for students traditionally underrepresented in engineering, the ability to integrate prior experiences and interests into one's evolving engineering identity may lead to better retention and sense of belonging in the profession. In the first-year design course, electrical and computer engi-neering students (N=28) at a liberal arts university completed an ePortfolio assignment to explore the discipline. Using a combination of inductive and deductive coding techniques, mul-tiple members of our team coded student reports and checked for intercoder reliability. Previously, we found that students' reflection dramatically improved in the second-year design course [1]. Drawing upon Hatton and Smith's (1995) categorizations of reflective thinking [2], we observed that students were particu-larly proficient in Dialogic Reflection, or reflection that relates to their own histories, interests, and experiences. In this paper, we compare the quality of student reflections in the second-year design course with those in a second-year required technical course to discover if reflective capabilities have transferred into a technical domain. We discovered that students are able to transfer reflective thinking across different types of courses, including those em-phasizing technical content, after a single ePortfolio activity. Furthermore, we identified a similar pattern of improvement most notably in Dialogic Reflection. This finding indicates that students are developing sustained habits of reflective thinking. As a result, we anticipate an increase in their ability to retain core engineering concepts throughout the curriculum. Our future plans are to expand ePortfolio usage to all design courses as well as some fundamental technical courses throughout the curriculum.

Wicked problems, the National Academy of Engineering's Grand Challenges, the United Nations' Sustainability Goals, and similar complex, global-scale endeavors fall under the broad umbrella of “convergent” work. Over the past two decades there has been an increase in interest and funding for work in this space. The NSF has two programs focused in this area, Growing Convergence Research and the Convergence Accelerator. Boston University's College of Engineering recently announced a focus on convergent projects and work. The National Academic of Engineering also has the Grand Challenge Scholars program with over 100 participating schools. The list continues to grow. The broad concept of convergence seems to be quite simple: combine the ideas, skills, and/or methods of multiple disciplines to create something new. More specific definitions vary and while the interest in convergence and convergent problems continues to increase, there is no easily operational definition of convergence. This is especially true with respect to undergraduate-level education where students have limited experience and knowledge to carry out such efforts. To better understand the variation that exists within the literature on convergence we conducted a systematic review to explore how convergence is defined in scholarly literature. We have identified a small number of categories within the definition space and conducted a thematic analysis of the aspects of each. The results show that there is a fairly consistent focus on the work being socially-relevant and on creating something new such as an idea, method, product, or process to address desired needs. Additionally, doing convergent work requires the integration of aspects of multiple disciplines and is conducted by diverse teams. Lastly, the disciplinary backgrounds of those teams almost always includes the natural and biological sciences with a subset the following disciplines: information or computing sciences, engineering, social sciences, and humanities. While there is some consistency in the definition, there also seems to be space for some variation which leaves for some level of choice in the definition.

Full Research Paper - Engineering students experience a wide range of learning practices through technical and analytical thinking and a process of reflection is needed to support them with their meaning-making process. In this study, we analyzed students’ perceptions on the role of empathy in human-centered design thinking processes. The reflection assignment took on a learning community pedagogical approach which can help students gain nuanced and deeper understanding of empathy. By situating the design experience within a community of peers, there are increased opportunities for norming and exchange, shared strategies and resources, and a sense of collective progress. We explored how the diversity in the dispositions and experiences of students played a unique and major role in framing and shaping design problems and perspective-taking. This paper reports the findings through a qualitative content analysis of reflective essays written by students in a sophomore engineering design course offered in two semesters. Through such assignments, instructors and curriculum integrators can understand the different ways their students are experiencing the curriculum.

Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them in MCPS. We present a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with an optimization method for patient-specific refinement of STL formulas based on real or simulated faulty data from the closed-loop system for the generation of monitor logic. We evaluate our approach in simulation using two state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results show the context-aware monitor achieves up to 1.4 times increase in average hazard prediction accuracy (F1score) over several baseline monitors, reduces false-positive and false-negative rates, and enables hazard mitigation with a 54% success rate while decreasing the average risk for patients.

This work-in-progress study continues prior work aimed at increasing the access of underrepresented minorities (URMs) in STEM fields to role models and mentors through values-focused, narrative profiles. We developed narrative profiles in which URM role models and majority mentors share their personal and professional experiences. Profile content follows from our work in identifying what URMs in STEM value in role models and mentors, allowing the profiles to serve as a vehicle to encourage URM students working toward STEM degrees. In this paper, we address the following research question: what themes emerge from the comparative analysis of "high-impact" role-model and mentor profiles? Specifically, we use emergent thematic analysis to explore similarities and differences across role model and mentor profiles that are considered "high-impact." We see that high-impact profiles tend to contain longer, more complete narratives that mention challenges in adjusting to their university, self-transcendent reasons for giving back to others and viewing failure as a process. These results will inform future profile development processes. By further refining these windows into the lives of role models and mentors, we can expand the definition of success in STEM and create emergent pathways of resilience for URM students. Through integrating profiles into the recruitment process, orientation, courses, and other campus resources, we can help students connect with role models and mentors as they transition to college life and continue into their future careers. We believe the flexibility and adaptability of this intervention has great potential for impact across STEM contexts.

This innovative practice work-in-progress paper examines student responses to a course whose content and pedagogy is aimed at preparing students to take on interdisciplinary work, and to consider human nature and societal context in their work. The course was developed in responds to the NAE call for undergraduate curricula to include interdisciplinary learning. The course helps students view engineering as a fundamentally human activity with all the features present when humans work together towards common goals. It uses active, experiential, and kinesthetic pedagogies to help students engage with the material. In the most recent iteration, data from course evaluations and student reflections indicate that students find the course applicable to their current and future endeavors working in interdisciplinary teams and valuable in their development. Students also generally find the pedagogy effective in helping them engage with the course content.

This research work-in-progress paper is part of a larger project that aims to develop personal values-based matching of role models, mentors, and coaches with the populations they serve. In previous work, we developed a process for generating profiles of role models that reflected the values of the population the role models were serving (underrepresented minority students in STEM). In short, we performed qualitative emergent thematic analysis on focus group data from underrepresented minorities in STEM to identify the qualities this population valued in role models. These qualities were used to develop a survey for potential role models (STEM alumni of the same institution) and the survey was subsequently used to develop profiles for the role model candidates. These profiles were well received by our original focus group participants. We have since run an analogous study at another institution using nearly the same survey to develop profiles of potential mentors. Here the mentors are upperclassmen, and the population they are serving includes all students in introductory STEM courses, though we remain particularly interested in underrepresented minorities. In this paper we present a simple textual comparison of the two sets of profiles as a first step in a comparative analysis of these profiles. The results will inform future work as we seek to further differentiate between role models, mentors, and coaches, and develop a robust method for increasing access to/successful matching of these important support structures and the students who need them.

Seeing themselves represented in the role models they aspire to, has been shown to be important to students' sense of belonging and success. Underrepresented college students in STEM fields are exposed to only a small set of role models. This set often consist of famous individuals with extraordinary stories (we call these outliers), and represent unfeasible paths to success for a large majority of these students. We aim to remedy this by identifying a set of role models who represent more feasible paths to success (we call these non-outliers) for many underrepresented students. We contend that, despite the less extraordinary success and stories of non-outliers, they share important qualities with outliers. We envision a “People Like Me” website based on profiles of this broader set of role models that can be used as a tool for recruitment and retention. Our current work is to (1) identify role model qualities from the perspective of students, (2) identify and create profiles of non-outlier role models based on these qualities, and (3) test if students are accepting of these non-outliers as potential role models. We have completed steps (1) and (2), and have found that non-outliers do exhibit the qualities our student sample pool seeks in role models.

In recent years, use of and interest in active learning techniques has been on the rise. In this special session, participants will be immersed in kinesthetic experiential learning activities that emphasize the “active” in active learning. Participants will be guided through a number of in-class activities and the underlying pedagogical philosophy. The session will end with open discussion and Q&A, allowing participants to consider how they may incorporate these activities into their own courses and explore opportunities to develop kinesthetic experiential learning activities to suit their own classroom needs.

With the arrival of the 2016 freshmen class on college campuses, the Engineer of 2020 is here. In the 13 years since the NAE's initial publication, many innovative engineering programs and initiatives have taken root across the United States. Many of these programs are rooted in experiential learning for technical mastery, and have demonstrated success in developing strong technical experts. The vision for the Engineer of 2020 goes beyond technical knowledge and deep into the human elements of engineering. We describe the use of innovative, kinesthetic activities to support the broader realization of the NAE vision. We draw from two unique courses, a communication course with a multidisciplinary engineering student enrollment, and a course for both engineering and non-engineering students that explores the implications of engineers as people. While different in context, the elective courses share a similar ideology and approach in emphasizing communication, human relationships, focus on the engineering process, opportunities for reflection, and growth mindset. Many of the activities described require students to play a role in a non-technical or abstract scenario which forms the basis for discussing engineering principles. The underlying philosophies of these non-technical courses also permeate our approaches to teaching technical material in core classes. Student feedback has been positive, with students recognizing the absence of this kind of learning in their broader education.

Human motion has been reported as having great relevance to various disease, disorder, injuries and emotional state. Therefore, motion assessment using inertial body sensor networks (BSNs) is gaining popularity as an outcome measure in clinical study and neuroscience research. The efficacy of motion assessment heavily relies on the accurate temporal clustering of human motion into actions on various time scales. However, two human factors in real-world deployments of inertial BSNs make such motion assessment challenging: mounting errors (where sensor displacement and orientation do not match what is assumed by processing algorithms) and insecure mounting (where sensors are loosely worn causing them to shake during operations). In order to enhance the robustness of human actions clustering from real-world BSN data, this work leverages dynamical systems modeling with the considerations of human factors. By proposing a computational body-model framework called the piecewise linear dynamical model (PLDM), we derive a robust method to segment time series data of inertial BSNs in real-world deployment with human factors into motion primitives and actions. We test the proposed method on three different inertial BSN datasets, extract actions on different temporal scales and recognize the actions into clusters. The experimental results demonstrate the effectiveness of our approach.

Auditeur is a general-purpose, energy-efficient, and context-aware acoustic event detection platform for smartphones. It enables app developers to have their app register for and get notified on a wide variety of acoustic events. Auditeur is backed by a cloud service to store user contributed sound clips and to generate an energy-efficient and context-aware classification plan for the phone. When an acoustic event type has been registered, the smartphone instantiates the necessary acoustic processing modules and wires them together to execute the plan. The phone then captures, processes, and classifies acoustic events locally and efficiently. Our analysis on user-contributed empirical data shows that Auditeur's energy-aware acoustic feature selection algorithm is capable of increasing the device lifetime by 33.4%, sacrificing less than 2% of the maximum achievable accuracy. We implement seven apps with Auditeur, and deploy them in real-world scenarios to demonstrate that Auditeur is versatile, 11.04% - 441.42% less power hungry, and 10.71% - 13.86% more accurate in detecting acoustic events, compared to state-of-the-art techniques. We present a user study to demonstrate that novice programmers can implement the core logic of interesting apps with Auditeur in less than 30 minutes, using only 15 - 20 lines of Java code.

Modeling and simulation play important roles in engineering research and design. These techniques are especially helpful in the early phases where limited detail is available about the design and where design changes are less costly. In addition, high-fidelity models can be employed at the later stages to complement testing. Models are also important research tools for understanding complex phenomena.

MusicalHeart is a biofeedback-based, context-aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, Septimu, which consists of a pair of sensor-equipped earphones that communicate to the smartphone via the audio jack. The Septimu platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user while listening to music. The physiological information and contextual information are then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% -- 85% correlated to the ground truth with an average error of 7.5 BPM. The accuracy of the person-specific, 3-class activity level detector is on average 96.8%, where these activity levels are separated based on their differing impacts on heart rate. We demonstrate the practicality of MusicalHeart by deploying it in two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and its quality of recommendation improves over time.