Engaging undergraduate students in research is a critical responsibility of institutions of higher learning; a respon-sibility which has innumerable positive impacts on students and faculty alike. To effectively fulfill this responsibility, it is critical for experienced researchers to consider ways to engage under-graduate students in research fields. One established method of increasing undergraduate engagement is by pairing research mentors with undergraduate mentees. However, this approach requires a substantial time commitment from both the mentor and mentee and often impacts only one student at a time. A review of current literature suggests that to combat these pitfalls, experienced researchers could foster a cohort of undergraduate students in a research setting, as this may also effectively increase undergraduate's involvement in research. To further examine both of the aforementioned approaches, this work outlines a framework comprised of a model of mentorship and a set of adoptable practices for experienced research mentors to use when mentoring undergraduates. Additionally, this work examines a preliminary case study in which a graduate student employed this framework in the setting of an engineering research lab. In this instance, a graduate student acts as a mentor to a cohort of three undergraduate student mentees. To conduct preliminary assessment, autoethnographic reflections were collected from participants to be coded and analyzed for themes. In addition, any resources associated with implementation of the mentorship framework was also saved for illustration of the proposed prac-tices associated with the framework. This assessment has yielded promising results, as all three undergraduate student mentees have remained in the lab as active and effective team members. Furthermore, all three students felt that this experience positively impacted their personal and professional growth. In analyzing student reflections, these undergraduate students have reported an increased level of confidence in their technical abilities and a recognition of their importance to the community of engineering research.

Our full paper discusses how the COVID-19 pandemic forced many K-12 schools and educational programs to change course from in-person learning to hybrid and remote learning, requiring students and teachers to adapt rapidly. Based on a project-based learning (PBL) design, our university education outreach program brings air quality sensor technology to rural high school students, allowing them to ask and answer their own research questions. Throughout their participation, K-12 students learn about air quality while designing and carrying out an experiment, and have the opportunity to present their findings at a symposium. During the 2018-2019 school year, learning activities took place mostly in person. For 2019-2020, the program began in person, then shifted to remote learning in March 2020 due to COVID-19 related closures. For 2020-2021, the program took place entirely online. We refer to these years as “in-person”, “hybrid*”, and “remote”, respectively. Qualitative and quantitative data were collected from high school students, teachers, and college mentors each year, including interviews, surveys, and analysis of public artifacts. While learning opportunities differed from year to year, learning outcomes remained relatively stable. While the rapid shift to remote learning during the hybrid* year challenged teachers and students alike, the program ran more smoothly during the fully remote year once everyone was adjusted to the new format. However, students' engagement, excitement, and confidence in engineering was weakest during the remote year. Implications include what combination of educational modes are worth continuing post-pandemic, affording students the best learning opportunities to produce favorable engineering outcomes.

Air pollution is a major global environmental health threat, in particular for people who live or work near air pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. In this work, we explore the use of mobile sensing data to detect pollution hotspots. We propose a two-step approach to detect hotspots from unevenly sampled mobile sensing data. To contextualize the detected hotspots and discover potential pollution source characteristics, we explore a variety of cross-domain urban data and extract features from them for hotspot inference. Evaluation results using real-world mobile sensing air quality data as well as cross-domain urban data demonstrate the effectiveness of our approach in detecting and inferring pollution hotspots.

Accurate next-day air quality prediction is essential to enable warning and prevention measures for cities and individuals to cope with potential air pollution, such as vehicle restriction, factory shutdown, and limiting outdoor activities. The problem is challenging because air quality is affected by a diverse set of complex factors. There has been prior work on short-term (e.g., next 6 hours) prediction, however, there is limited research on modeling local weather influences or fusing heterogeneous data for next-day air quality prediction. This paper tackles this problem through three key contributions: (1) we leverage multi-source data, especially high-frequency grid-based weather data, to model air pollutant dynamics at station-level; (2) we add convolution operators on grid weather data to capture the impacts of various weather parameters on air pollutant variations; and (3) we automatically group (cross-domain) features based on their correlations, and propose multi-group Encoder-Decoder networks (MGED-Net) to effectively fuse multiple feature groups for next-day air quality prediction. The experiments with real-world data demonstrate the improved prediction performance of MGED-Net over state-of-the-art solutions (4.2% to 9.6% improvement in MAE and 9.2% to 16.4% improvement in RMSE).

High-level university technology can enhance student learning with hands-on experience, but the complexity of these tools can also create challenges for users. This study investigates the benefits and challenges of laboratory tools in high school settings through the lens of a project-based learning model. Participants in the investigation are stakeholders in a university outreach program known as AQ-IQ, which is built around teaching rural high school students how to use low-cost air quality monitors. In the fourth year of the AQ-IQ program, two of the major goals were to improve the reliability of the monitor data collection and to make the data analysis more accessible for K-12 students and teachers. This study found that benefits of high school students using university technology included increased excitement and student engagement due to the hands-on and real-world nature of the technology. Two of the challenges in using the technology included power supply issues and users not following operation instructions regarding how to warm up the monitors at the beginning of their experiments. Recent improvements to the monitor and data analysis tools succeeded in reducing data losses and making it easier for students and teachers to view and interpret their results. We have found that making continual efforts to improve technology based on user feedback and ensuring that resources are adequate to address user challenges increases the success of integration of university tools into high school classrooms.

The availability of low-cost sensors and environmental monitoring technologies is growing rapidly. While researchers are making use of these technologies and validating their results, there is also enormous potential for use in education and outreach. Through the Air Quality Inquiry Project (AQIQ), we are bringing next-generation air quality monitors into the K-12 classroom and developing a curriculum with necessary resources to support student-driven research projects. This paper will represent year three of the AQIQ project. During year three of the project, one focus has been on the revision and publication of the curriculum. The AQIQ curriculum consists of five modules, which range from fundamental concepts related to air quality monitoring and management to a hands-on project using the monitor for student driven air quality monitoring projects in the local community. The curriculum culminates in an end of semester symposium in which students present their projects to the community. The modules are currently being submitted to the TeachEngineering Digital Library for further dissemination. Another large thrust for year three of the project has been the development of an undergraduate class to train engineering students to teach the curriculum in a broader number of K-12 classrooms. In this pilot offering of the year-long course, four pairs of engineering students travel monthly to eight science classrooms in rural communities on the western slope of the Rocky Mountains and on the high plains east of Denver where natural gas development is occurring. The course meets weekly to teach air quality monitoring concepts, process students' visits and plan future trips. The course is planned to become a regular course offering in the engineering curriculum at CU in year four. This paper will discuss the developing curriculum, the specifics of the pilot course and the results of our assessments implemented thus far.

The availability of low-cost sensors and environmental monitoring technologies is growing rapidly. While researchers are making use of these technologies and validating their results, there is also enormous potential for their use in education and outreach. Through the North Fork Valley (NFV) Project, we are bringing next-generation air quality monitors into the classroom and developing necessary resources to support student-driven research projects. This project is a collaboration between the University of Colorado at Boulder, community partners, and educational partners (Delta County School District 50, and St Vrain Valley School District) and is funded by the NSF-funded AirWaterGas (AWG) Sustainability Research Network Education and Outreach (E&O) efforts. During year two, we are formalizing the curriculum, implementing it in multiple school districts, and conducting assessment activities. This paper will discuss the curriculum, the results of our assessments thus far, and our vision for the project. The work completed during year two will help ensure we are building a sustainable project. Additionally, we intend to make the curriculum public with hopes that other research groups working with low-cost air quality sensors can utilize our materials and possibly our model for their own education and outreach.

Indoor air quality is important. It influences human productivity and health. Personal pollution exposure can be measured using stationary or mobile sensor networks, but each of these approaches has drawbacks. Stationary sensor network accuracy suffers because it is difficult to place a sensor in every location people might visit. In mobile sensor networks, accuracy and drift resistance are generally sacrificed for the sake of mobility and economy. We propose a hybrid sensor network architecture, which contains both stationary sensors (for accurate readings and calibration) and mobile sensors (for coverage). Our technique uses indoor pollutant concentration prediction models to determine the structure of the hybrid sensor network. In this work, we have (1) developed a predictive model for pollutant concentration that minimizes prediction error; (2) developed algorithms for hybrid sensor network construction; and (3) deployed a sensor network to gather data on the airflow in a building, which are later used to evaluate the prediction model and hybrid sensor network synthesis algorithm. Our modeling technique reduces sensor network error by 40.4% on average relative to a technique that does not explicitly consider the inaccuracies of individual sensors. Our hybrid sensor network synthesis technique improves personal exposure measurement accuracy by 35.8% on average compared with a stationary sensor network architecture.

People spend approximately 70% of their time indoors. Understanding the indoor environments is therefore important for a wide range of emerging mobile personal and social applications. Knowledge of indoor floorplans is often required by these applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor.

People spend the majority of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or mobile phone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Existing room localization methods, however, require labor-intensive manual annotation of individual rooms.

Mobile sensing systems carried by individuals or machines make it possible to measure position- and time-dependent environmental conditions, such as air quality and radiation. The low-cost, miniature sensors commonly used in these systems are prone to measurement drift, requiring occasional re-calibration to provide accurate data. Requiring end users to periodically do manual calibration work would make many mobile sensing systems impractical. We therefore argue for the use of collaborative, automatic calibration among nearby mobile sensors, and provide solutions to the drift estimation and placement problems posed by such a system.

Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO2 sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via user study. Detailed evaluation results demonstrate that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency.

Most people spend more than 90% of their time indoors. Indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This demo introduces MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method; (2) an air exchange rate based IAQ sensing method; and (3) a zone-based proximity detection method for collaborative sensing.