This research-to-practice paper investigated the development of Functional Analysis Learning Trajectories (FALT) within the undergraduate courses for engineers including Calculus, Differential Equations, Communications Theory, Control Systems and Electromagnetism. Focusing on the local instructional practices, manifestations of functional analysis ideas are tracked along these courses. Corresponding concept maps are collaboratively within and across courses to portray the relevant connections for engineering mathematics education. These trajectories help engineering and mathematics instructors to collectively design, implement, refine task sequences to improve students' preparedness for upper-level math-heavy courses in engineering, connecting mathematics to their disciplines. Here we utilized collaborative concept mapping as a research heuristics to build curricular innovations with functional analysis learning trajectories across courses, hierarchically arranging, integrating, and conceptually connecting instructional tasks. Through sequences of dual stance learning tasks, students are given opportunities to take multiple stances in a learning task from the perspectives of engineers and mathematical scientists. A higher stance on mathematics was supported to be developed by comparing and connecting alternative disciplinary perspectives and practices with the dual modeling tasks and reflecting on the cross-cutting ideas along the learning trajectory. Here we present the collaborative design and analysis of concept maps along functional analysis learning trajectories for undergraduates. This research builds an interdisciplinary scholarship of teaching/learning mathematics across disciplines among engineering and math faculty. This work helps foster reflection and collaboration on their teaching practices to design and implement instructional tasks to build coherent mathematical perspectives across disciplines. It exemplifies how to design a research-based practices to build cross-curricular innovations. Concept maps within courses are presented and discussed here to build mathematical connections and functional analysis learning trajectories for engineering and applied mathematics education.

This innovative practice full paper presents the implementation of whole system mapping for sustainable design thinking in engineering and non-engineering courses. Sustainable design has gained substantially more attention in both academia and industry as increasingly designers of products and services are expected to consider the longevity and sustainability of their products and services. Whole system mapping entails consideration of the resources, users, product life cycle, environment, and the interconnections of a product or service idea during design. Using the open educational resources, whole system mapping exercises were incorporated in senior engineering Project Management and senior Electrical and Computer Engineering Project Lab courses with engineering students, and a Gulf of Mexico Studies course for non-engineering students as a method to introduce the students to the idea of sustainable design. In this paper, we compared the students' utilization of whole system mapping to help them demonstrate sustainable design thinking in their proposed project ideas. This innovative practice assisted the students with determining resources needed to create their product or service, and whether more sustainable materials/ options could be chosen. The exercise assisted the students in considering the product life cycle from ideation to disuse of the product/service, and how they could incorporate reuse/ repurposing/recycling to extend the life of the product or its parts. The students considered the interactions based on the purpose of the product to incorporate useability in their design. Finally, the students also established connections to the users and where the product/service outputs or outcomes may end up, impacting their environment. Creative ideas were generated both in the interdisciplinary engineering and single-discipline engineering courses at two institutions. Non-engineering teams also brought fresh perspectives to the process with alternative project ideas in one course. This case study represents part of a wider study that includes additional cohorts to study whole system mapping and students' skills development in sustainable design.

Impervious surfaces increase surface runoff, leading to elevated flood risks and nonpoint source pollution. Predicting impervious surface ratios is essential for various urban management practices, ranging from drainage infrastructure design and water quality assessment to utility fee evaluation and flood risk mitigation. Traditionally, the information on impervious surface ratios is often estimated by city managers and engineers based on empirical values and assumptions. Recent studies have highlighted aerial image classification using machine learning and deep learning models, but such approaches are computationally intensive. We propose a graph neural network (GNN)-driven method, named GraphParcelNet, for advancing the quantification of parcel-level impervious surface ratios at the city scale. To our best knowledge, we are the first to transform land parcel datasets, consisting of vector data in geometric shapes (polygons), into a graph model that considers the spatial relationships between parcels. By utilizing a GNN-based approach, GraphParcelNet enhances the representation of spatial dependencies, resulting in more accurate and reliable predictions over traditional methods. Our experimental results demonstrate that GraphParcelNet outperforms previous methods, providing an accurate measurement for impervious surface ratios.

The availability of inexpensive sensor, communication, and computing devices continue to drive a rapid proliferation of Internet of Things (IoT) applications. This has resulted in an increase in the importance of the inclusion of IoT related content in education programs. A project underway at Texas A&M University-Kingsville seeks to support teaching IoT concepts to remote learners through the design and deployment of IoT learning toolkits. A series of exercises have been developed to assist remote learners in becoming familiar with the toolkits and to introduce basic IoT concepts. The learning kits are to be deployed in two senior level capstone project courses to teach IoT related concepts and encourage students to consider how IoT technology might be beneficial for and integrated into their project designs.

The COVID-19 pandemic resulted in changes in learning methods and attitudes in the STEM fields. The pandemic forced institutions of higher education to explore remote teaching/learning systems and tools some of which remain in use today. Such remote teaching/learning systems and tools present challenges for most STEM fields that involve hands-on experiences through laboratory or workshop environments that require access to devices, instruments and/or equipment. The engaged student learning through IoT project was introduced as a means to enable students to perform hands-on learning activities in their own time and space with minimal need for and dependence on university laboratory resources. In this paper, we describe engaged student learning through problem- and project-based learning (PBL) in IoT-based projects in senior capstone design courses at two Hispanic-Serving Institutions. Particular attention is paid to engagement and learning of historically under-represented student groups in capstone teams. Students in multidisciplinary teams borrowed IoT devices for the duration of their course work to discover and learn on their own with instructor guidance and developed course materials. The evaluation of the student performance and feedback indicate that students have benefited from access to IoT devices outside the laboratory environments by developing creative solutions to the undertaken capstone projects as well as problem-solving skills that are expected in the workforce. In this statistically small student sample size, there were no notable differences observed in the success of historically underrepresented students vs. all other students in the IoT-based projects in terms of engaged student learning. The IoT devices enabled the teams to undertake multidisciplinary and interdisciplinary projects that involved mechanical engineering, electrical engineering, mechanical engineering technology, and computer science students, each contributing to different objectives of the project to achieve the overall goal.

Fourier analysis learning trajectories are investigated in this full paper as a joint interdisciplinary construct for a scholarly collaboration among engineering and mathematics faculty. This is a dynamic and recursive construct for aligning, developing, and sharing research based innovative practices for engineering mathematics education. Towards building more coherence and transfer of learning between engineering and mathematics courses, these trajectories offer experimental practice templates for the interdisciplinary community of practice for engineering mathematics education. Conjectured learning trajectories for Fourier analysis thinking are here articulated and experimented in three courses - Trigonometry, Linear Algebra, and Signal Processing. Informed by the interdisciplinary perspectives from the team, these trajectories help to design instruction to support the complex learning of the mathematical, and engineering foundations for the advanced mathematical concepts and practices such as Fourier Analysis for engineers. The results highlight the impact of collaborative, interdisciplinary, and innovative practices within and across courses to purposefully build and refine instruction to foster coherence and transfer with learning trajectories across mathematics and engineering courses for engineering majors. This offers a transformative process towards an interdisciplinary engineering mathematics education. The valid assessment and measurement of complex learning outcomes along learning trajectories are discussed for engineering mathematics education, paving the pathway for our future research direction.