Upper Peninsula Teaching & Learning Conference - 2026

Engineering curricula are often structured around a sequence of core theory courses complemented by practice-based classes intended to develop professional skills, design intuition, and applied problem-solving. In practice, however, these two components are frequently experienced by students as disconnected or even competing elements of their education. This disconnect can limit students’ ability to transfer theoretical knowledge to real-world contexts and reduces the perceived value of both course types.


In response, the author describes a personal, instructor-driven approach to intentionally linking practice-based courses with core curriculum content, specifically within dynamics and vibrations. Practice classes are structured to allow for investigation and tinkering in mechanical engineering. Laboratory exercises, measurement activities, and semester long projects are deliberately aligned with topics currently being covered in dynamics, mechanics, and fluids classes, allowing students to discover physics phenomenon in some cases, and apply analytical tools in physical contexts in others. At the same time, examples in practice-based courses are carried back into core classes, where they are used to motivate theoretical development and highlight modeling assumptions.


Evidence from student feedback, course artifacts, and performance on integrative assessments suggests that this alignment improves conceptual understanding, increases student confidence in applying theory, and strengthens connections between analysis and engineering judgment. Students report a clearer sense of purpose in both course types and greater engagement when theoretical content is immediately contextualized through hands-on application. The results highlight the value of instructor-led curricular integration and offer practical, scalable methods for faculty seeking to better connect theory and practice within engineering education.

In my Intermediate Macroeconomic Theory course at Michigan Technological University, I have experimented with a series of applied, historically grounded projects designed to help students contextualize the development of macroeconomic models. This initiative began after I discovered that a majority of students could not identify the decade of the Great Depression, revealing a significant gap in historical-economic literacy that limited their ability to fully grasp the evolution of macroeconomic thought.
In response, I first implemented a half-semester project in which students created a detailed persona of an individual living during the Great Depression. While this immersive assignment significantly deepened students’ understanding of that period and increased engagement beyond expectations, it also narrowed their focus to a single event.
The following year, I redesigned the assignment into a group-based virtual timeline project spanning major economic events from 1900 to the present. This broadened students’ historical awareness but reduced the depth of contextual understanding necessary to fully appreciate the intellectual environment in which foundational macroeconomic models were developed.
This presentation will explore the design process behind these projects, assess the tradeoffs between breadth and depth in applied historical learning, and introduce a new hybrid model currently under development. 

Assessment requirements in universities have become nearly universal across higher education institutions. Evaluation efforts typically focus on documenting student and program learning outcomes. Despite being framed as tools to enhance educational quality, decades of assessment efforts strongly suggest that this goal is not being effectively achieved. Despite nearly 40 years of higher education assessment activity, even assessment professionals have conceded that there is minimal evidence demonstrating that assessment has led to improved student learning outcomes. While substantial amounts of assessment data have been gathered and archived, critics suggest that many of these findings are of questionable quality and limited usefulness. Some faculty critics have concluded that the time devoted to the end-of-semester exercise of organizing, documenting, and reporting student learning outcomes occurs at the expense of more meaningful student-centered advising activities,   course development, improved instructional strategies, and scholarly work.Assessment issues reflect broader shifts in higher education, notably corporatization and increased administrative oversight. Assessment professionals have often implemented behavioral and educational measurement methods without adhering to key principles, leading to ongoing data quality issues. As a result, current assessment measures may lack reliability and validity, reducing their credibility and utility Meanwhile, higher education assessment has become a distinct profession with its own institutions. The assessment community rarely acknowledges research from the Scholarship of Teaching and Learning. Alternative approaches to learning outcomes assessment, such as student-centered qualitative methods and formative assessment, will also be presented and discussed.

Are your students engaged, thinking critically, and learning with each other? In this interactive workshop, participants will experience Process Oriented Guided Inquiry Learning (POGIL) from the learner’s perspective and discover how this approach transforms the classroom into a space for active, student-centered learning. POGIL is built around structured teamwork, defined roles, and guided inquiry questions that help students develop both conceptual understanding and essential process skills such as communication, problem solving, and self-assessment. During the session, attendees will participate in a sample POGIL activity, reflect on the underlying pedagogy, and discuss practical strategies for integrating POGIL into their own teaching.

The use of artificial intelligence in academia has accelerated, alongside growing concerns that it may contribute to students’ cognitive decline and the erosion of critical thinking and deep learning. Proponents of AI, particularly university administrators, argue that its efficient, effective, and ethical use can enhance teaching and learning while improving students’ employability. When responsibly applied, AI can support the learning of complexity—a domain that is challenging to teach because it involves higher-order thinking, dynamic interactions, and several interconnected parts. Employing AI as a teaching tool and Socratic tutor, rather than as “tell pedagogy,” may foster deeper student understanding of complexity. Drawing on AI-enhanced modeling platforms (SD-A and CoModel.io), this presentation demonstrates how students and faculty can use AI responsibly to understand complex systems and identify best practices for AI-supported teaching on complexity.

As we reach the end of the term, we dread the student e-mails that read "Is there any way I can get just a few more points to raise my grade?  I'm so close..."   Planning for these inevitable requests provides opportunities to have students learn just a bit more before the semester ends.  The presenter will explore two strategies used for finalizing end-of-term grade assignment, and will encourage participants to share their own ideas about how to navigate and fairly manage students at grade boundaries.

Students often come to college without understanding how to approach learning effectively. This study examined the impact of structured self-analysis and reflection on 15 undergraduate students' metacognition, grit, learning styles, self-efficacy, and motivation. At the beginning of the semester, students completed a researcher-developed pre-assessment documenting their integrated understanding of how these constructs inform studying habits. For each construct, students participated in two instructional sessions covering theoretical foundations and practical applications, completed established self-assessments (Duckworth's Grit Scale, Bandura's Self-Efficacy Scale, Kolb's Learning Style Inventory), and wrote structured reflections analyzing their results. After seven weeks, students completed the post-assessment. A paired samples t-test revealed significant improvement in students' integrated understanding (t(14) = -2.47, p = .027). Thematic analysis showed students translated insights into action: they created individualized learning goals, with self-regulation and grit emerging as the most frequently identified areas for development. These findings suggest that structured self-analysis enables students to move from theoretical understanding to actionable learning strategies.

Integrating AI Literacy into the Composition Classroom: Successes, Challenges, and Insights


To address the rising use of generative AI by students to write their assignments in English classes, I decided to try something different. Instead of banning AI use – an approach which I have found impractical and ineffective - I integrated activities into the course work to help students better understand the benefits and drawbacks of using AI: when it is a helpful tool, and when it prevents them from gaining critical thinking and writing skills. Together, we developed a class AI policy for the semester.


In this workshop I will discuss the successes and challenges of this approach and the insights I gained. Student perspectives on the project will also be included. You will have the opportunity to engage in the activities and consider how they could be used or adapted for your own discipline and classroom.