JMM Workshops

 

These workshops are open to the registrants of the Joint Mathematics Meetings

JMM Workshop: Advances in Neural Operators and Uncertainty Quantification for Scientific Modeling

Organized by Panagiotis Stinis and Amanda Howard, Pacific Northwest National Laboratory; Friday, 3:00 p.m.-4:30 p.m. Room 4C-2, SCC, Arch at 705 Pike

Modeling complex systems typically involve the evaluation of integro-differential operators that map fields (e.g. initial and boundary data, parameter fields) into solution fields. These operators are traditionally approximated using discretization approaches such as finite difference or finite elements. An emerging alternative approach is to approximate the operators with neural operators, that is, with deep-learning based models. Once trained, neural operators are much faster than traditional computational models, and they are advantageous in different contexts, e.g., in inverse problems, uncertainty quantification and in high-dimensional problems. In addition, neural operators can naturally assimilate observations and experimental data and therefore can potentially achieve higher fidelity than traditional models. Several neural operator architectures have been proposed in the literature, e. g., Deep Operator Networks, Fourier Neural operators, Kernel Graph Operators, featuring different strengths and weaknesses. This workshop focuses on both theoretical and computational aspects of neural operator modeling, covering the design of neural operators, their training, and uncertainty quantification. Topics of interest include, but are not limited to: training methods using multi-modal or multi-fidelity data, training approaches that are parallel and scalable, strategies to enforce physical constraints or property preservation, design of hybrid models combining traditional simulation codes with neural operators, accuracy and epistemic uncertainty of neural operators. Presenters/Panelists include Bamdad Hosseini, University of Washington, Somdatta Goswami, John Hopkins University, and Justin Dong, Lawrence Livermore National Laboratory

JMM Workshop: Artificial Intelligence and Game Theory: An Intersection of Theory and Applications

Organized by Mark S. Lovett, Dartmouth College; Thursday, 1:00 p.m.-2:30 p.m.,Room 4C-2, SCC, Arch at 705 Pike

The recent surge of AI has fostered a vibrant connection with mathematics, particularly through game theory, bringing together economics, computer science, and engineering. Through the mathematical framework of game theory, researchers can gain deeper insights into the complex behaviors of AI systems. Our workshop will delve into this exciting intersection, exploring how game theory can illuminate the societal impacts of AI, the learning processes of agents in multi-agent reinforcement learning (MARL), and other forms of agent interaction. We will explore how game theory has been applied to analyze convergence, equilibria, and optimal strategies in complex environments with multiple learning agents. We will examine how game-theoretic tools have been used to model and understand the potential social and economic ramifications of AI advancements. Our aim is to further narrow the gap between theoretical foundations and practical AI challenges and foster a collaborative environment for mathematicians and AI researchers to explore the potential of game theory shaping the future of AI.

JMM Workshop: Developing Online Mathematics Courses: Strategies to Consider

Organized by Sharmila Sivalingam, Maryville University of St. Louis, and Pamela Bryan Williams, Chief Strategist, Learning Design and Development, Maryville University, School of Adult & Online Education; Wednesday, 1:00 p.m..–2:30 p.m., Room 4C-2, Seattle Convention Center Arch at 705 Pike

This workshop will focus on key factors that must be considered while designing and developing an online course that is student-oriented, engaging and accessible. As Artificial Intelligence is becoming a part of student learning, adaptive courseware(s) provide a multi-prong method for learning, assessment and teaching in online settings. Presenters will highlight key opportunities to support online mathematics learning through existing AI-based adaptive courseware.

Williams van Rooij and Zirkle (2016) noted that the success of online learning is determined by various factors like universal design and accessibility, individual and institutional factors. Martin et.al (2019) made their recommendations based on the perspectives of award-winning online faculty, which included using a variety of assessments, timely response and feedback, faculty presence, and periodic communication. This workshop will focus on the key factors that must be considered while designing and developing an online course, using adaptive courseware(s) and integrating AI learning support tools. Offering hybrid and online courses are becoming an essential part of higher education settings. As simple as the idea seems to be, a successful online math course has different components from course development, delivery, adaptive learning, engagement, assessment, faculty presence and student satisfaction. Designing and developing an online course could be daunting and have a multitude of sides to it. Unlike the traditional face-to-face courses, online course development needs a team of people to collaborate to bring the course to life. This workshop focuses on developing the content of the course, choosing adaptive learning tools, understanding what is meaningful for students, integrating online apps that could be used in assignments or discussion boards, and designing the course with an accessibility first mindset. As Artificial Intelligence is becoming a part of the everyday learning experience, introducing adaptive courseware and leveraging sound pedagogical practices to incorporate AI into the classroom, we can provide the opportunities for students to learn in a self-paced mode and receive real-time feedback and explanatory support of the topic-based learning. Adaptive courseware has been used for more than a decade. As Artificial Intelligence is becoming a part in student learning, adaptive courseware(s) provide a multi-prong method, where students use the courseware as a source of knowledge and learn at their pace and faculty could use it to facilitate the learning and for assessments. In this session, the presenters will highlight key opportunities to support mathematics learning through existing adaptive courseware, provide opportunities to explore adaptive courseware, share insight into how the learning design and faculty partnership has been essential to the student experience, and brainstorm how courseware, AI enhanced learning support, and active learning enables greater levels of learning success. Martin, F., Ritzhaupt, A., Kumar, S., & Budhrani, K. (2019). Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation. The Internet and Higher Education, 42, 34-43. Rooij, S. W., & Zirkle, K. (2016). Balancing pedagogy, student readiness and accessibility: A case study in collaborative online course development. The Internet and Higher Education, 28, 1-7.

JMM Workshop: Engage, Enhance, Educate: Exploring AI-Driven Teaching Strategies and Tools for Tomorrow's Math Classrooms

Organized by Ruby Ellis, North Carolina State University, Jerome Zegaigbe Amedu, University of New Hampshire, and Kenya Lawrence, North Carolina State University;Thursday, 10:30 a.m.–12:00 p.m., Room 4C-2, SCC Arch at 705 Pike

In this dynamic and interactive workshop, we will share strategies and tools for incorporating ChatGPT and related AI tools to help participants unlock the transformative power of AI in their classrooms. Some of these include strategies for supporting active learning using ChatGPT, identifying AI-generated essays, empowering preservice teachers with skills to critique AI-generated results (e.g, lesson plans), and tips for encouraging students' responsible use of AI tools.

There has been an upsurge of interest in the use of ChatGPT and related AI tools for teaching and learning recently. However, research on the potential impact and best practices for incorporating AI tools into math instruction is still in a nascent stage. In this dynamic and interactive workshop, we will share strategies and tools for incorporating ChatGPT and related AI tools to help participants unlock the transformative power of AI in their classrooms. Some of the strategies we will share are based on our research on math educators’ experiences using AI tools in the classroom as well as our experience integrating same in our math methods courses. For example, educators will learn about strategies for supporting active learning using ChatGPT, identifying AI-generated essays, empowering preservice teachers with skills to critique AI-generated results (e.g lesson plans), encouraging students’ responsible use of ChatGPT (and related AI tools), and so on.

The workshop is designed exclusively for math teachers and math teacher educators. Participants will engage directly with the technology, gain practical experience and discover ethical, effective ways to harness AI for math instruction. We will kick things off with cutting-edge research on blending AI with proven pedagogical approaches. Then, participants will step into their students' shoes during experiential learning tasks, using ChatGPT in real-time to simulate classroom interactions. This approach ensures participants not only learn about AI but also feel its impact, preparing them to anticipate and meet students' needs. By the end of the workshop, participants will walk away with a comprehensive toolkit of AI-enhanced teaching techniques, fresh perspectives on active learning, and a newfound confidence in their ability to create an engaging, interactive educational environment.

JMM Workshop: Entrepreneurial Mindset in Teaching Mathematics

Organized by Wojciech K. Kossek, University of Denver; Friday, 10:30 a.m..–12:00 p.m., Room 4C-2, SCC, Arch at 705 Pike

When teaching mathematics, one may want to seek to adopt pedagogical strategies that can help students cultivate their ability to solve complex, open-ended problems. Faculty who are part of the Kern Entrepreneurial Engineering Network (KEEN) are adopting pedagogical strategies identified as "entrepreneurial mindset."

Entrepreneurial Mindset Learning (EML) encourages faculty to approach student problem solving with two distinct aspects. The first aspect of EML is to foster the students' curiosity in a solution, allow the students to seek out connections within or across learned concepts, and to allow the students to create value for stakeholders. The second aspect of EML is for the student to delve more deeply into the impact, opportunity, and design of their proposed solutions.

The concepts of EML are not material that "cuts into" course content and, thus, requires the removal of existing course elements due to time concerns. Rather, EML is a structure through which material is presented and assessed. EML implementation can be scoped for individual activities or entire courses. In that way, a well-executed EML exercise can (and, perhaps, should) exist without the student ever being aware of the term "Entrepreneurially Minded Learning."

The workshop will begin with an overview of EML and a survey of the breadth of support materials that exist. The workshop participants will be introduced to the network of more than 60 institutions - large and small - actively engaging students with EML. EML resources now include more than 3000 documented problem-solving proposals, presentations, and assessment approaches - all of which will be accessible to workshop attendees. EML can currently be considered a conversation between more than 5000 educators, and the workshop attendees will learn how to be a part of that conversation. Specific examples of EML in mathematics will include:

  • Making polynomials to order: Taylor polynomials and the idea of approximating more complicated functions by simpler ones. This leads to the idea of defining new functions using infinite series, which in turn opens the door to a whole new world of applications.
  • Orthogonal projections, Fourier series and applications of harmonic analysis.
  • Un-coding the mystery of One-to-One functions and Inverse functions. Encryption by matrix multiplication.
  • Usury and Payday Industry. Discovering the exponential function to the base e.

Each attendee will have the opportunity to consider the application of EML in their own classrooms in a group activity format. Applications ranging from micro-EML activities to entire course implementations will be discussed. The EML models shared during the workshop can be a starting point for attendees, who can adapt these ideas to their own teaching practice and to their students’ learning styles. Presenters are Wojciech K. Kossek, University of Denver and Stephanie Salomone, University of Portland.

JMM Workshop: High School Mathematics Reimagined

Organized by Latrenda Knighten, National Council of Teachers of Mathematics, Saturday 3:00 p.m.–4:30 p.m., Room 4C-2, SCC, Arch at 705 Pike.

In this session, participants will examine a framework based on the newest publication from the National Council of Teachers of Mathematics (NCTM) for reimagining high school mathematics to make it more relevant, useful, and engaging for students. The current high school mathematics system leaves many students with negative beliefs about their mathematical abilities and the value of mathematics. We need to shift from covering abstract content to cultivating students’ affection and appreciation for mathematics. Participants will be introduced to the framework which consists of five focal points that should guide the organization and instruction of high school mathematics. These focal points highlight the connections and utility of mathematical ideas and support students’ development of mathematical and statistical practices. Additional topics highlighted include the role of interest-driven pathways for high school mathematics and the role of technology and modeling in high school mathematics.

JMM Workshop: Reimagining Exams to Focus on Meaningful Learning and Disrupt the Dominant Grade-Focused Culture

Organized by Hyman Bass and Deborah Loewenberg Ball, University of Michigan; Saturday,1:00 p.m.–2:30 p.m.; 4C-2, SCC, Arch at 705 Pike.

Most math courses use homework, quizzes, and exams to monitor students' learning. Preoccupied with earning good -- or just passing -- grades, students' engagement often becomes distorted. Grades also function significantly to bar advancement in mathematics and other fields. This workshop will engage participants in exploring specific alternatives to modal approaches to assessment and grading that recenter mathematics teaching on meaningful mathematics learning.

Normative practice in undergraduate mathematics courses are typically characterized by instructor didactic lectures, homework practice, and exams that focus on correctness. Students leverage time with instructors in different ways, with some students seeking attention for individual gain and others shying away from close interaction with instructors. The signals about what mathematics is, what it is to be “good” at learning and doing it, and who is “smart” at math reproduce exclusionary patterns that often alienate, discourage, and push students out. Although mathematics instructors say that they aim to engage students in understanding concepts, developing skills and fluency, solving problems, and reasoning, the culture of most mathematics courses impedes these aspirations. Departmental norms reinforce students’ and instructors’ focus on grades and lead to concerns about the “security” of exams and formulas for how final grades are calculated. Seeking to confront and disrupt these patterns, this workshop will engage participants in exploring, examining, and refining a set of principles and structures for assessment that shift the focus from policing students in ways that distort their learning to supporting authentic mathematical engagement and practice. The workshop will be structured as follows:

  1. (15 min.) Introductions and overview and framing and evidence of the problem of the grade-focused culture and routine exams and the impact of these on students’ experiences and success.
  2. (15 min.) Analyzing normative practice: Examination and discussion of typical exams and grading schemes to identify underlying theories of learning and conceptions of mathematical competence reflected in these common artifacts.
  3. (15 min.) Alternative principles and structures for assessment: Presentation of a set of materials that offer a different representation of learning and conception of mathematical competence.
  4. (15 min.) Hands-on exploration of a set of alternative assessments: Participants will engage in actual assessment items and discuss them in small groups.
  5. (10 min.) Evaluation of student performance on the alternative assessments: Participants will apply an alternative rubric to appraise a small sample of student work on the assessment items they have tried themselves in (d).
  6. (20 min.) Challenges, opportunities, next steps: Participants will discuss the potential of disrupting the grade-oriented normative culture and next steps to advance this work. They will consider, comment on, and raise questions about ways to promote a more equitable and mathematically worthy learning experiences for students that holds promise for reorienting both students and instructors’ focus in courses, and that would entail in their departments.