The SESAME Colloquium

Title: 

Role of Mental Imagery in STEM Education

Mohsen Janatpour

Abstract

Traditionally, STEM education in the USA has emphasized verbal education and shied away from the role of mental imagery in STEM thinking. However, scientists and mathematicians such as Einstein and Poincaré developed theories by emphasizing and developing their ability to think with images.

In recent years, I have incorporated thinking with images in communicating the course material to my students and have sought to help them develop their mental imagery. The results, while preliminary, have been measurably positive. In this presentation, after briefly introducing my Conjunctive Theory of Imagery, I will discuss some of the techniques I use to help them think with images.

About the speaker

Mohsen Janatpour is a math, physics, and astronomy professor at the College of San Mateo, and is the coordinator of CSM’s Astronomy Program. He has taught at the college for 134 semesters. 131 of them consecutively. While at the College of San Mateo, he has taught astronomy, aeronautics, chemistry, drafting, mathematics, and physics courses. Before that, he worked as a quality assurance engineer and mathematician at Coors and at Varian Associates. During his employment at Varian, he was involved with developing an interpolator for mapping the moon’s surface from the data gathered by Apollo 11.

As an artist and philosopher, Mohsen has lectured on the nature of mental imagery and human perception from 1995 to the present. In 2023, he developed two courses: Physical Science 125, “Mental Imagery in the Arts and Sciences,” and Physical Science 126, “Mental Imagery in Art and Science of Different Cultures,” based on his own theory of mental imagery. Both courses have been published as textbooks by Cognella Textbook Publishing Company.

Mohsen obtained his MS in mathematics from San Jose State University in 1969. Ten years later, he returned to the same institution and earned a second MS in physics. He began his teaching career at the College of San Mateo in 1979 while a Ph.D. candidate in biophysics at the University of California, Berkeley. This early start in academia set the stage for his subsequent 45-year teaching career, his art development, and the creation of his theory of mental imagery. (See https://www.mohsensart.com for examples of his artwork.)

Title: What is "good" code? Exploring CS students' knowledge of programming patterns

Anna Rafferty, Chair of Computer Science

Abstract

When learning to program, students are often most focused on how to write code that functions correctly—yet many different code structures can have the same functionality while differing in how easy they are to understand or extend. Thus, helping students to understand how to write well-structured code is an important learning objective. In this talk, I'll describe our research exploring students' understanding of particular programming patterns (code structures).

Using a survey-based approach, we demonstrate that students' understanding of programming patterns and anti-patterns is multi-faceted, and that, in some cases, relatively minor interventions can lead to large improvements in code structure. Over time, students do show improvement in their knowledge of programming patterns, but many students continue to prefer anti-patterns. We end by discussing implications for future research and potential classroom interventions. This talk describes joint work with Sara Nurollahian and Eliane Wiese.

About the speaker

Anna Rafferty is Associate Professor and Chair of Computer Science at Carleton College. She earned her PhD in computer science at UC Berkeley, working with Dr. Tom Griffiths.

Her work spans topics in machine learning, computational cognitive science, and education. She teaches a wide variety of CS courses, and the CS education work in this talk stems from combining her research interests with her observations of students in her classes.

Outdoor Environmental Education: Definition, Meaning, Research Challenges, And Educational Implications In The Slovenian Context

Gregor Torkar, Department of Biology, Chemistry and Home Economics; Chairholder of the UNESCO Chair on Teacher Education for Sustainable Development

Abstract

This talk will highlight Outdoor Environmental Education (OEE) and its potential in today’s globalized world. We will contextualize OEE in Slovenia, which has a long tradition of residential outdoor education programs. Slovenia is currently one of five countries in the world where residential outdoor education is part of formal education, with every student spending at least two weeks learning in natural settings during their elementary school years.

We present some key research findings and challenges in measuring the educational outcomes of OEE programs, as well as our views on the educational implications.

About the speaker

Gregor Torkar is Full Professor of Biology Didactics and holder of the UNESCO Chair on Teacher Education for Sustainable Development at the University of Ljubljana, Slovenia. He also chairs a program committee of the Centers for Outdoor Education in Slovenia and is a member of various national commissions for educational policy.

In his research and teaching he is active mainly in the field of biology and environmental education. His current research interests include ecology; biodiversity and evolutionary education; sustainability, environmental attitudes and behavior; outdoor education; ICT in science teaching and learning; and nature conservation.

AI Systems for Enhancing Educational Mentorship and Social Learning

Soya Park

Abstract

In this talk, I will introduce two AI-driven systems designed to address key challenges in educational mentorship and social learning, aligning with research on how technology can enhance development and collaboration.

The first project explores the concept of "Thinking assistants" aimed at fostering reflective thinking and self-directed learning. Thinking assistants act as an AI-powered virtual mentor that supports users during critical phases of their educational journey, such as the graduate school application process. It facilitates preparation and identity development by encouraging users to engage in deep reflection and brainstorm ideas.

The second project, Who2chat, addresses the social and academic challenges of peer collaboration and networking in virtual educational environments. This system supports students and researchers in overcoming social barriers by enabling them to create academic profiles, connect with peers who share similar research interests, and engage in meaningful conversations during virtual conferences and social hours. Who2chat enhances students' confidence and ability to form connections, which are critical for collaborative learning and professional development.

This talk shares implications of how to design AI systems that effectively support educational mentorship and foster social learning.

About the speaker

Soya Park is a postdoctoral researcher at Emory University working with Chinmay Kulkarni. She earned a PhD at MIT with David Karger. She is passionate about creating systems designed to empower scientists in the social aspect of collaboration. Her systems focus on "social preparation," a concept aimed at equipping users with the mental and cognitive readiness needed to navigate and thrive in complex collaborative settings.

Title: Scaling Expertise via Language Models: With Applications to Education

Rose Wang

Abstract

Access to expertise is essential for fostering effective interactions in many areas of society. For example, in education, experienced teachers teach students through effective interactions and train novices. However, access to expertise is often limited, undermining the training of novice educators and student outcomes.

While language models offer the promise of democratizing access, they often mimic surface-level patterns and lack the human touch to keep students engaged when learning becomes difficult. In this talk, I will present two works that address these challenges by embedding expert-like thinking into language models and empowering human novices to perform at expert level in real-time interactions.

First, I will discuss Bridge, an adaptation method that extracts latent expert reasoning to improve language models in complex interactions. Then, I will introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors in real time. In the first randomized controlled trial of a Human-AI system for live tutoring, Tutor CoPilot significantly improved the quality of learning interactions for 1,800 K-12 students and 900 tutors.

About the speaker

Rose E. Wang is a Computer Science PhD candidate at Stanford University. She develops machine learning and natural language processing methods to tackle challenges in real-world interactions, with a focus on Education.

Her work is deployed in industry and directly improves the education of under-served students through partnerships she has cultivated during her Ph.D., including Title I school districts and several education companies, impacting 200,000+ students, 1,700+ teachers, and 16,100+ tutors in millions of tutoring sessions across the U.S., UK, and India.

Her work is recognized by NSF Graduate Research Fellowship, CogSci Best Paper Award, NeurIPS Cooperative AI Best Paper Award, ICLR Oral, Rising Star in Data Science, Building Educational Applications Ambassador Paper Award, and the Learning Engineering Tools Competition Award.

Testing the role of executive function in fraction comparisons for third graders

Dana Miller-Cotto

Abstract

One topic that is especially hard for children to learn is fractions. In many cases, difficulties with fractions are linked to the whole number bias, or a tendency to focus on whole number components of fractions rather than thinking of a fraction as a single number.

I will present on two studies stemming from predictions about whole number bias in children: (a) whole number knowledge interferes with understanding fractions; and (b) some children struggle to inhibit their whole number knowledge, potentially due to challenges with executive function ability, a set of cognitive processes that enable us to store, manipulate information, inhibit distractions, and flexibly shift between tasks or goals. Here, I compare children (N = 96) and adults’ (N = 50) performance on fraction comparison tasks where fractions were either congruent (i.e., numerators with bigger numbers equals bigger fractions) or incongruent (i.e., numerators with smaller numbers are the bigger fraction) with whole number knowledge.

I investigated whether whole number knowledge predicts performance on fraction tasks and executive function moderated this relationship. The study aimed to clarify whether whole number bias impairs fraction understanding and if executive function explained this interference on incongruent trials, with potential educational implications for improving math instruction through material presentation.

About the speaker

Dana Miller-Cotto is an Assistant Professor in the School of Education at the University of California, Berkeley. She is broadly interested in educational inequity in the U.S., with a particular eye toward understanding the measurement and interpretation of minoritized students’ performance on assessments, 2) the co-development and measurement of cognitive processes and math skills, and 3) using cognitive science to improve instructional materials for students who struggle.

She earned her Ph.D. in Educational Psychology from Temple University and her Bachelors in Psychology from the City University of New York (CUNY). She conducts this research through various methods, including secondary longitudinal analyses, experimental designs, and meta-analyses.

Title: TBA

Laura Hirshfield

Department of Chemical and Biomolecular Engineering, University of California, Berkeley

Abstract

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About the speaker

(Laura Hirshfield ...)

Generative AI as a Disruptive Technology for Educational Systems: A Cognitive, Pedagogical, and Curricular Analysis

Orit Hazzan

Department of Education in Science & Technology, Technion – Israel Institute of Technology

Abstract

Disruptive technologies are groundbreaking innovations that fundamentally transform existing markets, create new economic opportunities, and render previous technologies or business models obsolete by offering more efficient, cost-effective, and user-friendly solutions.

In my talk I illustrate, from cognitive, pedagogical, and curricular perspectives, why Generative AI (GenAI) can be viewed as a disruptive technology for educational systems worldwide. To support this claim, we revisit cognitive, pedagogical, and curricular models and theories, and explore their implementation and implications regarding GenAI in educational settings. Among the pedagogical and cognitive theories and models we explore are constructivism and constructionism, motivation theories, Bloom’s taxonomy, didactic transposition, the knowledge-skills-attitudes (KSA) model, and the 21st century skills set.

One conclusion of the above claim is that there is an urgent need to rethink and apply new educational formats and models so as to remain relevant in the current transforming era that we are all witnessing, but whose future is still unclear and cannot be predicted.

One important insight that I aim for my audience to take away from this talk is that the education community should not perceive GenAI as a threat, but rather as an opportunity. That is, by enabling to increase the level of abstraction and complexity of the tasks we assign students and of the skills and competencies that we seek to impart, GenAI makes it possible to achieve pedagogical goals and impart the values that we have always sought to impart but could not, mainly but not only due to the prevailing organizational culture of education systems whose roots were planted in the first industrial revolution, which took place two hundred years ago. As a disruptive technology, GenAI will actually force education systems to adapt to, and skip directly to, the 5th industrial revolution, which is taking place now.

About the speaker

Professor Orit Hazzan has been a faculty member of the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering, and data science education. Therein, she researches cognitive and social processes on the individual, team, and organizational levels, in all kinds of organizations. Her attention has recently turned to the assimilation of GenAI into the education system and academia.

Professor Hazzan has published about 150 papers in professional refereed journals and conference proceedings, and eight books. Her co-authored book Guide to Teaching Computer Science was published by Springer in three editions (2011, 2015, and 2020); the fourth edition is schedule for publication in 2025. Her co-authored book Guide to Teaching Data Science: An Interdisciplinary Approach was published by Springer in 2023, just before the emergence of GenAI. Her new co-authored book Inevitability of AI in Education: Futuristic Perspectives for Education for the Next Two Decades was published by Springer in 2024.

In addition to her research, Professor Hazzan has held several administrative roles at the Technion. From 2011 to 2015, she served as her faculty’s Dean, and from 2017 to 2019 she served as Technion Dean of Undergraduate Studies. Recently (in November 2023), she began heading the Education in Emergency Forum, assembled by the Samuel Neaman Institute for National Policy Research.

Additional details about Hazzan’s endeavours can be found on her professional homepage.