CCSEs juleseminar 2025

Vi har ofte hatt juleseminaret vårt på selve Luciadagen eller en av dagene rett før, og derfor benytter vi anledningen til å servere hjemmebakte lussekatter også i år. I år med safran fra Nepal. 

Data is everywhere. Growing up in a world saturated with data, students are constantly creating, using, and being impacted by the data that surrounds them. Equipping students with the skills and knowledge to make informed decisions, advocate for fair data practices, and thrive in a data-driven world is a critical role for K-12 education to play. However, students have relatively few opportunities to develop these foundatoinal data science skills. 

This talk will introduce the API Can Code, an interest-driven data science curriculum for high school students. I will focus on strategies for integrating data science learning within students’ lived experiences and fostering authentic engagement. 

Drawing on insights from classroom implementations, I will explore methods for designing contextually relevant and meaningful educational experiences in data science. In doing so, this work seeks to prepare students to succeed in the data-rich world that awaits them. 

David Weintrop is an Associate Professor and the Dean's Impact Professor in the Department of Teaching & Learning, Policy & Leadership in the College of Education with a joint appointment in the College of Information at the University of Maryland. 

His research focuses on the design, implementation, and evaluation of effective, engaging, and equitable computational learning experiences. His work lies at the intersection of design, computer science education, and the learning sciences. 

David holds a PhD in the Learning Sciences from Northwestern University and a B.S. in Computer Science from the University of Michigan.

A common purpose of computer programming is to carry out a task, where the desired object is the result of the task being carried out (e.g., data analysis or a simulation). We put forth another potential use of computer programming in a didactic setting—that of an inscribed semiotic resource similar to conventional mathematical notation.

Our theoretical findings draw from data where students concurrently used programming in Python and by-hand work to reason about counting problems in combinatorics, an area of mathematics that offers little formal notation to describe the sets of outcomes being counted or the processes to do so. Students used a computer to write and then run the programs, producing and counting the set of outcomes.

The use of Python allowed them to inscribe counting processes and then connect the inscriptions to mathematical expressions; in this way, computer program can be considered as one semiotic representation that allows for conversions into other representation systems. Although the exploration here is in the context of combinatorics, the findings apply to other areas of mathematics.

One potential implication is that programming languages might be introduced for didactical purposes as complementary to conventional representations, especially allowing for more robust descriptions and reasonings in areas of mathematics that offer limited representational resources.

Elise Lockwood er professor i matematikkdidaktikk ved Oregon State University og førsteamanuensis II i matematikkdidaktikk ved CCSE.

Hun har mottatt et NSF karrierestipend med fokus på hvordan beregninger påvirker studenters forståelse av kombinatorikk, og hun er en æresmottaker av Presidentens Early Career Award for Scientists and Engineers (PE-CASE) i 2025.

It has been argued by educational researchers that dealing with generative AI in higher education is a “wicked problem”: it cannot be permanently solved, but must be managed as an enduring feature of the educational landscape.

Any decision about how to respond to generative AI in education will have both positive and negative consequences — and importantly, choosing to do nothing is also a choice.

In this talk I will frame my experiences using generative AI from an educational-science perspective, and identify decisions I made and the trade‑offs involved.

Jan-Fredrik Olsen er førstelektor ved Lunds universitet. Han har lang erfaring med undervisning i matematikk ved Lund, og jobber nå med et doktorgradsprosjekt ved UiO/CCSE om hvordan beregninger påvirker studenters forståelse av matematiske konsepter.

Generative AI is rapidly reshaping how physicists teach, learn, and conduct research, yet little is known about how physics faculty are responding to these changes. We interviewed 12 physics professors at a major Scandinavian research university to explore their uses and perceptions of Generative AI (GenAI) in both teaching and research. 

Using the theoretical framework of epistemic framing, we conducted a thematic analysis that identified 19 overlapping practices, ranging from coding and literature review to assessment and feedback. From these practices, we derived six overlapping epistemic frames through which professors make sense of GenAI: as a threat to genuine learning and assessment, a source of knowledge, a discussion partner, a text-processing tool, a coding tool, and a labor-saving device. While the latter five position GenAI as a useful tool in the physicists’ toolbox, the threat frame represented an overarching concern that colored all other frames.

These findings reveal how GenAI is beginning to transform what it means to be a physicist, highlighting both opportunities for innovation and challenges for academic integrity and learning.

Les preprint av artikkelen.

Computational modeling is a central part of our physics practice and instruction, it allows students to engage with new physical phenomena, explore complex real world systems, and build highly relevant skills for both research and industry. Over the last few years however, students’ use of AI chatbots have challenged the ways we teach and assess this key practice.

We have found that some students use AI in ways they know are harmful for their learning when they are feeling rushed or don’t value mastering the task they are struggling with. Students who value learning and quality work however, needed to put more thought into when and how they use AI to reach a good result. The students who used AI most productively used it only as a support to their own problem solving, and used instructors and teaching assistants to keep their modeling on the right track.

This talk will present examples of these student experiences, and, building on a model of computational modeling, discuss how we should navigate the strengths and limitations of modeling with AI to best support student learning. 

Karl Henrik Fredly er doktorgradsstipendiat ved Senter for beregningsorientert realfagsutdanning. Han forsker på undervisning og bruk av programmering i fysikkemner på universitetsnivå, samt hvordan store språkmodeller bør brukes i disse emnene.