2024

Tor Ole Odden will hold a seminar about how Artificial Intelligence and human cognition are related. 

Andreas Haraldsrud is going to talk about how we can use computers to widen our understanding of proteins. Note: this seminar will be held in Norwegian. 

Computational literacy provides a metaphor and framework for how learners’ understanding, practice, and social relationships in computing can be organized.

In science disciplines that use computing, computational literacy posits cognitive, material, and social pillars of knowledge, practice, and belief.  In a new national computing education project, we are working to define these pillars and their related knowledge, practices, and beliefs across undergraduate physics.

As we have started that work, we've found the need to examine physics computational literacy (PCL) more critically. Here, we discuss issues related to the context in which a student’s PCL might develop and the relationship between that development and external forces (in the US, power and privilege), as well as pedagogical considerations.

The role of programming in mathematics education and research has been debated since Babbage and Lovelace described their Analytical Engine in the early 19th century. This debate has re-surfaced multiple times since: e.g., during the post-Sputnik era in the 1950s and 1960s, when personal computers and calculators became widely available in the 1980s, when the internet became widely available in the late 1990s, when high performance computing became widely available in smart phones and tablets in the 2010s, and most recently with the ongoing revolution in generative AI (this Spring, two consecutive issues of the Bulletin of the AMS was devoted to the topic). 

Artificial intelligence is a field which in recent years has had an exponential growth, both in the academic theatre and in the presence of our everyday life. 

An example of a Large Language Models (LLMs) is Open AI’s Chat GPT, which almost everyone in society has heard of or encountered in some form or another. As of today, it is suspected that these models can have a huge number of applications, and one which we have looked at during this summer-project is how they can be applied to the field of teaching.

In this presentation we will explain what we did during the project, our results and insights gained from researching how to utilize LLMs in the pedagogical scene, and some examples of how to integrate LLMs into exercises.

The University of Minnesota – Twin Cities Physics Department is restructuring its upper-level curriculum to integrate computation throughout the physics major courses. While this change has the potential to ensure that all students learn context-specific computing, it may also impact the ways students think of themselves as ‘physics people’ and of physics computational literacy. 

Our students take a programming course in their first semester, where they are taught the basics of syntax and coding strategy. They then go on to use programming in most of their other courses, gaining experience as they use it in varied and increasingly sophisticated ways. But we don’t really have specific goals, strategies or measurements in place for how students should develop their programming skills past the first year, despite programming being essential in physics research and industry. 

I’m therefore researching how computational physics masters students develop their skills in programming, using interview data and student code.

Markus Fleten Kreutzer, Jonas Timmann Mjaaland and Halvor Tyseng: 

Qualitative analysis is a cornerstone of social science research, used in fields as diverse as education research, sociology, history, and business. Traditional qualitative analysis, however, is time consuming, labor intensive, and difficult to both scale and replicate. To tackle these issues, we have devised a fast, replicable, and scalable technique for deductive, qualitative research on text-data, by utilizing novel advances in machine learning and artificial intelligence. 

Tor Ole Bigton Odden: 

Over the last year and a half, generative AI tools like ChatGPT have gone from niche research projects in the field of Natural Language Processing to tools that have upended whole sectors like education, software development, social science, and more.

This rapid development is partially due to the fact that these tools have, through gradual updates, developed capabilities that could not have been predicted in November 2022, like the ability to reason spatially, mathematically, and visually. Many users reasonably wonder how, and more importantly why, they work.

Henning Vinjusveen Myhrehagen: 

As of 2023, Norway has implemented a new science curriculum which explicitly includes programming as a part of education in science and mathematics. However, the curriculum documents do not provide clear-cut guidelines on how and to what extent programming should be used for science learning, and it is also unclear how far teachers have come with their implementation of programming in education.

To understand the effects of the new curriculum, we have surveyed Norwegian upper secondary science teachers to find out what affordances they see with the use of programming, and what challenges they are experiencing. 

Jana Legerská: 

Quantum physics is becoming increasingly important as the background knowledge for the emerging quantum technologies. In the recent years, the role of quantum physics in the high school curriculum has been emphasized, too. However, the curricular documents cannot bring a complete picture of incorporating quantum physics into the school reality.

In this talk, I will present a pilot study on describing the current state of teaching quantum physics at the high schools in the Czech Republic.

I will explore what topics are typically covered in what time constraints, what are teachers' main goals in their quantum physics teaching, and how teachers feel about introducing students to quantum physics.