Tor Ole Bigton Odden

• Myhrehagen, Henning Vinjusveen; Odden, Tor Ole Bigton & Henriksen, Ellen Karoline (2026). Integrating Programming into Norwegian Upper-Secondary Science Instruction: Teachers’ Backgrounds and Perceived Challenges. Nordic Studies in Science Education (NorDiNa). ISSN 1504-4556. 23(2), p. 234–254. doi: 10.5617/nordina.12186. Full text in Research Archive Show summary

  In 2020, Norway introduced a science curriculum reform integrating programming into upper-secondary natural science, biology, chemistry, and physics subjects. However, many teachers had limited programming experience, and it is therefore of interest to investigate how they have implemented it. We surveyed 260 science teachers three years after initial implementation to assess their programming backgrounds and perceived challenges with integrating programming into their instruction. We identified significant disciplinary differences: Physics teachers had the strongest programming foundation, while biology, chemistry, and natural science teachers struggled with learning programming and finding relevant disciplinary applications. Across all subjects, teachers cited insufficient knowledge among themselves and their students, lack of time, and limited resources as major challenges. While professional development (PD) courses have helped some get started, access to these courses is limited. Based on these results we argue that to ensure effective and continued programming integration, teachers need targeted, subject-specific PD that addresses both programming skills, science applications, and pedagogical strategies.

• Skogvoll, Vidar & Odden, Tor Ole Bigton (2026). How physics professors use and frame generative AI tools. European Journal of Physics. ISSN 0143-0807. 47(2). doi: 10.1088/1361-6404/ae55e0. Full text in Research Archive
• Sabo, Hannah Christine; Odden, Tor Ole Bigton & Caballero, Marcos (2026). How do we assess computation in physics? The Physics Teacher. ISSN 0031-921X. 64(Marchsd), p. 200–203. doi: 10.1119/5.0174333. Full text in Research Archive Show summary

  In recent years, computing has become an important part of the way we teach and learn physics. Teachers, both at high school and college levels, now use computational activities in many of their courses. Several countries have added programming or computational thinking to their secondary education standards and some physics departments now offer specialized courses and degrees in computational physics. Research and reports on computation in physics have identified some learning goals for computation: students should be able to use computers to handle and analyze data, read and interpret code, produce and extract physical insight from computational models. Furthermore, the American Association of Physics Teachers (AAPT) recommend the development of a robust, multimodal repository of discipline- and grade-level-appropriate assessment tasks. Although the Partnership for Integration of Computation into Undergraduate Physics (PICUP) website currently hosts many great computational physics activities, they do not specifically focus on assessment. Thanks to recent efforts, we know more about how to teach computation in physics9, but that’s only half the picture: we need to know how to assess it. In this paper, we respond to AAPT’s call for work on computational physics assessment activities by defining and providing a snapshot of two components of computational assessment: the assessment activity and the assessor’s focus. We use the term assessment activity to refer to a broad category of task, such as exams or assignments, and the term assessor’s focus to refer to the object of the assessor’s attention and manner of assessment, such as a code’s output or a student’s interpretation of the code’s result into physical meaning. "The assessment activity and assessor’s focus" can be mixed-and-matched for better coverage of students’ learning.

• Mingyar, Michael; Odden, Tor Ole B. & Willoughby, Shannon D. (2025). Exploring Query-Driven Centroiding and Embedding Strategies for Automated Thematic Analysis in Physics Education. Physics Education Research Conference Proceedings. ISSN 1539-9028. doi: 10.1119/perc.2025.pr.mingyar. Full text in Research Archive Show summary

  Understanding how Physics Education Research (PER) has changed and matured can help researchers and materially improve physics teaching and learning. The advent of large language models (LLMs) and embedding-based NLP techniques opens new avenues for this work by enabling the analysis of large corpora with minimal manual coding. This preliminary study builds off the work of [T. O. B. Odden et al., Physical Review Physics Education Research 20, 020151 (2024)]. We explore the use of text embeddings and retrieval-augmented generation (RAG)-like methods without generative AI to analyze themes across a random sample of 94 articles from major PER journals. Specifically, we examine how two methodological choices affect topic modeling performance: (1) representing articles using a single embedding versus multiple sentence-level embeddings, and (2) deriving topic centroids from representative texts versus researcher-defined queries. All results are evaluated against a human-coded dataset focused on four overarching thematic categories: teacher-centered, student-centered, physics content, and journal business. Our findings inform best practices for researchers seeking scalable, interpretable, and low-barrier approaches to literature analysis in PER.

• Caramaschi, Martina & Odden, Tor Ole B. (2025). Analyzing the history of physics education in the USA and Europe through natural language processing. Physical Review Physics Education Research. 21(2). doi: 10.1103/wfvw-hkyy. Full text in Research Archive Show summary

  This study systematically explores the thematic evolution of articles in and journals during a critical period in modern history, from the Cold War era to the prepandemic world (1966–2019). To contextualize the analysis, we first present an overview of historical events and developments that have characterized physics education in the United States and Europe since the 1950s. Using a natural-language-processing-based inductive topic modeling approach, we then identify recurring themes that have shaped physics education literature, including content-based topics, teaching methodologies, laboratory practices, curriculum development, and the influence of physics education research (PER). Our findings reveal both overarching trends and distinct thematic preferences between the journals. has historically emphasized curriculum structures, social aspects of education, and interdisciplinary connections, whereas has focused more on pedagogical strategies, demonstrations, and practical teaching tools. In the last three decades, both journals have increasingly incorporated discussions on technology, computation, and PER-driven instructional practices. By tracing these shifts over five decades, this study provides a broader perspective on how physics education has responded to changing educational priorities, technological advancements, and research developments.

• Haraldsrud, Andreas & Odden, Tor Ole Bigton (2025). Bridging Artificial Intelligence and Real Intelligence: Self-Scaffolding Computational Modeling with Generative AI in Chemistry. Journal of Chemical Education. ISSN 0021-9584. doi: 10.1021/acs.jchemed.5c00657. Full text in Research Archive Show summary

  Chemistry education researchers have, for many years, explored different ways of learning chemistry through modeling and open-ended problem-solving. With the emergence of generative artificial intelligence (GenAI) tools such as ChatGPT, students now have access to dynamic scaffolds that can potentially support them in modeling. However, we still know little about how these tools function within such contexts. This study investigates how students in higher education use GenAI to engage in complex computational modeling in chemistry. Using cognitive clinical interviews with think-aloud protocols, the role of GenAI in students’ modeling activities is analyzed through the lens of distributed cognition. The findings from this study reveal four distinct patterns of GenAI use: (1) Leveraging AI to retrieve and clarify information, (2) Using GenAI to test and critique ideas, (3) Outsourcing thinking to the AI, and (4) GenAI output exceeds student understanding. Productive interactions occurred when students actively orchestrated the AI as part of a distributed cognitive system, using it to retrieve, evaluate, and build upon their own reasoning. In contrast, unproductive use occurred when students outsourced thinking to the AI or were overwhelmed by outputs they could not integrate into their existing knowledge. These findings suggest that effective use of GenAI in chemistry education depends not only on technical proficiency, but also on students’ ability to structure prompts, evaluate AI output, and retain control of the problem-solving process. We argue that the use of GenAI should be explicitly addressed in chemistry instruction and propose that students be taught to engage with GenAI as part of a distributed cognitive system─ – retaining executive control, providing appropriate context, and iteratively refining their inquiries─ – to support meaningful engagement and learning in chemistry.

• Odden, Tor Ole Bigton & Hansen, Audun Skau (2025). Physics Intuition About Vectors Can Help Us Understand Generative Artificial Intelligence. The Physics Teacher. ISSN 0031-921X. 63(3), p. 173–176. doi: 10.1119/5.0149555. Full text in Research Archive
• Odden, Tor Ole Bigton & Zwickl, Ben (2025). How physics students build computational literacy by creating computational literature. The Journal of the Learning Sciences. ISSN 1050-8406. 34(5), p. 700–742. doi: 10.1080/10508406.2025.2494791. Full text in Research Archive Show summary

  Background Computation has revolutionized science and is gradually being integrated into science teaching and learning. However, theoretical frameworks for understanding how students learn to use computation as a disciplinary tool are currently underdeveloped. Methods In this study, we propose computational literacy as a productive theoretical lens on this subject, with a particular focus on how people engage with and write computational literature. This theory views computation as a literacy consisting of material, cognitive, and social elements, to which we add the notion of computational literature that has epistemic and communicative aspects. We use this perspective, operationalized through both narrative case study and conjecture mapping methodologies, to analyze the cases of two pairs of students writing computational essays in an intermediate physics course. Findings Through these case studies we see the pairs leveraging elements of their existing computational literacy to both engage with and produce computational literature in their discipline. This, in turn, helps them to adopt the practices of computational scientists and deepen their computational literacy. Contribution This study shows the usefulness of computational literacy as a framework for understanding computational learning within a discipline and elaborates on the role that computational literature plays in that process.

• Haraldsrud, Andreas & Odden, Tor Ole Bigton (2024). Using feedback loops from computational simulations as resources for sensemaking: a case study from physical chemistry. Chemistry Education Research and Practice (CERP). doi: 10.1039/d4rp00017j. Full text in Research Archive Show summary

  Sensemaking is an important way of learning and engaging in science. Research has shown that sensemaking activities, such as questioning, hypothesizing, and model building, are pivotal in developing critical thinking and problem-solving skills in science education. This paper investigates the role of computational simulations in facilitating sensemaking in chemistry education, specifically examining how these simulations can sustain the sensemaking process. Through a detailed case study in a physical chemistry course, we explore the interplay between students' real-world experiences, theoretical knowledge, and computational simulations. This analysis reveals that computational simulations, by providing interactive and visual representations of chemical phenomena, can create a conducive environment for sensemaking, where students actively engage in exploring and resolving conceptual uncertainties. Based on these results, we argue that computational tools, when effectively integrated into educational settings, can facilitate sensemaking in science education.

• Fredly, Karl Henrik; Odden, Tor Ole Bigton & Zwickl, Ben (2024). How Computational Physics Students Improve their Computational Literacy. Physics Education Research Conference Proceedings. ISSN 1539-9028. doi: 10.1119/perc.2024.pr.Fredly. Full text in Research Archive Show summary

  Physics students now use computation from the introductory level until the advanced level, but little is known about how or why they develop their programming skill. We have interviewed computational physics students toward the end of their studies to understand how and why their programming practice has changed. We found that there is a qualitative change in how they write and think about code when they encounter large collaborative coding projects. These projects challenge students with their inherent complexity, pushing them to make their code more readable and sophisticated. The collaborative nature of these projects also play a big part in making students reflect on their choices and learn from each other. Though this is only one example of how to teach computational physics, we discuss implications for teaching from these student experiences.

• Odden, Tor Ole Bigton; Tyseng, Halvor; Mjaaland, Jonas Timmann; Kreutzer, Markus Fleten & Malthe-Sørenssen, Anders (2024). Using text embeddings for deductive qualitative research at scale in physics education. Physical Review Physics Education Research. 20(2). doi: 10.1103/PhysRevPhysEducRes.20.020151. Full text in Research Archive Show summary

  We propose a technique for performing deductive qualitative data analysis at scale on text-based data. Using a natural language processing technique known as text embeddings, we create vector-based representations of texts in a high-dimensional meaning space within which it is possible to quantify differences in meaning as vector distances. To apply the technique, we build off prior work that used topic modeling via latent Dirichlet allocation to thematically analyze 18 years of the Physics Education Research Conference Proceedings literature. We first extend this analysis through 2023. Next, we create embeddings of all texts and, using representative articles from the 10 topics found by the LDA analysis, define centroids in the meaning space. We calculate the distances between every article and centroid and use the inverted, scaled distances between these centroids and articles to create an alternate topic model. We benchmark this model against the LDA model results and show that this embeddings model recovers most of the trends from that analysis. Finally, to illustrate the versatility of the method, we define eight new topic centroids derived from a review of the physics education research literature by Docktor and Mestre and reanalyze the literature using these researcher-defined topics. Based on these analyses, we critically discuss the features, uses, and limitations of this method and argue that it holds promise for flexible deductive qualitative analysis of a wide variety of text-based data that avoids many of the drawbacks inherent to prior NLP methods.

• McHale, Sarah; Odden, Tor Ole Bigton & Ken, Heller (2024). Mapping intent and impact of activity design in a computationally integrated physics course. Physics Education Research Conference Proceedings. ISSN 1539-9028. p. 280–285. doi: 10.1119/perc.2024.pr.McHale. Full text in Research Archive Show summary

  In this paper, we demonstrate the suitability of conjecture mapping for specifying misalignments between intent and impact of activity design. We situate our methodological inquiry in the context of a multi-day activity within a computationally integrated physics course, informed by data from interviews and ethnographic observations. Specifically, we consider affect through the theoretical frameworks of physics computational literacy and physics identity. We find that centering separate analyses on student and professor perspectives produces distinct conjecture maps that highlight misalignments in two different uses of mediating processes, which provides insight into the paths towards unintended affective outcomes.

• Caballero, Marcos D. & Odden, Tor Ole Bigton (2024). Computing in Physics Education. Nature Physics. ISSN 1745-2473. 20(3), p. 339–341. doi: 10.1038/s41567-023-02371-2. Full text in Research Archive
• Odden, Tor Ole Bigton & Caballero, Marcos (2023). Physics Computational Literacy: What, Why, and How? In Taşar, Mehmet Fatih & Heron, Paula R. L. (Ed.), The International Handbook of Physics Education Research: Learning Physics. AIP Publishing (American Institute of Physics). ISSN 9780735425477. p. 1–28. doi: 10.1063/9780735425477. Full text in Research Archive
• Haraldsrud, Andreas & Odden, Tor Ole Bigton (2023). From Integrated Rate Laws to Integrating Rate Laws: Computation as a Conceptual Catalyst. Journal of Chemical Education. ISSN 0021-9584. 100(5), p. 1739–1750. doi: 10.1021/acs.jchemed.2c00881. Full text in Research Archive Show summary

  When learning chemistry, students must learn to extract chemical information from mathematical expressions. However, chemistry students’ exposure to mathematics often comes primarily from pure mathematics courses, which can lead to knowledge fragmentation and potentially hinder their ability to use mathematics in chemistry. This study examines how computation can affect students’ blending of cognitive resources and their framing of mathematics in a chemical context. How students analytically and numerically interpret mathematical expressions in chemistry are examined through 14 individual clinical interviews with undergraduate chemistry students. The analysis was done by performing a thematic analysis of the interviews through a theoretical lens provided by the blended processing framework. This analysis reveals that computation and iterative thinking might serve as a catalyst for conceptual understanding and blending of cognitive resources. These findings suggest that we should evaluate how computational approaches could be leveraged to give students a better understanding of both mathematical models and the chemical concepts illustrated through these models.

• Odden, Tor Ole Bigton; Lauvland, Anders; Bøe, Maria Vetleseter & Henriksen, Ellen Karoline (2023). Implementing the learning assistant model in European higher education. European Journal of Physics. ISSN 0143-0807. 44(3). doi: 10.1088/1361-6404/acb39e. Full text in Research Archive Show summary

  Abstract The Learning Assistant (LA) Model is a widely used and researched model for institutional and course transformation towards research-based instructional strategies. The LA model leverages learning assistants, pedagogically trained students, to facilitate student learning in active learning environments. However, although the LA model has shown significant results when implemented in university contexts in the United States, there has as yet been little documentation of similar implementation or results in European higher education. In this study, we present the results of a three-semester design-based research project to implement the LA Model in a Scandinavian physics department. The three core elements of the LA model (pedagogical training, course content meetings, and teaching in active learning environments) were implemented and iteratively refined, with necessary adjustments due to the specific institutional and cultural contexts documented. Throughout all three semesters, data was collected on how participation in the LA model affected LAs’ buy-in to research-based instructional strategies using focus groups, pre/post surveys, and teaching observations. A thematic analysis of these data showed that participation in the LA model helped ensure LA buy-in to both the interactive engagement methods and goal of cultivating conceptual understanding that underlie most research-based instructional strategies, and that the combination of teaching practice and pedagogical training seminars were key to this buy-in. We argue that these results demonstrate the potential usefulness of the LA model for creating institutional and cultural change in European higher education.

• Odden, Tor Ole Bigton; Silvia, Devin W. & Malthe-Sørenssen, Anders (2022). Using computational essays to foster disciplinary epistemic agency in undergraduate science. Journal of Research in Science Teaching. ISSN 0022-4308. 60(5), p. 937–977. doi: 10.1002/tea.21821. Full text in Research Archive Show summary

  This article reports on a study investigating how computational essays can be used to help students in higher education STEM take up disciplinary epistemic agency—cognitive control and responsibility over one's own learning within the scientific disciplines. Computational essays are a genre of scientific writing that combine live, executable computer code with narrative text to present a computational model or analysis. The study took place across two contrasting university contexts: an interdisciplinary data science and modeling course at a large research university in the Midwestern United States, and a third-semester physics course at a large research university in Scandinavia. Over the course of a semester, computational essays were simultaneously and independently used in both courses, and comparable datasets of student artifacts and retrospective interviews were collected from both student populations. These data were analyzed using a framework that operationalized the construct of disciplinary epistemic agency across the dimensions of programming, inquiry, data analysis and modeling, and communication. Based on this analysis, we argue that computational essays can be a useful tool for fostering disciplinary epistemic agency within higher education science due to their combination of adaptability and disciplinary authenticity. However, we also argue that educational contexts, scaffolding, expectations, and student backgrounds can constrain and influence the ways in which students choose to take up epistemic agency.

• Odden, Tor Ole Bigton; Marin, Alessandro & Rudolph, John (2021). How has Science Education changed over the last 100 years? An analysis using natural language processing. Science Education. ISSN 0036-8326. 105, p. 653–680. doi: 10.1002/sce.21623. Full text in Research Archive Show summary

  For well over a century, the journal Science Education has been publishing articles about the teaching and learning of science. These articles represent more than just a repository of past work: they have the potential to offer insights into both the history of science education as well as well as the dynamics of field-specific change. It can be difficult, however, for educators, researchers, reformers, and policymakers to grasp the nuances of over 100 years of scholarship given the overwhelming amount of textual material. To address this problem, we have used latent Dirichlet allocation, an automated machine-learning algorithm from the field of natural language processing, to perform an automated literature review and classification of the corpus of work in Science Education. Using this technique, we have classified research in the journal into 21 distinct topics, falling into three thematic groups: science content topics, teaching-focused topics, and student-focused topics. We have also quantified the rise and fall of these topics and groups over time, and used them to begin to extract insight into the development of the field, including the effects of national policy changes on topics of interest to the research community, the interrelationships between different research topics, and the effects of intellectual cross-pollination. Based on this analysis, we argue that this technique shows great promise for even larger-scale analyses of educational literature and other textual data.

• Odden, Tor Ole Bigton (2021). How conceptual blends support sensemaking: A case study from introductory physics. Science Education. ISSN 0036-8326. 105, p. 989–1012. doi: 10.1002/sce.21674. Full text in Research Archive Show summary

  When learning science, students must often make sense of complex and counterintuitive ideas. However, this process of sensemaking is difficult, and consequently students risk emerging from science courses with highly fragmented understandings. In this study, I examine the ways in which students create conceptual connections to resolve such difficulties and defragment their understandings. Using three intersecting theoretical frameworks—Knowledge in Pieces, sensemaking, and conceptual blends—I analyze a case study of two undergraduate physics students making sense of the concepts of voltage, electric potential, and electric potential energy. I show how the students move through the different stages of the sensemaking process and how a conceptual blend was constructed and productively applied to help them resolve their knowledge fragmentation. Based on this case, I argue that conceptual blends can serve as a cognitive mechanism for the sensemaking process.

• Odden, Tor Ole Bigton (2021). What Does It Mean to “Make Sense” of Physics? The Physics Teacher. ISSN 0031-921X. 59, p. 596–598. doi: 10.1119/5.0024095. Full text in Research Archive Show summary

  What does it mean to “make sense” of physics? It’s not a simple question. Most people have an intuitive feeling for when things do (or do not) make sense to them. But putting this feeling into words—especially actionable words—is another task entirely. Generally speaking, there are two ways in which we commonly use the term “make sense.”1 The first connotes reasonability: for example, “it makes sense to start saving for college early.” Of the two, this is likely the more familiar meaning for the average physics teacher, since evaluating reasonability is a habit that we commonly teach in physics problem solving. We regularly instruct our students to check if their answers “make sense,” and there are numerous dependable strategies for this kind of evaluative sense-making such as unit checks, limiting-case analysis, order-of-magnitude estimation, and qualitatively examining relationships between variables.2 The second way we use “make sense” connotes understanding, intuition, and explainability (or lack thereof): for example, “it makes no sense to me how something as heavy as an airplane is able to fly.” This meaning is harder to put into action since it tends to be much more intuitive and emotional—what feels sensible to one person may be incoherent to someone else. I call this “intuitive sense-making,” to distinguish it from the evaluative sense-making described above. It is this meaning I would like to unpack here.

• Odden, Tor Ole & Malthe-Sørenssen, Anders (2020). Using computational essays to scaffold professional physics practice. European Journal of Physics. ISSN 0143-0807. 42(015701). doi: 10.1088/1361-6404/abb8b7. Full text in Research Archive Show summary

  This article describes a curricular innovation designed to help students experience authentic physics inquiry with an emphasis on computational modeling and scientific communication. The educational design centers on a new type of assignment called a computational essay, which was developed and implemented over the course of two semesters of an intermediate electricity and magnetism course at the University of Oslo, Norway. We describe the motivation, learning goals, and scaffolds used in the computational essay project, with the intention that other educators will be able to replicate and adapt our design. We also report on initial findings from this implementation, including key features of student-written computational essays, student reflections on the inquiry process, and self-reported conceptual and attitudinal development. Based on these findings, we argue that computational essays can serve a key role in introducing students to open-ended, inquiry-based work and setting the foundation for future computational research and studies.

• Odden, Tor Ole; Marin, Alessandro & Caballero, Marcos Daniel (2020). Thematic analysis of 18 years of physics education research conference proceedings using natural language processing. Physical Review Physics Education Research. 16(1). doi: 10.1103/PhysRevPhysEducRes.16.010142. Full text in Research Archive Show summary

  We have used an unsupervised machine learning method called latent Dirichlet allocation (LDA) to thematically analyze all papers published in the Physics Education Research Conference Proceedings between 2001 and 2018. By looking at co-occurrences of words across the data corpus, this technique has allowed us to identify ten distinct themes or “topics” that have seen varying levels of prevalence in physics education research (PER) over time and to rate the distribution of these topics within each paper. Our analysis suggests that although all identified topics have seen sustained interest over time, PER has also seen several waves of increased interest in certain topics, beginning with initial interest in qualitative, theory-building studies of student understanding, which gave way to a focus on problem solving in the late 2000s. Since 2010 the field has seen a shift toward more sociocultural views of teaching and learning with a particular focus on communities of practice, student identities, and institutional change. Based on these results, we suggest that unsupervised text analysis techniques like LDA may hold promise for providing quantitative, independent, and replicable analyses of educational research literature.

• Odden, Tor Ole & Burk, John (2020). Computational Essays in the Physics Classroom. The Physics Teacher. ISSN 0031-921X. 58(April), p. 252–255. doi: 10.1119/1.5145471. Full text in Research Archive Show summary

  Writing and argumentation are critical to both professional physics and physics education. They are both necessary to the practices of professional science like paper writing and peer review, and are increasingly emphasized in science education standards and best practices.1,2 Despite this, the skill of making an extended argument in writing is often overlooked in physics classrooms, apart from certain isolated practices like lab notebooks or mathematical proofs. Computation is also critical to both professional physics and, increasingly, physics education.3 Over the past 30 years computation has grown into a common, and often indispensable, part of the physics classroom. Visual, interactive tools like PhET simulations, Easy Java Simulations, and VPython,4–6 have steadily lowered the costs and barriers to entry for computational modeling, and recent pushes to expand students’ opportunities to learn programming7 have only added momentum to this trend. Now, with the advent of new computational technology we can use computation to facilitate writing and argumentation in physics, through the use of what are known as computational essays. What is a computational essay?

• Odden, Tor Ole; Lockwood, Elise & Caballero, Marcos Daniel (2019). Physics computational literacy: An exploratory case study using computational essays. Physical Review Physics Education Research. 15(2), p. 020152–020152. doi: 10.1103/PhysRevPhysEducRes.15.020152. Full text in Research Archive Show summary

  Computation is becoming an increasingly important part of physics education. However, there are currently few theories of learning that can be used to help explain and predict the unique challenges and affordances associated with computation in physics. In this study, we adapt the existing theory of computational literacy, which posits that computational learning can be divided into material, cognitive, and social aspects, to the context of undergraduate physics. Based on an exploratory study of undergraduate physics computational literacy, using a newly developed teaching tool known as a computational essay, we have identified a variety of student practices, knowledge, and beliefs across these three aspects of computational literacy. We illustrate these categories with data collected from students who engaged in an initial implementation of computational essays in a 3rd-semester electricity and magnetism class. We conclude by arguing that this framework can be used to theoretically diagnose student difficulties with computation, distinguish educational approaches that focus on material vs cognitive aspects of computational literacy, and highlight the benefits and limitations of open-ended projects like computational essays to student learning.

• Odden, Tor Ole & Russ, Rosemary (2019). Vexing questions that sustain sensemaking. International Journal of Science Education. ISSN 0950-0693. 41(8), p. 1052–1070. doi: 10.1080/09500693.2019.1589655. Full text in Research Archive Show summary

  Current science education reforms highlight the importance of students making sense of scientific ideas. While research has studied how to support sensemaking in classrooms, we still know very little about what drives students to pursue and persist in it on their own. In this article, we use a set of parallel case studies of undergraduate students discussing introductory physics to show how certain student-generated, vexing questions both initiate and sustain students' sensemaking processes. We examine affective and linguistic markers in student discourse in paired-clinical interviews to demonstrate both of these functions of vexing questions and detail their role in the explanations students construct. We conclude by discussing the implications of this analysis both for supporting sensemaking in classrooms and for studying it in research.

• Odden, Tor Ole & Caballero, Marcos Daniel (2019). Computational Essays: An Avenue for Scientific Creativity in Physics. Physics Education Research Conference Proceedings. ISSN 1539-9028. doi: 10.1119/perc.2019.pr.Odden. Full text in Research Archive Show summary

  Computation holds great potential for introducing new opportunities for creativity and exploration into the physics curriculum. At the University of Oslo we have begun development of a new class of assignment called computational essays to help facilitate creative, open-ended computational physics projects. Computational essays are a type of essay or narrative that combine text and code to express an idea or make an argument, usually written in computational notebooks. During a pilot implementation of computational essays in an introductory electricity and magnetism course, students reported that computational essays facilitated creative investigation at a variety of levels within their physics course. They also reported finding this creativity as being both challenging and motivating. Based on these reflections, we argue that computational essays are a useful tool for leveraging the creative affordances of programming in physics education.

• Odden, Tor Ole & Russ, Rosemary (2019). Defining sensemaking: Bringing clarity to a fragmented theoretical construct. Science Education. ISSN 0036-8326. 103(1), p. 187–205. doi: 10.1002/sce.21452. Full text in Research Archive
• Sand, Odd Petter; Odden, Tor Ole; Lindstrøm, Christine & Caballero, Marcos Daniel (2018). How computation can facilitate sensemaking about physics: A case study. Physics Education Research Conference Proceedings. ISSN 1539-9028. p. 1–4. doi: 10.1119/perc.2018.pr.Sand. Full text in Research Archive Show summary

  We present a case study featuring a first-year bio-science university student using computation to solve a radioactive decay problem and interpret the results. In a semi-structured cognitive interview, we use this case to examine the process of sensemaking in a computational science context. We observe the student entering the sensemaking process by inspecting and comparing computational outputs. She then makes several attempts to resolve the perceived inconsistency, foregrounding knowledge from different domains. The key to making sense of the model for this student proves to be thinking about how to implement a better model computationally. This demonstrates that integrating computation in physics activities may provide students with opportunities to engage in sensemaking and critical thinking. We finally discuss some implications for instruction.

• Odden, Tor Ole & Russ, Rosemary (2018). Recurring questions that sustain the sensemaking frame. Physics Education Research Conference Proceedings. ISSN 1539-9028. p. 1–4. doi: 10.1119/perc.2018.pr.Odden. Full text in Research Archive Show summary

  Many physics instructors aim to support student sensemaking in their classrooms. However, this can be challenging since instances of sensemaking tend to be short-lived, with students often defaulting to approaches based on answer-making or rote mathematical manipulation. In this study, we present evidence that specific recurring questions can serve a key role in the sensemaking process. Using a case-study of two students discussing an E&M thought experiment, we show how students' entry into sensemaking is marked by the articulation of a particular question, based on a perceived gap or inconsistency in understanding. and how this question recurs throughout their subsequent explanations, arguing that these recurrences may serve to stabilize and extend the process.

• Odden, Tor Ole & Russ, Rosemary (2018). Sensemaking epistemic game: A model of student sensemaking processes in introductory physics. Physical Review Physics Education Research. 14(2), p. 020122-01–02012-16. doi: 10.1103/PhysRevPhysEducRes.14.020122. Full text in Research Archive Show summary

  Although physics teachers often seek to help their students make sense of physics concepts, our field has yet to thoroughly explore how and why students engage in sensemaking. In this study we use the epistemic games framework to propose a model for students’ sensemaking processes. Our analysis of a series of clinical interviews with introductory physics students discussing electricity and magnetism topics suggests the goal of this sensemaking game is to iteratively construct an explanation in response to a perceived gap or inconsistency in knowledge. We illustrate the model with a case study of two students discussing electric current and use that case to outline the entry conditions, exit conditions, and several moves and constraints within this sensemaking game. We conclude by arguing for the utility of this model in designing learning environments that support sensemaking and in directing future sensemaking-focused research.

• Odden, Tor Ole & Russ, Rosemary (2017). "Charges are everywhere": A case of student sensemaking about electric current. Physics Education Research Conference Proceedings. ISSN 1539-9028. p. 280–283. doi: 10.1119/perc.2017.pr.065. Full text in Research Archive Show summary

  In recent years, there has been a growing interest in how students make sense of physics concepts, but there is as of yet little consensus on what it actually means for students to "make sense" of physics. We propose a theoretical model for the sensemaking process synthesized from the science education research literature on sensemaking. We then illustrate the model with a case study of two introductory physics students articulating and resolving an inconsistency in their understanding of electric circuits in a clinical interview context. We show how the students' transitions in discourse and framing fit with the model, and argue that this sensemaking process is driven by a particularly vexing inconsistency in knowledge.

View all works in NVA

• Tyseng, Halvor; Mjaaland, Jonas Timmann; Kreutzer, Markus Fleten & Odden, Tor Ole Bigton (2025). DANTE: Deductive Content Analysis Using Text Embeddings. doi: 10.3233/FAIA251443. Full text in Research Archive Show summary

  Traditional qualitative content analysis of text is laborintensive and resource-demanding. Recent NLP techniques can relieve these constraints, but they usually require substantial computational abilities. We introduce DANTE, a reliable and transparent few-shot tool, that makes text embedding-based qualitative analysis more accessible. The impact is significant because it opens up new avenues of research and enables researchers, regardless of programming abilities, to perform large-scale deductive content analysis of text.

• Skogvoll, Vidar & Odden, Tor Ole Bigton (2024). Lære fysikk med språkmodeller. Fra Fysikkens Verden. ISSN 0015-9247. 2, p. 74–80. Full text in Research Archive
• Odden, Tor Ole Bigton (2024). Computation as a Literacy in Physics. Full text in Research Archive
• Fredly, Karl Henrik; Odden, Tor Ole Bigton & Zwickl, Ben (2024). How Computational Physics Students Improve their Computational Literacy. Full text in Research Archive
• Odden, Tor Ole Bigton & Caballero, Marcos D. (2023). Theoretical Frameworks for Computational Physics Education. Full text in Research Archive
• Skogvoll, Vidar; Odden, Tor Ole Bigton & Myhrehagen, Henning Vinjusveen (2023). Real læring - ChatGPT: The teacher of tomorrow is a robot! [Radio]. Podcast. Full text in Research Archive
• Odden, Tor Ole Bigton (2022). Using LDA to thematically analyze PER literature. Full text in Research Archive
• Odden, Tor Ole Bigton (2022). Using computational essays to support student agency in physics. Full text in Research Archive
• Odden, Tor Ole; Yadav, Aman; Tellefsen, Cathrine Wahlstrøm & Caeli, Elisa Nadire (2020). Integrating Computing into K-16 Education: Scaffolding Teacher and Student Learning in STEM Disciplines. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE. ISSN 1942-647X. p. 517–518. doi: 10.1145/3341525.3393961. Full text in Research Archive
• Fyhn, Marianne; Malthe-Sørenssen, Anders; Mørken, Knut Martin; Nederbragt, Alexander Johan; Odden, Tor Ole & Sundnes, Joakim (2019). Beregningsorientering og utdanningsutvikling. Full text in Research Archive
• Odden, Tor Ole (2019). Computational Literacy and Computational Essays at the University of Oslo. Full text in Research Archive
• Odden, Tor Ole & Malthe-Sørenssen, Anders (2019). Computational Essays and Computational Literacy at the University of Oslo. Full text in Research Archive
• Lauvland, Anders; Odden, Tor Ole; Bøe, Maria Vetleseter & Henriksen, Ellen Karoline (2019). Learning assistants' transformed views on teaching and the teacher role after a training program. Full text in Research Archive
• Sand, Odd Petter; Odden, Tor Ole; Lindstrøm, Christine & Caballero, Marcos Daniel (2018). How computation can facilitate sensemaking about physics. Full text in Research Archive
• Odden, Tor Ole (2018). Grokking: The Endpoint of Sensemaking. Full text in Research Archive
• Odden, Tor Ole & Russ, Rosemary (2018). Vexing Questions that Sustain Sensemaking. Full text in Research Archive

View all works in NVA