Programme
Monday, May 13th - Topic: Introductions, CompLit in STEM
Introductions - Discussion: What do we all mean by computational literacy?
Tor Ole Odden: Computational Literacy at the University of Oslo: Practice and Research
What does it mean to learn to use computation in physics? One way to look at it is as a new type of literacy that students are acquiring, similar to the physics-specific ways of using mathematics and writing.
From this perspective, learning computation is much more than just programming: it is a way of thinking and learning, that involves specific ways of writing and using code, creating and evaluating models and data analyses, and communicating and collaborating with others around computational projects.
In this talk, I will outline some key elements necessary to become computationally literate in undergraduate physics, based on a longstanding research program on physics computational literacy at the University of Oslo. I will also outline what our computationally-focused physics curriculum looks like, and describe how we have studied this form of learning using computational essays.
CompLit across STEM: Henning Vinjusveen Myhrehagen and Christian Cammarota
Henning Vinjusveen Myhrehagen: High School Teachers’ Perspectives on Implementing Programming in Science: Affordances and Challenges
In recent years, Norway has implemented a new science curriculum which explicitly includes programming. 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.
Our results show that most teachers in general science and physics have started using programming in their teaching, although their perceived programming proficiency and confidence varies greatly. Physics teachers regard programming as a useful tool that enables the students to learn more physics than what was possible without programming. Teachers of other science subjects, however, struggle to see how and why programming should be integrated with their subject.
Based on our results, we argue that teachers need continued support from teacher educators and researchers from the science disciplines to advance the disciplinary integration of programming. We suggest that the overlap between scientific practices and computational thinking practices should play a central role in these efforts.
Christian Cammarota: A Framework for Social Computational Literacy / Integrating Computational Modeling into Genetics Education
This discussion will have two parts. The first part is a late-stage project investigating social computational literacy (SCL) among STEM researchers. Strong communication is a major focus for STEM researchers. Aspects of SCL, such as documentation and visual creation, are essential for researchers’ publications and presentations.
However, other aspects of SCL, such as writing well-documented code, is often described as a best practice to learners without a contextual basis. We interviewed 15 STEM researchers and used Activity Theory to identify the ways that SCL supported their research goals. We organized our analysis into a framework that separates goals by motivation (to persuade or to be productive), and by representation (visual and nonvisual).
The second portion of this discussion centers on an early-stage project integrating computational modeling into a bachelor’s level genetics course. Specifically, object-oriented programming will be used as an organizational scheme to explore polygenic inheritance using pedigree analysis. Using think-aloud interviews, we will probe students’ understanding of material, cognitive, and social aspects of the model, and to what degree an introduction to coding is beneficial to students.
Ben Zwickl: Integrating computation into a physics bachelor’s laboratory curriculum
Over the past decade, the RIT School of Physics and Astronomy has significantly increased the presence of computation within a series of four lab courses for physics bachelor’s students (electronics, optics, modern, and advanced labs). I will highlight unique aspects of computational literacy which arise in laboratory courses, such as data analysis, computational modeling of experimental data, and creating workflows for data collection and analysis.
I will discuss how integrating computation changes many instructional decisions for the laboratory, including: learning objectives, computational platforms (especially those beyond Python), the role of lab manuals, scaffolding skill development, the use of lab notebooks, what is graded, and collaboration.
I will also examine tensions that arise through the integration of computation, such as tension between documenting code and documenting other aspects of the experiment and tension between automation of data collection and teaching experimental techniques.
Tuesday, May 14th - Topic: Introductions, CompLit in STEM
Sarah McHale - Mapping Impact and Intent: Paths Towards Physics Computational Literacy and Physics Identity
The physics department at the University of Minnesota – Twin Cities is restructuring its upper-level curriculum to integrate computation throughout the physics major courses. My broader research centers on the first two courses involved in this change effort, both at an activity-level and a course-level. I investigate the affective impacts of this curricular change on through course observations and interviews with professors, TAs, and students.
Through the analytic method of conjecture mapping, I separate the intent behind the design from the resulting impact on students’ perceptions of themselves and of their computationally integrated physics courses.In doing so, I examine the role that curricular design plays in the development of students’ physics computational literacy and physics identities.
Karl Henrik Fredly: How Computational Physics Students Improve their Computational Literacy
Physics students at the University of Oslo learn programming in their first semester and go on to use it in almost every single course from then on, gaining experience as they use it in varied and increasingly sophisticated ways. But course instructors don't teach and assess programming at this increased level of sophistication.
Why then, do the students improve? And which resources do they draw on in order to improve? I have conducted interviews with computational physics masters students, and have found that when students write large codes in collaboration with others, they are motivated to improve their coding. Student are motivated both by the difficulty of the task. and from pressure from peers. Furthermore, peers play a vital role in giving guidance and opportunities for reflection on how to write better code.
In this talk, I will present how these factors and more motivate, help and hinder physics students in improving their programming skill.
CompLit in Math: Mike Foster and Jan-Fredrik Olsen
Mike Foster: The Design of a Computational Literacy Rubric/ How the Computational Tool can Influence Students’ Enacted Computational Literacy
This talk will be separated into two segments. In the first segment, the creation of a computational literacy (CL) rubric designed to assess students’ CL is presented. Building on the sub-pillars of physics computational literacy identified by Odden et al. (2019), our CL rubric is structured into nine categories, one for each pillar and sub-pillar combination. Within each of these categories, we operationalize a two-level approach to assessing students as evidencing proficient CL (i.e., aligning with computational objectives identified in the literature) or emergent CL (i.e., not aligning and having room for improvement). This rubric is forwarded as a means of assessing students’ CL in differing domains and across tools.
In the second segment, the early stages of a project comparing how different computational tools influence students’ enacted CL is presented. In the implementation of our CL rubric, a difference emerged in how students’ using different tools enacted their computational literacy. To further explore the influence the computational tool has on students’ enacted CL, we designed an assignment used in two sections of probability and statistics. In one section, the students used a menu-driven statistical tool called Minitab. In the other section, the students used the programming language of R. By comparing the students’ work on this assignment, we further the conversation about what tools are being used in our courses and how these tools influence students’ development of CL.
Jan-Fredrik Olsen: Why are computations controversial in undergraduate mathematics education?
The inclusion of programming in undergraduate mathematics education remains a contentious topic among mathematicians. While universities are increasingly placing programming courses into their study programs in mathematics, there is considerable reluctance to take advantage of these programming skills in foundational courses like calculus and linear algebra.
This project adopts a systemic approach, utilizing cultural-historical activity theory (CHAT) as its theoretical framework, to explore the roots causes of this tension (Engeström, 2015). Drawing upon interviews with 16 mathematicians from a leading Nordic university, we examine their use of programming in both research and teaching, along with their institutional views.
In the talk, I will present findings from the initial phase of the project, where we focus on the tensions related to the use of computer-based computations in the mathematicians’ research activities. This aligns with the recent trends in undergraduate mathematics education research, which increasingly recognize the importance of investigating authentic mathematical disciplinary practices (Nardi, 2023). Tentative implications for the future of undergraduate mathematics education will also be explored.
Tony Wong: Characterizing Longitudinal Development of Computational Literacy: Highlighting Needs and Opportunities
Computational skills have become a necessity in most, if not all, science, technology, engineering, and mathematics (STEM) fields. These skills include, but are not limited to, competency with tools to analyze and visualize data, perform calculations to test competing hypotheses, and automate routine analyses.
Computational literacy provides a useful lens for how students develop disciplinary knowledge and the computational skills to implement that knowledge. While the theoretical underpinnings of these skills often show up in mathematics degree program learning objectives or program goals, the computational skills themselves are often not explicitly included.
Here, we will highlight early stage work at the Rochester Institute of Technology to expand assessment of students’ computational literacy in our School of Mathematics and Statistics. Our aims for this discussion are to 1) consider how to evaluate students’ development of computational literacy in an undergraduate STEM degree program, and 2) generate ideas for how such a longitudinal assessment of skills development could be operationalized to yield curriculum improvements.
Wednesday, May 15 - Topic: CompLit and AI, Plus perspectives from Biology
Ariel Steele: Core Stages of Graduate Education Activate Imposter Feelings for Biology Doctoral Students
Imposter phenomenon (IP) is a persistent barrier for graduate students to fully engage with their programs. While previous research has treated IP as an individual experience, recent studies point to the need to examine IP as a social phenomena and how specific contexts in graduate education activate imposter feelings.
In this study, we interviewed 20 doctoral students about their experiences with IP in their graduate programs. Then, we mapped their experiences to specific graduate education contexts and milestones.
We found that high-pressure, high-stakes situations were more likely to activate imposter feelings, especially in the transition to graduate school, during lab rotations, and during the qualifying exam process. By identifying these specific contexts, we can pinpoint when graduate students may need additional support to fully engage with their program.
Andreas Haraldsrud: Computational literacy in chemistry and generative AI
We will discuss what makes a computationally literate chemist. We will also show productive and unproductive use of AI in chemistry with a focus on a computational setting.
CompLit for Researchers: Mike Verostek and Vidar Skogvoll
Mike Verostek: Computational literacy for education researchers: Creating a qualitative data analysis pipeline
This talk and discussion will be centered around computational literacy, but not for a particular science domain. Instead, we examine the role of CL in science education research. Traditional methods in qualitative data analysis often leverage either manual pen-and-paper approaches or dedicated software for handling qualitative data. Manual coding can offer more nuanced and complex insights into data, but can become too cumbersome as the size of the dataset grows. Meanwhile, software designed for qualitative analysis supports vast quantities of data, but researchers are limited with regard to how they are able to organize, summarize, and share results. Here, we describe a pipeline for qualitative data analysis that leverages multiple computational platforms to handle larger quantities of qualitative data (e.g., 40 interviews, ~40 hours of transcripts) while maintaining several advantages offered by manual coding techniques.
We will discuss our workflow, beginning with the use of Dedoose, a qualitative data analysis software, to perform traditional coding of the data. Exporting the data and codes from Dedoose into R allowed for reorganization of the data and codes for use in the software Lucid, which offers a graphical interface with which to organize and group codes, analogous to coding manually using sticky notes. We also demonstrate how the coding scheme developed in Lucid can be exported back to R for further analysis and graphical summary.
Thus, by combining several computational tools, this workflow offers researchers several of the benefits derived from coding manually as well as those from using dedicated qualitative data analysis software. We will use this case study to start a discussion on the multiple dimensions of CL for education researchers, varying levels of CL within the science education research community, and how this form of disciplinary CL is learned.
Vidar Skogvoll: Best uses of artificial intelligence in learning and teaching
Many hoped that artificial intelligence would even the playing field among students, making it easier for students struggling to master difficult concepts. However, few professors employ artificial intelligence as a meaningful tool of scaffolding, and fewer still are teaching the skills necessary to use AI in a productive way for learning.
Struggling students might find that the playing field might become more rough. Thus, there is a dire need for resources on how artificial intelligence can be used in education to both foster learning, scaffold teaching of hard subjects and do research.
In this talk, I will lay out our efforts in covering this terrain, and also talk about our coming summer project in developing exercises to include AI-scaffolding.
Wrapup, final discussion
Workshop for invited participants:
- Tony Wong, Mike Foster, Mike Jr Verostek, Christian Cammarota and Ben Zwickl from Rochester Institute of Technology, Physics Education Research Group
- Sarah McHale and Ariel Steele from University of Minnesota Twin Cities, School of Physics and Astronomy
- Jan-Fredrik Olsen from Lund University, Mathematics
- Henning Vinjusveen, Andreas Haraldsrud, Vidar Skogvoll, Halvor Tyseng, Markus Fleten Kreutzer, Jonas Timmann Mjaaland, Karl Henrik Fredly and Tor Ole Odden from University of Oslo, Center for Computing in Science Education