Thank you for watching our video! My name is Emma Anderson and I am one of the researchers on Making Sense of Models. I, along with my colleagues Irene Lee and Aditi Wagh, are very interested in hearing from you. 

In our video we speak about how we present students with broken models and ask them to fix them so they better represent the scientific phenomenon being modeled. We would love to hear what you think of this approach. How do you help students think through the validity of a computational scientific model? 

All of our units link, math, science and CT learning. We use the broken model activity as a way for students to think through the science phenomena they have learned, understand the math concepts and relationships learned, and connect to the underlying CT in the model as they figure out how to fix the model. Do you do cross curricular CT integration? If so, how do you help students make links across different disciplines so that students are gaining knowledge and skill in both or all three disciplines? What do you think of our approach?

Of course we would love to hear all questions and comments even if not related to these two areas of focus (model validity and cross curricular learning). 

I like this approach -- giving students the opportunity (and indeed the need) to think critically in domain-specific terms.  It does seem as though construing "model" principally in terms of computer models may introduce additional levels of complexity that can distract from the more basic challenges of learning how to critique representations vis-à-vis the phenomena being represented.  Are you (is your research) interested in the more general problem of model validation?  

Very interesting work.  Can you tell us more about any specific learning outcomes you have in mind, in mathematics, scientific process, or computing?  It would seem there might also be gains in confidence that would be good to assess.  Do you have data about what students see that signals the model is broken - and do students see the "same problems" in a given model?

Hi Joan! Thanks for your questions!

To build on Emma’s reply about learning outcomes: A key learning outcome we are interested in is students’ understanding of mechanism. Each of our curricular units centers on a particular mechanism which is represented in code in the model. For instance, in one unit, students explore probabilistic conditionals to investigate the mechanism of varied reaction to surfaces. Our math and science learning outcomes in each unit are centered around that particular mechanism. For instance, in this unit, students investigate the albedo effect and its impact on rising ocean temperatures, and in math, they explore probability and chance. In this way, each unit focuses on a coding, math and science concept that are tightly linked in the unit (probabilistic conditionals, probability and albedo and rising ocean temperatures). 

The assessment corresponding to each unit is designed for us to investigate this linked code-math-science mechanistic understanding. We are looking for gains in each independently (code, math and science) as well as gains in their mathematical and scientific understanding of a concept through code (math+code and science+code). Feel free to reach out if you’d like to learn more or look at the assessments (awagh@mit.edu). 

In our student interviews, we are seeing that students detect that the model is broken in different ways. Many students notice that something is off from the visuals in a simulation run of the model. Some students notice the graph that is dynamically updated as the model runs, and recognize that the trend does not look right. Fewer students look right away at the code and notice that it’s broken. As Emma pointed out, most students are able to identify that something is wrong with the model. 

And you’re absolutely right about gains in confidence. In interviews, students talk about feeling proud that they could tell the model was wrong and were able to fix it. Field observers from classroom implementations have also noted that students express feelings of confidence around being able to engage in modeling and coding. This is something we're looking to assess in future work. 

Thanks for sharing about this great curriculum. I appreciate the integration or Science, Math, and CS.

I am wondering how student choice and experience is embedded into these complex lesson plans? Also, what motivated the creation of this curriculum?

Such empowering work! This is a great way to engage students in issues that more greatly resemble real-world issues. It's rare that we walk into an error-free situations. This trains them to look for flaws and trains them to use the tools to fix them. It would be great to hear more about how students grow and collaborate in this model.

I really enjoyed learning about this project. I also love the idea of students trying to figure out what it broken in code so that it can be fixed to better portray a scientific phenomena. I loved hearing the young student share the how empowering it was to find what was broken. In some of my own work, I give students challenging visual puzzles to solve and then ask them to share about the process of finding the solution. 

Can you share about students persistence and specifically about their ability to persist productively- even when the task is really challenging and they are stuck? Also, I note that you have a focus both on the coding and on the scientific phenomena. How are these dual learning goals managed? Do students struggle with the coding more or the scientific concepts? Or perhaps with something different all together?

Thanks and I look forward to learning more.

I enjoyed watching the video for your project.  I am part of a team that has been researching the impacts of using programming to teach mathematical reasoning. Though our current project has been focused on 7^th and 8^th grade math classrooms, we have utilized our instructional model in science classrooms as well.  We have a high school forensics lesson, an 8^th grade science lesson about waves, and a high school bioinformatics lesson. These lessons were developed in conjunction with Physics faculty members and local science teachers. When we are training teachers to use our instructional model, we often have them investigate and correct broken codes.  We have found this builds their confidence in programming before leading the students in the coding activities.  It is interesting to see the students in this role and will be something for us to consider in the future as well.   Thanks for sharing your work!

I really like how the student was proud of the fact that they were able to identify the issue with the model themselves. And from one of the replies it sounds like other students said similar things! Building confidence in problem-solving and STEM is fantastic!