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Auteur:Forrest Bradbury

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HBO/WO

MBO

2024

How to design open inquiry labs

In this article we show how student “open inquiries” in undergraduate labs can be powerful teaching and assessment methods. In such open inquiries it is the students who define the question they want to answer, formulate the hypothesis, search for information, design and carry out experiments to reach their goal, and share their ideas, needs, and results with others. This student agency afforded by open inquiry trains critical thinking, teaches students to learn from failure, and requires effective science communication in authentic contexts. Adapting open inquiry methods for the first time can be especially daunting for instructors who have no examples to draw upon. To address this challenge we explain the findings of a diverse group of five lab instructors and an educational researcher who have compared open inquiry practices in different contexts. As a group, we formed a Community of Practice dedicated to “Supporting students’ scientific Open Inquiries in Labs” (aka SssOIL). The commonalities across our diverse project team suggest some defining features and best practices. By sharing our findings and example course designs and materials, we hope to aid instructors in adapting more open inquiry teaching methods in their own lab courses. Any questions about this article can be addressed to Forrest via: forrestbradbury@gmail.com or to our team via: S3OIL@outlook.com

Document

Forrest Bradbury, Simone Mesman, Morten Stromme, Paul Logman

2023

Maker Lab assignments (and rubrics)

The subpages below link to the Maker Lab assignments, where the two Open Inquiries (and their relevant assignments) make up 80% of the students' final course grades. An overview of the assignments, their weightings, and their connections to course learning objectives can be found in the Maker Lab course description document (under the Maker Lab course information page).

Forrest Bradbury

2023

Lecture Notes chapter 3: Uncertainty Analysis

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 3 of the lecture notes introduces Uncertainty Analysis using Python. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Maker Lab Open Inquiry: Presentation grading forms

Each of the Maker Lab's two Open Inquiry projects requires two graded presentations: a midway presentation by one of the two team members, and a final presentation by the other team member. These are submitted as pre-recorded videos. The midway videos are played during class, each followed by 10 minute live-Q&A sessions with peers and instructor(s). The final videos, on the other hand, are not played during class. Instead the presenter gives an abbreviated live version during class, followed by a 5-10 minute live-Q&A session with peers and instructor(s). Students are instructed to pitch the level of the talk at their peers (who have not done nor closely followed their inquiry). Students are further given these grading forms in advance to see on which assessment criteria their talk will be judged. Note: Simple "grading forms" are used instead of rubrics, with the difference being that performance levels are not individually described. I use grading forms instead of rubrics for several reasons: 1. I am the only grader for the course, thus inter-rater reliability is not an issue. 2. I include 10-12 specific assessment criteria which themselves help to communicate expectations, 3. The diverse student projects make it difficult to create meaningful level descriptors, and 4. I often leave explanatory remarks under the individual assessment criteria to explain my marks.

Document

Forrest Bradbury

2023

Maker Lab course schedule (example)

This example Maker Lab course schedule is meant to help show how the course is structured and implemented. In addition to the full .pdf schedule, a pictoral representation is given to show the basic timeline, starting with skills training and then two open inquiries. Note that this example schedule has benefitted from four previous course iterations, and has been tweaked each time for better and more efficient student support. Instructors implementing open inquiry lab projects for the first time are advised to schedule in a bit extra student contact time until they have a good sense of when and how the students need to be supported.

Afbeelding

Forrest Bradbury

2023

Lecture Notes chapter 4: Curve Fitting

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 4 of the lecture notes introduces Curve Fitting using Python. In fact, one of these documents covers Curve Fitting using two different Python libraries (lmfit and scipy), however, the lmfit library is the recommended one for the Maker Lab course, as it's more user-friendly and covers most uses needed by the student projects. Thus, the other document cuts out the scipy library explanations, keeping the document shorter and easier to follow. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Maker Lab complete set of JupyterPython-materials zip file

This a zip file of all the course's Jupyter Notebook documents, bundled together here for the convenience of anyone wanting to use them within Jupyter Notebook*. The Maker Lab course uses Jupyter Notebook documents in several ways: for Readings for Students (specifically the "lecture notes") and for Assignments (both descriptions and templates). These individual files are found in the relevant pages here on Edusources - and also shared in PDF form so that they can be viewed without using Jupyter Notebooks. However, in order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is best to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via this JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. *Note: there is a text file within this zip file with instructions for installing Python and Jupyter Notebook.

Document

Forrest Bradbury

2023

Maker Lab Assignment: Semi-structured_Experiments

Before the Maker Lab students begin the open inquiry phases, they first learn and practice some important skills, supported by small assignments. This assignment is a "structured", or "guided" inquiry, where students know ahead of time the result they should find, but have agency in their experimental design, within some prescribed limits. By assigning all teams different methods for addressing a single research question (currently: estimating the acceleration due to gravity), they are encouraged to reflect upon how experimental design and equipment limits the certainty of empirical findings. This assignment requires teams of four students (organized into two pairs, each performing the experiment separately, but analyzing data and reporting on conclusions together). In two weeks (wherein students' course workload is 10 hours per week), the teams must set up experiments, controlling sensors with self-programmed Arduinos, collect and analyze experimental data, and create a pre-recorded video presentation to be viewed in class and aimed at their peers, followed by a Q&A. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324 Contents: partial experimental instructions (for students) in Jupyter Notebook: Semi-structured_Experiments.ipynb (and .pdf ) grading form for pre-recorded video presentation (both .docx and .pdf )

Document

Forrest Bradbury

2023

Maker Lab: full course materials

This is an open inquiry natural science lab course utilizing Maker tools (e.g. inexpensive electronic sensors/sensorboards controlled by Arduinos). Students are tasked with conceiving, designing, and carrying out scientific experiments with significant process guidance but little experimental oversight, instead leveraging the Maker movement's online resources and open source hardware and software tools. Target audience: The course is given at Amsterdam University College to a multidisciplinary mix of natural science bachelor's students, usually in their second or third year of a three year bachelor's. Duration / Study load: 6 ECTS course, equivalent to ~168 total hours of student work, evenly spread across a 16 week term Over-arching learning goals: (see also the Learning Objectives subpage for specific, assessable list) students navigate the iterative empirical research process, including conception, experimental design, testing, data & uncertainty analyses, and drawing conclusions students build self-efficacy in conducting quantitative empirical research and creating scientific knowledge students improve science communication skills in an authentic (team) setting Directory: Course materials are published in the following locations (see links to them below) course description (pre-requisites, learning activities, teaching roles, grouping of students, learning environment) learning objectives course schedule readings and lecture slides for students assignments (and rubrics) resource list: facilities, equipment, software JupyterPython-materials zip file This page's linked websites give official course descriptions. Any questions about this Maker Lab course can be addressed to Forrest via: forrestbradbury@gmail.com

Website

Forrest Bradbury

2023

Lecture Notes chapter 5: Fourier Transform

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 5 of the lecture notes introduces the discrete Fourier Transform using Python. This lecture notes document is presented in the Maker Lab course as optional reading, but sometimes relevant to students with projects involving (for example) signal analyses in the temporal frequency domain. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury