In this four-day workshop, participants will learn how to create and implement online learning experiments using the Distributed Open Education Network (Doenet, doenet.org). Doenet is designed to help faculty critically evaluate how different content choices influence student learning in their classrooms. Doenet enables instructors to quickly test hypotheses regarding the relative effectiveness of alternative approaches by providing tools to assign different variations of an activity and analyze the resulting data.
Following brief introductions and demos of features of the Doenet platform, participants will work in small groups to develop learning experiments that can be used in the college classroom, assisted by the developers of Doenet. The expectation is that participants will leave the workshop with a learning experiment that they can use in their classroom the following year.
The workshop will run from 9 AM on Monday, May 23 though noon on Thursday, May 26. All organized activities will occur between 9 AM and 4 PM each day.
The workshop is open to faculty at all levels teaching STEM courses; instructors of mathematics courses are particularly encouraged to apply.
To apply, please submit the following documents through the Program Application link at the top of the page:
- A personal statement briefly (200 words or less) stating what you hope to contribute to the discussion on learning experiments and what you hope to gain from this workshop. Include courses you teach for which you'd like to develop learning experiments. Priority will be given to those able to run learning experiments in their courses in the following year.
- A brief CV or resume. (A list of publications is not necessary.)
This workshop is fully funded by the National Science Foundation. All accepted participants who request funding for travel and/or local expenses will receive support. There is no registration fee.
Participants who perform learning experiments on Doenet during the following academic year will be eligible to receive a small stipend to support their work.
Supported by NSF grant DUE 1915363.