The Distributed Open Education Network (Doenet) is an open data-driven educational technology platform that is being designed for measuring and sharing student interactions with web pages and storing anonymized data in an open distributed data warehouse. Doenet will allow instructors to assign activities published on outside web pages and collect the resulting data to evaluate student performance. Educational researchers will be able to analyze the resulting anonymized data to identify effective content, helping future instructors to select the best content.
Need. Despite a proliferation of options in educational technology, the most effective uses of technology to facilitate learning remain unclear. One obstacle to researching technology effectiveness is the increasing commercialization of data, as the value of personal data has been more widely recognized. At the same time, despite an abundance of openly available educational material, the paucity of available data on how students use and learn from those materials has impeded their effective use in the classroom. The absence of data-driven guidance for improving open educational content and its implementation inhibits our ability to develop educational experiences that will maximize learning.
Guiding Question. We are developing Doenet to promote the collection, analysis, and application of student interaction. We seek not only to facilitate the authoring of interactive educational content but also to empower faculty to design and implement online learning experiments to test the effectiveness of content and teaching strategies. Through preliminary learning experiments on Doenet, we are beginning to investigate the impact of guidance on student learning using interactive activities and virtual manipulatives.
Outcomes. We developed DoenetML, a semantic markup language for writing interactive activities based on the PreTeXt XML vocabulary, and have piloted using Doenet in a small number of mathematics courses. We created and implemented interactive activities written in DoenetML, including comparisons of multiple versions of the same content. These versions were successfully randomized at both the individual and student group levels, and analysis shows promise for both comparing instructional materials as well as catering materials to students’ backgrounds and interests. We are integrating our content hosting with IPFS (the InterPlanetary File System) where content is stored on a distributed peer-to-peer system, paving the way for content to be accessible even with limited network connectivity.
Broader Impacts. Doenet is designed to reduce barriers to the development and access of online content and learning experiments. We are creating authoring and experiment tools to streamline the task of setting up a course and learning experiments. Doenet’s distributed nature will facilitate participation without requiring a server or complicated setup. Our focus on semantic markup in DoenetML lays the groundwork for converting materials into accessible formats that are compatible with assistive technology, moving us closer to our goal of making content available to anyone.
Jim Fowler, Ohio State University, Columbus, OH; Matt Thomas, Cornell University, Ithaca, NY