There is little consensus over whether graphical user interfaces (GUIs) or programmatic systems are better for word processing. Even less is known about each interfaces’ affordances and limitations in the context of creating content for adaptive tutoring systems. In order to afford instructors the use of such systems with their own or adapted pedagogies, we must study their experiences in inputting their content. In this study, we conduct a between-subjects A/B test with two content authoring interfaces, a GUI and spreadsheet, to explore 32 instructors’ experiences in authoring algebra content with hints, scaffolds, images, and special characters. We study their experiences by measuring time taken, accuracy, and their perceptions of each interfaces’ usability. Our findings indicate no significant relationship between interface used and time taken authoring problems but significantly more accuracy in authoring problems in the spreadsheet interface over the GUI. Although both interfaces performed reasonably well in time taken and accuracy, both were perceived as average to low in usability, highlighting a dissonance between instructors’ perceptions and actual performances. Since both interfaces are reasonable in authoring content, other factors can be explored, such as cost and author incentive, when deciding which interface approach to take for authoring tutor content.

Despite decades long establishment of effective tutoring principles, no adaptive tutoring system has been developed and open-sourced to the research community. The absence of such a system inhibits researchers from replicating adaptive learning studies and extending and experimenting with various tutoring system design directions. For this reason, adaptive learning research is primarily conducted on a small number of proprietary platforms. In this work, we aim to democratize adaptive learning research with the introduction of the first open-source adaptive tutoring system based on Intelligent Tutoring System principles. The system, we call Open Adaptive Tutor (OATutor), has been iteratively developed over three years with field trials in classrooms drawing feedback from students, teachers, and researchers. The MIT-licensed source code includes three creative commons (CC BY) textbooks worth of algebra problems, with tutoring supports authored by the OATutor project. Knowledge Tracing, an A/B testing framework, and LTI support are included.

Learning @ Scale has embraced movements that spread access to education through open and free platforms of learning. In this tutorial, we introduce OATutor (recently published at CHI'23), the field's first free and open-source adaptive tutoring system based on ITS principles and designed for rapid experimentation. The MIT-licensed platform can be deployed to git-pages in only a few clicks and supports BKT mastery-based adaptive problem selection. We demonstrate how the system can be used to rapidly run A/B experiments, analyze the data, and publish the entire tutor, content, and analysis scripts to facilitate unprecedented ease of replication and transparency, as demonstrated in a recent study comparing ChatGPT generated hints to human-tutor hints. Our four-part tutorial will include how to add lessons to the system and link to them from assignments in a MOOC platform or LMS via LTI. The structured JSON format of the four CC BY courses worth of content released with OATutor opens up avenues for researchers to apply new and existing educational data mining and NLP techniques (e.g., KC tagging) and rapidly evaluate the impact of subsequent changes on learners.