An official website of the United States government
Abstract Guiding teachers to customize curriculum has shown to improve science instruction when guided effectively. We explore how teachers use student data to customize a web-based science unit on plate tectonics. We study the implications for teacher learning along with the impact on student self-directed learning. During a professional development workshop, four 7th grade teachers reviewed logs of their students’ explanations and revisions. They used a curriculum visualization tool that revealed the pedagogy behind the unit to plan their customizations. To promote self-directed learning, the teachers decided to customize the guidance for explanation revision by giving students a choice among guidance options. They took advantage of the web-based unit to randomly assign students (N = 479) to either a guidance Choice or a no-choice condition. We analyzed logged student explanation revisions on embedded and pre-test/post-test assessments and teacher and student written reflections and interviews. Students in the guidance Choice condition reported that the guidance was more useful than those in the no-choice condition and made more progress on their revisions. Teachers valued the opportunity to review student work, use the visualization tool to align their customization with the knowledge integration pedagogy, and investigate the choice option empirically. These findings suggest that the teachers’ decision to offer choice among guidance options promoted aspects of self-directed learning.
more »
« less
Can Crowds Customize Instructional Materials with Minimal Expert Guidance?: Exploring Teacher-guided Crowdsourcing for Improving Hints in an AI-based Tutor
AI-based educational technologies may be most welcome in classrooms when they align with teachers' goals, preferences, and instructional practices. Teachers, however, have scarce time to make such customizations themselves. How might the crowd be leveraged to help time-strapped teachers? Crowdsourcing pipelines have traditionally focused on content generation. It is an open question how a pipeline might be designed so the crowd can succeed in a revision/customization task. In this paper, we explore an initial version of a teacher-guided crowdsourcing pipeline designed to improve the adaptive math hints of an AI-based tutoring system so they fit teachers' preferences, while requiring minimal expert guidance. In two experiments involving 144 math teachers and 481 crowdworkers, we found that such an expert-guided revision pipeline could save experts' time and produce better crowd-revised hints (in terms of teacher satisfaction) than two comparison conditions. The revised hints however, did not improve on the existing hints in the AI tutor, which were carefully-written but still have room for improvement and customization. Further analysis revealed that the main challenge for crowdworkers may lie in understanding teachers' brief written comments and implementing them in the form of effective edits, without introducing new problems. We also found that teachers preferred their own revisions over other sources of hints, and exhibited varying preferences for hints. Overall, the results confirm that there is a clear need for customizing hints to individual teachers' preferences. They also highlight the need for more elaborate scaffolds so the crowd can have specific knowledge of the requirements that teachers have for hints. The study represents a first exploration in the literature of how to support crowds with minimal expert guidance in revising and customizing instructional materials.
more »
« less
- Award ID(s):
- 1760922
- PAR ID:
- 10601609
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Format(s):
- Medium: X Size: p. 1-24
- Size(s):
- p. 1-24
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Orchestration tools may support K-12 teachers in facilitating student learning, especially when designed to address classroom stakeholders’ needs. Our previous work revealed a need for human-AI shared control when dynamically pairing students for collaborative learning in the classroom, but offered limited guidance on the role each agent should take. In this study, we designed storyboards for scenarios where teachers, students and AI co-orchestrate dynamic pairing when using AI-based adaptive math software for individual and collaborative learning. We surveyed 54 math teachers on their co-orchestration preferences. We found that teachers would like to share control with the AI to lessen their orchestration load. As well, they would like to have the AI propose student pairs with explanations, and identify risky proposed pairings. However, teachers are hesitant to let the AI auto-pair students even if they are busy, and are less inclined to let AI override teacher-proposed pairing. Our study contributes to teachers’ needs, preference, and boundaries for how they want to share the task and control of student pairing with the AI and students, and design implications in human-AI co-orchestration tools.more » « less
-
Students benefit from dialogs about their explanations of complex scientific phenomena, and middle school science teachers cannot realistically provide all the guidance they need. We study ways to extend generative teacher–student dialogs to more students by using AI tools. We compare Responsive web-based dialogs to General web-based dialogs by evaluating the ideas students add and the quality of their revised explanations. We designed the General guidance to motivate and encourage students to revise their explanations, similar to how an experienced classroom teacher might instruct the class. We designed the Responsive guidance to emulate a student–teacher dialog, based on studies of experienced teachers guiding individual students. The analyses comparing the Responsive and the General condition are based on a randomized assignment of a total sample of 507 pre-college students. These students were taught by five different teachers in four schools. A significantly higher proportion of students added new accurate ideas in the Responsive condition compared to the General condition during the dialog. This research shows that by using NLP to identify ideas and assign guidance, students can broaden and refine their ideas. Responsive guidance, inspired by how experienced teachers guide individual students, is more valuable than General guidance.more » « less
-
This design-based research takes advantage of advanced technologies to support teachers to rapidly respond to evidence about student ideas generated in their classrooms. Leveraging advances in natural language processing methods, the Teacher Action Planner (TAP) analyzes students’ written explanations embedded in web-based inquiry projects to provide teachers with a report on student progress in developing the three-dimensional understanding called for by the Next Generation Science Standards. Based on the pattern in student scores, the TAP recommends research-based ways for teachers to customize instruction. This study examines how ten middle school teachers in 4 schools used the analysis of student ideas and suggestions for instructional customization presented in the TAP. This paper reports on how well their implemented customizations addressed student learning needs. It concludes with a discussion of the implications of the findings for redesign of the TAP.more » « less
-
Wang, N.; Rebolledo-Mendez, G.; Matsuda, N.; Santos, O.C.; Dimitrova, V. (Ed.)Research indicates that teachers play an active and important role in classrooms with AI tutors. Yet, our scientific understanding of the way teacher practices around AI tutors mediate student learning is far from complete. In this paper, we investigate spatiotemporal factors of student-teacher interactions by analyzing student engagement and learning with an AI tutor ahead of teacher visits (defined as episodes of a teacher being in close physical proximity to a student) and immediately following teacher visits. To conduct such integrated, temporal analysis around the moments when teachers visit students, we collect fine-grained, time-synchronized data on teacher positions in the physical classroom and student interactions with the AI tutor. Our case study in a K12 math classroom with a veteran math teacher provides some indications on factors that might affect a teacher’s decision to allocate their limited classroom time to their students and what effects these interactions have on students. For instance, teacher visits were associated more with students’ in-the-moment behavioral indicators (e.g., idleness) than a broader, static measure of student needs such as low prior knowledge. While teacher visits were often associated with positive changes in student behavior afterward (e.g., decreased idleness), there could be a potential mismatch between students visited by the teacher and who may have needed it more at that time (e.g., students who were disengaged for much longer). Overall, our findings indicate that teacher visits may yield immediate benefits for students but also that it is challenging for teachers to meet all needs - suggesting the need for better tool support.more » « less
