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Technological tools, like virtual assistants (aka chatbots), have been ubiquitous in people’s day to day. The challenge becomes how educators leverage digital omnipresence to benefit the learning environment. Using a curated chatbot allows educators to reach more students with instructor-approved information, particularly in large classrooms. Students can receive direct responses and guidance towards course materials, and educators may have less to manage by automating routine queries to a chatbot. Data from the 293 collected logs from 232 unique student users provide insight into the information students are interested in when tasked to complete an essay assignment contextualizing chemistry through a sustainability lens. Using process mining to show how students seek information, the extracted 5185 events from the logs created 204 unique pathways from students’ actions in the curated chatbot. Additional text mining was done on the 116 freeform queries students typed into the curated chatbot. Results from both analyses showed that students were primarily sought information on the sustainability context of the writing assignment in their queries and that the curated chatbot can provide personalized assistance, responding to students’ unique pathways of seeking help. A selection of subsets of student users’ chatbot interactions, limitations of the study, and extension of the curated chatbot use in other classroom tasks and settings were discussed.
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NeTra: A Neuro-Symbolic System to Discover Strategies in Math Learning
Understanding how students with varying capabilities think about problem solving can greatly help in improving personalized education which can have significantly better learning outcomes. Here, we present the details of a system we call NeTra that we developed for discovering strategies that students follow in the context of Math learning. Specifically, we developed this system from large-scale data from MATHia that contains millions of student-tutor interactions. The goal of this system is to provide a visual interface for educators to understand the likely strategy the student will follow for problems that students are yet to attempt. This predictive interface can help educators/tutors to develop interventions that are personalized for students. Underlying the system is a powerful AI model based on Neuro-Symbolic learning that has shown promising results in predicting both strategies and the mastery over concepts used in the strategy.
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- Award ID(s):
- 1934745
- PAR ID:
- 10353238
- Date Published:
- Journal Name:
- Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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