Understanding Student Help-Seeking for Contextualizing Chemistry through Curated Chatbot Data Analysis

Abstract

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 toward 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, 5185 events were extracted from the logs which 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.