TY - JOUR
T1 - Understanding Visitors’ Curiosity in a Science Centre with Deep Question Processing Network
AU - Xu, Zhaozhen
AU - Howarth, Amelia
AU - Briggs, Nicole
AU - Cristianini, Nello
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/11/20
Y1 - 2023/11/20
N2 - Questions have a critical role in learning and teaching. People ask questions to obtain information and express interest in ideas. The Bristol scientific centre “We The Curi- ous” launched “Project What If” in 2017 to inspire residents of Bristol to record their questions and pursue their curiosities. Researching these questions may help the museum better understand the curiosity of its audiences and create exhibitions or educational content that are more relevant to their interests and lives. The project managed to collect more than 10,000 questions on various topics, and more questions are being collected on a daily basis. With this large amount of data collected, it is time-consuming to process and analyse all the questions by humans. This research aims to apply artificial intelligence (AI) techniques and models in analysing these questions gathered by We The Curious. Meanwhile, in AI, there is a lack of tools that focus on processing and analysing the questions. Thus, we introduce a deep neural network called QBERT to process the questions for three tasks: question taxonomy, equivalent question detection, and question answering. Then we apply QBERT to pro- vide an analysis of the questions collected by We The Curious, as well as comprehend Bristolians’ curiosity. Then using QBERT, we categorise the We The Curious ques- tions into 90 themes and 5,930 communities. Moreover, 436 questions are answered by one-sentence answers extracted from Wikipedia.
AB - Questions have a critical role in learning and teaching. People ask questions to obtain information and express interest in ideas. The Bristol scientific centre “We The Curi- ous” launched “Project What If” in 2017 to inspire residents of Bristol to record their questions and pursue their curiosities. Researching these questions may help the museum better understand the curiosity of its audiences and create exhibitions or educational content that are more relevant to their interests and lives. The project managed to collect more than 10,000 questions on various topics, and more questions are being collected on a daily basis. With this large amount of data collected, it is time-consuming to process and analyse all the questions by humans. This research aims to apply artificial intelligence (AI) techniques and models in analysing these questions gathered by We The Curious. Meanwhile, in AI, there is a lack of tools that focus on processing and analysing the questions. Thus, we introduce a deep neural network called QBERT to process the questions for three tasks: question taxonomy, equivalent question detection, and question answering. Then we apply QBERT to pro- vide an analysis of the questions collected by We The Curious, as well as comprehend Bristolians’ curiosity. Then using QBERT, we categorise the We The Curious ques- tions into 90 themes and 5,930 communities. Moreover, 436 questions are answered by one-sentence answers extracted from Wikipedia.
KW - Deep Learning
KW - Natural language processing
KW - Question Answering
KW - BERT
U2 - 10.1007/s40593-023-00377-8
DO - 10.1007/s40593-023-00377-8
M3 - Article (Academic Journal)
SN - 1560-4306
JO - International Journal of Artificial Intelligence in Education
JF - International Journal of Artificial Intelligence in Education
ER -