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Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today’s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.
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To Read or Not to Read: Predicting Student Engagement in Interactive Reading
In this study, upper-elementary-age students used an interactive reading app to read from a classic children’s novel during a summer program. Students took turns reading with an adult virtual narrator (audiobook). We use process and background data to explore factors that could predict whether a reader will read their next turn or skip it. We find that skipping quickly becomes self-perpetuating, underscoring the need to support the teacher in providing just-in-time personalized intervention to help students avoid the disengagement trap.
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- Award ID(s):
- 2229612
- PAR ID:
- 10591598
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Journal Name:
- Journal of King Saud University Computer and information sciences
- Volume:
- 14830
- ISSN:
- 2213-1248
- ISBN:
- 978-3-031-64298-2
- Page Range / eLocation ID:
- 209-222
- Subject(s) / Keyword(s):
- reading engagement
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Book Chapter:
- https://doi.org/10.1007/978-3-031-64299-9_15
