Responding to Students' Science Ideas in a Natural Language Processing–Based Adaptive Dialogue (Poster 8)

• In Event: AERA Poster Session Three
  In Poster Session: Division C Science Poster Session Two

Abstract

We explored how Natural Language Processing dialogues that are designed following Knowledge Integration pedagogy elicit rich student ideas about photosynthesis and cellular respiration. We tested the dialogue in 7th grade middle school classrooms with 162 students. The dialog asks students to explain how animals get energy from the sun to survive. Students receive adaptive guidance based on their response, followed by generic guidance asking about their uncertainties. We found that the adaptive guidance helped students link normative ideas with evidence as well as generate non-normative ideas that needed further attention. After the dialogue, most students distinguished among all the ideas elicited and significantly improved their science explanations. Findings suggest that adaptive dialogs are a promising tool to scaffold science sense-making.

Authors

• Weiying Li, University of California - Berkeley
  Presenting Author

• Allison Bradford, University of California - Berkeley
  Non-Presenting Author

• Libby F. Gerard, University of California - Berkeley
  Non-Presenting Author