The effects of transformative and non-transformative discourse on individual performance in collaborative-inquiry learning

作者：

Highlights：

• Machine learning model was built to identify (non-)transformative discourse.

• The effects of (non-)transformative discourse on learning were quantified.

• Interpretation and sustained mutual understanding led to better learning outcomes.

• Too much orientation and proposition generation resulted in poor learning outcomes.

摘要

•Machine learning model was built to identify (non-)transformative discourse.•The effects of (non-)transformative discourse on learning were quantified.•Interpretation and sustained mutual understanding led to better learning outcomes.•Too much orientation and proposition generation resulted in poor learning outcomes.

论文关键词：Collaborative-inquiry learning,CSCL,STEM,Learning analytics,Discourse analysis

论文评审过程：Received 2 June 2018, Revised 24 April 2019, Accepted 27 April 2019, Available online 29 April 2019, Version of Record 14 May 2019.

论文官网地址：https://doi.org/10.1016/j.chb.2019.04.022