Work

Predicting Specificity in Classroom Discussion

Year: 2017

Type: conference-paper

Abstract: High quality classroom discussion is important to student development, enhancing abilities to express claims, reason about other students' claims, and retain information for longer periods of time.Previous small-scale studies have shown that one indicator of classroom discussion quality is specificity.In this paper we tackle the problem of predicting specificity for classroom discussions.We propose several methods and feature sets capable of outperforming the state of the art in specificity prediction.Additionally, we provide a set of meaningful, interpretable features that can be used to analyze classroom discussions at a pedagogical level.

Source: Workshop on Innovative Use of NLP for Building Educational Applications

DOI: 10.18653/v1/w17-5006

Authors: Luca Lugini, Diane Litman

Institution: University of Pittsburgh

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: University of Pittsburgh