A1 Journal article (refereed)
Analyzing teacher talk using topics inferred by unsupervised modeling from textbooks (2020)


Espinoza, Catalina; Pikkarainen, Toni; Viiri, Jouni; Araya, Roberto; Caballero, Daniela; Jiménez, Abelino; Gormaz, Raúl (2020). Analyzing teacher talk using topics inferred by unsupervised modeling from textbooks. FMSERA Journal, 3 (1), 4-17. https://journal.fi/fmsera/article/view/79631


JYU authors or editors


Publication details

All authors or editors: Espinoza, Catalina; Pikkarainen, Toni; Viiri, Jouni; Araya, Roberto; Caballero, Daniela; Jiménez, Abelino; Gormaz, Raúl

Journal or series: FMSERA Journal

eISSN: 2490-158X

Publication year: 2020

Volume: 3

Issue number: 1

Pages range: 4-17

Publisher: The Finnish Matehematics and Science Education Research Association

Publication country: Finland

Publication language: English

Persistent website address: https://journal.fi/fmsera/article/view/79631

Open Access: Publication published in an open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68829


Abstract

We propose a method that automatically describes teacher talk. The method allows us to describe and compare classroom lessons, as well as visualizing changes in teacher discourse throughout the course of a lesson. The proposed method uses a machine learning model to infer topics from school textbooks. Certain topics are related to different contents (e.g. kinematics, solar system, electricity), while others are related to different teaching functions (e.g. explanations, questions, numerical exercises). To describe teacher talk, the machine learning method measures the appearance of the inferred topics throughout each lesson. We apply the proposed method to a collection of transcripts from physics lessons, as well as discussing the potentialities of integrating the proposed method with other kinds of automatic and manual classroom lesson descriptions.


Keywords: teachers; lessons; speech (phenomena); machine learning


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 1


Last updated on 2020-18-08 at 13:18