A4 Article in conference proceedings
Assessing Teacher’s Discourse Effect on Students’ Learning : A Keyword Centrality Approach (2020)

Schlotterbeck, Danner; Araya, Roberto; Caballero, Daniela; Jimenez, Abelino; Lehesvuori, Sami; Viiri, Jouni (2020). Assessing Teacher’s Discourse Effect on Students’ Learning : A Keyword Centrality Approach. In Alario-Hoyos, Carlos; Rodríguez-Triana, María Jesús; Scheffel, Maren; Arnedillo-Sánchez, Inmaculada; Dennerlein, Sebastian Maximilian (Eds.) EC-TEL 2020 : Addressing Global Challenges and Quality Education, Lecture Notes in Computer Science. Cham: Springer, 102-116. DOI: 10.1007/978-3-030-57717-9_8

JYU authors or editors

Publication details

All authors or editors: Schlotterbeck, Danner; Araya, Roberto; Caballero, Daniela; Jimenez, Abelino; Lehesvuori, Sami; Viiri, Jouni

Parent publication: EC-TEL 2020 : Addressing Global Challenges and Quality Education

Parent publication editors: Alario-Hoyos, Carlos; Rodríguez-Triana, María Jesús; Scheffel, Maren; Arnedillo-Sánchez, Inmaculada; Dennerlein, Sebastian Maximilian

Place and date of conference: Heidelberg, Germany, 14.-18.9.2020

ISBN: 978-3-030-57716-2

eISBN: 978-3-030-57717-9

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2020

Pages range: 102-116

Number of pages in the book: 489

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: http://doi.org/10.1007/978-3-030-57717-9_8

Open Access: Publication channel is not openly available

Abstract

The way that content-related keywords co-occur along a lesson seems to play an important role in concept understanding and, therefore, in students’ performance. Thus, network-like structures have been used to represent and summarize conceptual knowledge, particularly in science areas. Previous work has automated the process of producing concept networks, computed different properties of these networks, and studied the correlation of these properties with students’ achievement. This work presents an automated analysis of teachers’ concept graphs, the distribution of relevance amongst content-related keywords and how this affects students’ achievement. Particularly, we automatically extracted concept networks from transcriptions of 25 physics classes with 327 students and compute three centrality measures (CMs): PageRank, Diffusion centrality, and Katz centrality. Next, we study the relation between CMs and students’ performance using multilevel analysis. Results show that PageRank and Katz centrality significantly explain around 75% of the variance between different classes. Furthermore, the overall explained variance increased from 16% to 22% when including keyword centralities of teacher’s discourse as class-level variables. This paper shows a useful, low-cost tool for teachers to analyze and learn about how they orchestrate content-related keywords along with their speech.

Keywords: learning; teaching and instruction; teachers; discourse analysis

Free keywords: learning analytics; teacher discourse analysis; concept graphs; centrality measures

Contributing organizations

Ministry reporting: Yes

Preliminary JUFO rating: 1