A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Schlotterbeck, Danner; Araya, Roberto; Caballero, Daniela; Jimenez, Abelino; Lehesvuori, Sami; Viiri, Jouni

Emojulkaisu: EC-TEL 2020 : Addressing Global Challenges and Quality Education

Emojulkaisun toimittajat: Alario-Hoyos, Carlos; Rodríguez-Triana, María Jesús; Scheffel, Maren; Arnedillo-Sánchez, Inmaculada; Dennerlein, Sebastian Maximilian

Konferenssin paikka ja aika: Heidelberg, Germany, 14.-18.9.2020

ISBN: 978-3-030-57716-2

eISBN: 978-3-030-57717-9

Lehti tai sarja: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Julkaisuvuosi: 2020

Artikkelin sivunumerot: 102-116

Kirjan kokonaissivumäärä: 489

Kustantaja: Springer

Kustannuspaikka: Cham

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Avoin saatavuus: Julkaisukanava ei ole avoin


Tiivistelmä

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.


YSO-asiasanat: oppiminen; opetus; opettajat; diskurssianalyysi

Vapaat asiasanat: learning analytics; teacher discourse analysis; concept graphs; centrality measures


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Alustava JUFO-taso: 1


Viimeisin päivitys 2020-07-09 klo 10:27