A semantic classification of nominal technical terms in secondary school biology textbooks

Nominal technical terms (NTTs), as crucial builders for disciplinary knowledge, can cause difficulties for students. However, previous studies have rarely associated NTTs with disciplinary knowledge construction. Apart from that, scholars of English for Specific Purposes (ESP) have mainly focused on making wordlists for one specific discipline based on corpora rather than on meanings. Moreover, the current categorization of technical terms cannot reveal their role in constructing disciplinary knowledge. Against this backdrop, we carried out a corpus-based study to classify NTTs in secondary school biology textbooks and to unveil the knowledge constructed by different types of those NTTs. As a result, we found that NTTs in those textbooks could fall into five major categories: Thing, Activity, Semiotic, Place, and Time. We also found intra-disciplinary differences in NTT distributions. The lexicogrammatical analysis indicates that the five types of NTTs can construct different knowledge. With the findings, we put forward implications for teaching biology in secondary schools.

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Publication Details

Journal
PLoS ONE
Published
2024-11-11
DOI
https://doi.org/10.1371/journal.pone.0312040
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A semantic classification of nominal technical terms in secondary school biology textbooks

Rurong Le, Xianhe Zhang, Shengquan Yu
PLoS ONE
Natural Language Processing Techniques
article

A semantic classification of nominal technical terms in secondary school biology textbooks

Rurong Le, Xianhe Zhang, Shengquan Yu
article en

Abstract

Nominal technical terms (NTTs), as crucial builders for disciplinary knowledge, can cause difficulties for students. However, previous studies have rarely associated NTTs with disciplinary knowledge construction. Apart from that, scholars of English for Specific Purposes (ESP) have mainly focused on making wordlists for one specific discipline based on corpora rather than on meanings. Moreover, the current categorization of technical terms cannot reveal their role in constructing disciplinary knowledge. Against this backdrop, we carried out a corpus-based study to classify NTTs in secondary school biology textbooks and to unveil the knowledge constructed by different types of those NTTs. As a result, we found that NTTs in those textbooks could fall into five major categories: Thing, Activity, Semiotic, Place, and Time. We also found intra-disciplinary differences in NTT distributions. The lexicogrammatical analysis indicates that the five types of NTTs can construct different knowledge. With the findings, we put forward implications for teaching biology in secondary schools.

PLoS ONEVol. 19(11)
Guangdong University of Foreign Studies (CN), Xiamen University (CN), Guangzhou University (CN), Tan Kah Kee Innovation Laboratory (CN)
Guangzhou University
Quality Education
Openalex Percentile: Top 82%
Natural Language Processing Techniques
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