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Data were collected from 26,857 posts from a South Korean self-generated teacher CoP, “Indischool”, and analyzed using natural language processing. Text classification employed word2vec, BERT, and machine-learning classifiers; BERT embeddings with a classifier achieved the best F1 score of 0.756. Topic modeling further revealed PCK distinctions between high- and low-interaction posts, showing deeper reflections in high-interaction contributions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-natural-language-processing-to-analyze-elementary-teachers-mathematical-pedagogical-content-knowledge-in-online-community-of-practice/600284/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-natural-language-processing-to-analyze-elementary-teachers-mathematical-pedagogical-content-knowledge-in-online-community-of-practice/600284.png","ImageObject",300,407,{"name":92,"@type":93},"RuangKosong","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main purpose of the study?","Question",{"text":112,"@type":113},"To investigate how elementary teachers’ mathematics pedagogical content knowledge (PCK) differs based on their interaction levels in an online teacher community of practice.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the dataset collected and processed?",{"text":117,"@type":113},"The study used 26,857 posts collected from the South Korean online teacher CoP “Indischool” and analyzed them with natural language processing methods.",{"name":119,"@type":110,"acceptedAnswer":120},"Which method performed best for predicting interaction level?",{"text":121,"@type":113},"BERT embeddings with a machine-learning classifier showed the best performance, reaching an F1 score of 0.756.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},600284,1792105487,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090883219,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","OPEN ACCESS  \nResearch Article  \nContemporary Educational Technology, 2023, 15(3), ep438 ISSN: 1309-517X (Online)  \nUsing natural language processing to analyze elementary teachers’ mathematical pedagogical content knowledge in online community of practice  \nJiseung Yoo 1  \n 0000-0002-2613-6446  \nMin Kyeong Kim 1*  \n 0000-0002-6788-9890  \n1 Ewha Womans University, Seoul, SOUTH KOREA  \n* [Corresponding author:](Corresponding author: mkkim@ewha.ac.kr)[ mkkim@ewha.ac.kr](Corresponding author: mkkim@ewha.ac.kr)  \nCitation: Yoo, J., & Kim, M. K. (2023) . Using natural language processing to analyze elementary teachers’ mathematical pedagogical content knowledge in online community of practice. Contemporary Educational Technology, 15(3), ep438 .  \n[https://doi.org/10.30935/cedtech/13278](https://doi.org/10.30935/cedtech/13278)  \nARTICLE INFO ABSTRACT  \n\n| Received: 3 Mar 2023\u003Cbr>Accepted: 1 May 2023 | This study focuses on how teachers’ pedagogical content knowledge (PCK) of mathematics may differ depending on teacher interactions in an online teacher community of practice (CoP) . The study utilizes data from 26,857 posts collected from the South Korean self-generated online teacher CoP, ‘ Indischool’. This data was then analyzed using natural language processing techniques; specifically, text classification with word2vec, BERT, and machine learning classifiers was used. The results indicate that the texts of posts can predict the level of teacher interactions in the online CoP. BERT embedding and classifier exhibited the best performance, ultimately achieving an F1 score of .756. Moreover, topic modeling utilizing BERT embedding is used to uncover the specific PCK of teachers through high-and low-interaction posts. The results reveal that high-interaction posts with numerous likes and replies demonstrate more in-depth reflections on teaching mathematics and refined PCK. This study makes two significant contributions. First, it applies a data science framework that allows for the analysis of real data from an actual online teacher community. Secondly, it sheds light on the intricacies of knowledge management in an online teacher CoP, an area that has to this point received limited empirical attention.\u003Cbr>Keywords: online community of practice, natural language processing, mathematics pedagogical content knowledge, knowledge management |\n| --- | --- |\n| INTRODUCTION |  |\n\nWith the technological advancements that have been made in the last several years, online teacher communities have rapidly proliferated as a form of web-based networking for purposes ranging from teacher professional development to bonding and trust (Greenhow et al., 2020; Lantz-Andersson et al., 2018; Macià & Garcia, 2016) . Through the active participation of teachers and the development of platforms such as social media, these online communities, which provide a way for teachers to connect beyond the constraints of traditional face-to-face communities, have come to develop their own distinct roles and functions. As a result, there has been a wide range of research aiming to understand these developments by examining aspects of teacher learning in communities (Booth, 2012; Rehm & Notten, 2016; Tseng & Kuo, 2014), their relationship to teaching and learning practices (Goodyear et al., 2014; Hu et al., 2018), emotional ties and support (Davis, 2013; Trust, 2015), and networks in online teacher communities (Gao et al., 2017; Xie & Luo, 2019) .  \nOnline teacher communities share a unique characteristic in that most of their users are working teachers. Online experiences naturally influence teachers’ professional practices in their classrooms, and their in-class activities also affect online activities. Informal learning experiences allow participants in online communities  \nto hone their skills as they expand their tacit knowledge with accumulated practices and discussion (Faraj et al., 2011) . However, previous research has also pointed out some limitations in teache","cbCaibKZoPptENMq","https://ap.wps.com/l/cbCaibKZoPptENMq","pdf",490826,15,"English","# Introduction\n## Online teacher communities and learning\n## Online community of practice (CoP)","[{\"question\":\"What is the main purpose of the study?\",\"answer\":\"To investigate how elementary teachers’ mathematics pedagogical content knowledge (PCK) differs based on their interaction levels in an online teacher community of practice.\"},{\"question\":\"How was the dataset collected and processed?\",\"answer\":\"The study used 26,857 posts collected from the South Korean online teacher CoP “Indischool” and analyzed them with natural language processing methods.\"},{\"question\":\"Which method performed best for predicting interaction level?\",\"answer\":\"BERT embeddings with a machine-learning classifier showed the best performance, reaching an F1 score of 0.756.\"}]","Using natural language processing to analyze elementary teachers’ mathematical pedagogical content knowledge in online community of practice | PDF",1792090388,38]