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Structural topic modeling (STM), a natural language processing approach, analyzes how participants with different equity attitudes respond across four simulations. Participants with less equitable beliefs converged toward behavior patterns of more equitable participants (ES=1.08 SD). Related evidence included changes in equity mindsets (ES=0.88 SD) and self-reported equity-promoting practices (ES=0.32 SD).",{"@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/measuring-equity-promoting-behaviors-in-digital-teaching-simulations-a-topic-modeling-approach/590095/",{"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/measuring-equity-promoting-behaviors-in-digital-teaching-simulations-a-topic-modeling-approach/590095.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What approach does the study use to analyze simulation responses?","Question",{"text":112,"@type":113},"The study applies structural topic modeling (STM), a natural language processing method that does not require pre-labeled data.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How many simulations and participants were included in the evaluation?",{"text":117,"@type":113},"Participants completed a sequence of four digital equity teaching simulations, drawn from an MOOC with 963 educators.",{"name":119,"@type":110,"acceptedAnswer":120},"What did the study find about participants with less equitable beliefs?",{"text":121,"@type":113},"Their simulation behaviors converged to be more similar to the behavior of participants with more equitable beliefs (ES=1.08 SD), supported by changes in mindsets and self-reported practices.","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},590095,1792189580,{"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":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},1374404737137,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","AERA Open  \nJanuary-December 2021, Vol. 7, No. 1, pp. 1–19 DOI: 10. 1177/23328584211045685  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2021. [https://journals.sagepub.com/home/ero](https://journals.sagepub.com/home/ero)  \nMeasuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach  \nJoshua Littenberg-Tobias  \nElizabeth Borneman  \nJustin Reich  \nMassachusetts Institute of Technology  \nDiversity, equity, and inclusion (DEI) issues are urgent in education. We developed and evaluated a massive open online course (N = 963) with embedded equity simulations that attempted to equip educators with equity teaching practices. Applying a structural topic model (STM)—a type of natural language processing (NLP)—we examined how participants with different equity attitudes responded in simulations. Over a sequence of four simulations, the simulation behavior of participants with less equitable beliefs converged to be more similar with the simulated behavior of participants with more equitable beliefs (ES [effect size] = 1.08 SD). This finding was corroborated by overall changes in equity mindsets (ES = 0.88 SD) and changed in self-reported equity-promoting practices (ES = 0.32 SD). Digital simulations when combined with NLP offera compelling approach to both teaching about DEI topics and formatively assessing learner behavior in large-scale learning environments.  \nKeywords: diversity, equity, inclusion, simulations, MOOCs, natural language processing, structural topic modeling  \nAn increasing number of organizations are investing significant resources in diversity, equity, and inclusion (DEI) training to address issues of bias and racist behavior (Carter et al., 2020). Many U.S. K–12 schools and organizations have also adopted DEI professional education to address issues ofsystemic racism. However, there is very little empirical evidence that these types of interventions lead to substantive changes in educators’ teaching practice (Bravo et al., 2014; Civitillo et al., 2018; Ehrke et al., 2020; Love, 2019) . DEI professional education often emphasizes broad, theoretical concepts that teachers have difficult transferring into practice (Emdin, 2011) .  \nSimulations hold promise as a pedagogical approach for professional learning on equity-based teaching practices. Simulations have been used successfully in teacher education to approximate various teaching scenarios and support the transfer learning into new situations (Dalinger et al., 2020; Dotger & Ashby, 2010; Kaufman & Ireland, 2016) . Increasingly, simulations are being used to help prepare teachers for equity-based teaching (G. A. Chen, 2020; J. A. Chen et al., 2020; Cohen et al., 2020; Self & Stengel, 2020) . However, because of the difficulty of analyzing large amounts of text it can be difficult to assess participants’ simulation performance. Accordingly, most studies of simulation have either limited their analysis to a small number of participants (G. A. Chen, 2020; Robinson et al., 2018; Self, 2016) or have focused on participants’ reactions to the simulations rather  \nthan behavior in the simulations themselves (Bautista & Boone, 2015; J. A. Chen et al., 2020; Dalinger et al., 2020; Girod & Girod, 2006) .  \nIn our study, we describe a novel method of analyzing participants’text-based responses to simulations using natural language processing (NLP) tools. We focus on data collected from a series of four digital equity teaching simulations that were given to participants within a massive open online course (MOOC) for K–12 classroom educators on equity teaching practices (N = 963) . In the simulations, participants responded using oral or written text, and they produced more than 30,000 rows of unstructured text data. Using structural topic modeling (STM; Roberts et al., 2014)—an NLP method that does not require prelabeled data—we analyzed partic","cbCaiuqj3xkqkxUZ","https://ap.wps.com/l/cbCaiuqj3xkqkxUZ","pdf",1019167,"English","# Measuring Equity-Promoting Behaviors in Digital Teaching Simulations\n## DEI context and limitations of prior evidence\n## Simulation-based professional learning and analysis challenges\n## Method: structural topic modeling with NLP\n## Findings: convergence, mindsets, and self-reported practices","[{\"question\":\"What approach does the study use to analyze simulation responses?\",\"answer\":\"The study applies structural topic modeling (STM), a natural language processing method that does not require pre-labeled data.\"},{\"question\":\"How many simulations and participants were included in the evaluation?\",\"answer\":\"Participants completed a sequence of four digital equity teaching simulations, drawn from an MOOC with 963 educators.\"},{\"question\":\"What did the study find about participants with less equitable beliefs?\",\"answer\":\"Their simulation behaviors converged to be more similar to the behavior of participants with more equitable beliefs (ES=1.08 SD), supported by changes in mindsets and self-reported practices.\"}]","Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach | PDF",1792068783,48]