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However, these systems work best when learners remain engaged, and existing engagement detection research has rarely included neurodiverse populations. This study evaluates engagement in children with ADHD, autism, comorbid ADHD+autism, and no known diagnosis while they interact with a home-based digital literacy system, using parent reports and extracted acoustic features. Results link temporal, pitch, and vocal-quality measures to engagement across clinical groups.",{"@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/engagement-driven-differences-in-acoustic-features-during-online-reading-comprehension-conversations/585372/",{"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/engagement-driven-differences-in-acoustic-features-during-online-reading-comprehension-conversations/585372.png","ImageObject",300,407,{"name":92,"@type":93},"Aria Callaghan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How was reading engagement measured in the study?","Question",{"text":112,"@type":113},"Engagement was measured using parent reports while children interacted with a digital literacy learning system from home.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which acoustic features were found to relate to engagement?",{"text":117,"@type":113},"Acoustic measures related to temporal features, pitch, and vocal quality were associated with engagement levels across all clinical groups.",{"name":119,"@type":110,"acceptedAnswer":120},"Why do the results emphasize considering clinical diagnosis?",{"text":121,"@type":113},"Interaction effects showed that temporal features vary across different clinical groups, indicating diagnosis-specific considerations are important when measuring engagement.","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},585372,1792210591,{"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":24,"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,"preview_page_count":4},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Engagement-Driven Differences in Acoustic Features During Online Reading Comprehension Conversations  \nE. Margaret Perkoff1  \n[eperkoff@upenn.edu](eperkoff@upenn.edu)  \nRyan S. Baker3 [ryan.baker@adelaide.edu.au](ryan.baker@adelaide.edu.au)  \nAdam Porsch4 [adam.porsch@amiralearning.com](adam.porsch@amiralearning.com)  \nMegan Lyons2  \n[lyonsm3@chop.edu](lyonsm3@chop.edu)[ ](lyonsm3@chop.edu)Alan Gee4  \n[alan.gee@amiralearning.com](alan.gee@amiralearning.com)[ ](alan.gee@amiralearning.com)Neville Ryant6 [nryant@ldc.upenn.edu](nryant@ldc.upenn.edu)  \nAlexandria Mulqueen2  \n[mulqueena@chop.edu](mulqueena@chop.edu)  \nJaclyn Ocumpaugh5 [jocumpau@central.uh.edu](jocumpau@central.uh.edu)  \nJulia Parish-Morris2 [parishmorrisj@chop.edu](parishmorrisj@chop.edu)  \nLyle Ungar1  \n[ungar@upenn.edu](ungar@upenn.edu)  \n1 University of Pennsylvania 2 Children’s Hospital of Philadelphia 3Adelaide University  \n4Amira Learning 5 University of Houston 6 Linguistic Data Consortium  \nABSTRACT  \nThe prevalence of online reading platforms has surged along with improvements to automatic speech recognition (ASR) systems. These platforms facilitate personalized reading instruction, but like all learning systems, work best when students find them engaging. The Educational Data Mining (EDM) community has made great efforts to detect student engagement, but these efforts have largely been confined to school and lab environments without intentionally including neurodiverse populations. In the present study, we evaluate engagement levels in children with attention deficit hyperactivity disorder (ADHD, n=55), autism (n=38), both autism and ADHD (n=38) and with no known clinical diagnoses (n=22) as they interact with a digital literacy learning system from their homes. Engagement was measured using parent report, and acoustic features were extracted from audio recordings of the students while they interacted with the Amira literacy learning system. Our results indicate that acoustic measures related to temporal features, pitch, and vocal quality are related to engagement levels across all clinical groups. We also identify key temporal features that vary across different clinical groups. These findings demonstrate the validity of using non-invasive acoustic features to measure engagement levels during reading comprehension tasks at home. However, interaction effects also highlight the importance of taking into account children’s clinical diagnosis when measuring engagement.  \nKeywords  \nReading Engagement, Online Reading Comprehension, Neurodivergent  \nE. Margaret Perkoff, Megan Lyons, Alexandria Mulqueen, Ryan S. Baker, Alan Gee, Jaclyn Ocumpaugh, Adam Porsch, Neville Ryant, Julia Parish-Morris, and Lyle Ungar. Engagement-Driven Differences in Acoustic Features During Online Reading Comprehension Conversations. In Anthony Botelho, Maria Mercedes T. Rodrigo, Adish Singla, Hiroaki Ogata, Hyojeong So, and Young Hoan Cho (eds.) Proceedings of the 19th International Conference on Educational Data Mining, Seoul, Republic of Korea, June, 2026, pp. 90–103. International Educational Data Mining Society (2026) .  \n© 2026 Copyright is held by the author(s) . This work is distributed under the Creative Commons Attribution NonCommercial NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. [https://doi.org/10.5281/zenodo.21039919](https://doi.org/10.5281/zenodo.21039919)  \n1. INTRODUCTION  \nDigital reading platforms have emerged as a promising way to provide personalized literacy instruction to students, both at home and at school. These platforms guide the student through level-appropriate passages where they can read text out loud, receive real-time interventions, and respond to reading comprehension questions[69] . Compared to traditional instruction, Intelligent tutoring systems (ITSs) for reading can even produce larger learning gains [65, 70], even among students with learning differences that sometimes interfere with reading comprehension, such as Autism Spectrum","cbCaijxqsGgy9u78","https://ap.wps.com/l/cbCaijxqsGgy9u78","pdf",669703,14,"English","# Introduction\n## Digital reading platforms and personalized instruction\n## Definitions and dimensions of reading engagement\n## Limits of existing engagement measures","[{\"question\":\"How was reading engagement measured in the study?\",\"answer\":\"Engagement was measured using parent reports while children interacted with a digital literacy learning system from home.\"},{\"question\":\"Which acoustic features were found to relate to engagement?\",\"answer\":\"Acoustic measures related to temporal features, pitch, and vocal quality were associated with engagement levels across all clinical groups.\"},{\"question\":\"Why do the results emphasize considering clinical diagnosis?\",\"answer\":\"Interaction effects showed that temporal features vary across different clinical groups, indicating diagnosis-specific considerations are important when measuring engagement.\"}]","Engagement-Driven Differences in Acoustic Features During Online Reading Comprehension Conversations | PDF",1792054543,35]