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This work introduces engagement forecasting as a supervised prediction task from ITS logs, targeting weekly minutes practiced and weekly new skills mastered. Using data from 425 middle-school students across a school year, the study benchmarks fifteen predictive models and shows 22–33% MAE reductions over heuristic baselines, with explainability via feature importance and tutor interview analysis.",{"@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/from-heuristics-to-analytics-forecasting-effort-and-progress-in-online-learning/585380/",{"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/from-heuristics-to-analytics-forecasting-effort-and-progress-in-online-learning/585380.png","ImageObject",300,407,{"name":92,"@type":93},"Aria Callaghan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does engagement forecasting predict in this study?","Question",{"text":112,"@type":113},"It predicts a learner’s next-week engagement level using prior ITS interaction traces, focusing on minutes practiced per week and new skills mastered per week.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the forecasting approach evaluated?",{"text":117,"@type":113},"The paper benchmarks fifteen predictors using interaction logs from 425 middle-school students over a school year and compares model error against heuristic baselines.",{"name":119,"@type":110,"acceptedAnswer":120},"What explains differences between effort and progress predictions?",{"text":121,"@type":113},"Feature importance and ablation analyses show effort forecasting is driven mainly by recent activity features, while progress forecasting relies more on learner-state and content-difficulty signals.","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},585380,1792398867,{"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":81,"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","From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning  \nEric S. Qiu  \nCornell University Ithaca, NY, USA  \n[sq225@cornell.edu](sq225@cornell.edu)  \nDanielle R. Thomas  \nCarnegie Mellon University Pittsburgh, PA, USA  \n[drthomas@cmu.edu](drthomas@cmu.edu)  \nBoyuan Guo  \nCarnegie Mellon University Pittsburgh, PA, USA  \n[boyuang@andrew.cmu.edu](boyuang@andrew.cmu.edu)  \nVincent Aleven Carnegie Mellon University  \nPittsburgh, PA, USA [aleven@cs.cmu.edu](aleven@cs.cmu.edu)  \nABSTRACT  \nSustained effort is essential for realizing the benefits of intelligent tutoring systems (ITS), yet many learners disengage or underuse available practice time. We introduce engagement forecasting as a supervised prediction task based on ITS logs, targeting two outcomes central to effort and learning progress: minutes practiced per week and new skills mastered per week. Using interaction log data from 425 middleschool students over a school year, we benchmark fifteen predictors including regressions, decision trees, and neural networks. We show that these feature-based models reduce mean absolute error (MAE) by 22–33% relative to heuristic baselines, including fixed-percentile rules adapted from prior work in other behavioral domains. We find that percentile heuristics systematically overpredict, whereas feature-based models better track student practice trajectories across weeks. To support explainability, we analyze feature importance and ablations, revealing target-specific patterns: effort forecasting is driven mainly by recent activity features, while progress forecasting depends more on learner-state and content difficulty signals. Finally, in a semi-structured user interview case study with eight college tutors, we examine how tutors reasoned about system-generated predictive features when setting goals with students. We find that tutors reasoned differently about effort versus progress goals in ways that mirror our pattern analysis. Together, these results establish a reproducible benchmark for forecasting weekly effort and learning progress in ITS. By making patterns of sustained effort and progress visible at a weekly timescale, engagement forecasting offers a foundation for supporting tutor–learner goal setting and timely instructional decisions.  \nKeywords  \nEffort Regulation, Intelligent Tutoring Systems, Machine  \nEric S. Qiu, Danielle R Thomas, Boyuan Guo, Vincent Aleven, and Conrad Borchers. From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning. 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. 117–129. 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.21039913](https://doi.org/10.5281/zenodo.21039913)  \nConrad Borchers Carnegie Mellon University  \nPittsburgh, PA, USA[cborcher@cs.cmu.edu](cborcher@cs.cmu.edu)  \nLearning, K-12  \n1. INTRODUCTION  \nActive learning systems that scaffold student problem solving can substantially improve learning outcomes, especially when learners engage in high volumes of practice with feedback over several months [36, 33] . Yet, persistence and effort remain bottlenecks: many students disengage after initial practice or do not utilize the class time they have at their disposal, limiting these systems’ long-term impact [25, 31] . In large-scale deployments, engagement is often highly skewed—for example, only a small minority of students may reach recommended weekly usage levels [29, 24, 22] . This disparity in engagement motivates a central question: how can analytics predict and explain differences in student effort to generate actionable, learner- and tea","cbCaihlvbAscAX35","https://ap.wps.com/l/cbCaihlvbAscAX35","pdf",898472,13,"English","# Abstract\n# 1. Introduction\n## Engagement forecasting targets\n## Why weekly engagement is actionable","[{\"question\":\"What does engagement forecasting predict in this study?\",\"answer\":\"It predicts a learner’s next-week engagement level using prior ITS interaction traces, focusing on minutes practiced per week and new skills mastered per week.\"},{\"question\":\"How was the forecasting approach evaluated?\",\"answer\":\"The paper benchmarks fifteen predictors using interaction logs from 425 middle-school students over a school year and compares model error against heuristic baselines.\"},{\"question\":\"What explains differences between effort and progress predictions?\",\"answer\":\"Feature importance and ablation analyses show effort forecasting is driven mainly by recent activity features, while progress forecasting relies more on learner-state and content-difficulty signals.\"}]","From Heuristics to Analytics - Forecasting Effort and Progress in Online Learning | PDF",1792054561,33]