[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125290-en":3,"doc-seo-125290-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125290,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Student Academic Outcome Using Online Behavioural Statistics and Neighbourhood Influences - A Machine Learning Approach","Universities use scheduled student census checks to maintain accurate academic records, supported by local monitoring of disengaging students. Attendance-based identification of at-risk learners is often unreliable because attendance alone cannot foresee achievement. This study uses virtual learning analytics from Blackboard—login frequency, time on content, and related module indicators—together with area-based neighbourhood influence data. Data from 160 students across four modules at week 6 predicts pass/fail at week 12 via machine learning, outperforming attendance-based methods and indicating the value of online behavioural statistics.","SN Computer Science (2025) 6:661  \n[https://doi.org/10.1007/s42979-025-04189-6](https://doi.org/10.1007/s42979-025-04189-6)  \nPredicting Student Academic Outcome Using Online Behavioural Statistics and Neighbourhood Influences: A Machine Learning Approach  \nNonso Nnamoko1 · Joseph Barrowclough1 · Babatunde Onikoyi1,2 · Mark Liptrott1  \nReceived: 23 May 2025 / Accepted: 4 July 2025 © The Author(s) 2025  \nAbstract  \nUniversities normally conduct student census checks at designated points in the academic calendar to ensure accurate central records. These checks complement local systems that monitor disengaging students, which can negatively impact learning experience, grades, and the overall retention. Local monitoring often relies on student attendance, typically below a certain threshold (say 50%) across all registered modules to identify at-risk students. However, this simplistic approach may not be reliable as attendance alone cannot predict academic outcomes. Virtual learning platforms like Blackboard provides access to online behavioural statistics beyond attendance, including login frequency, time spent on content and other module-related indicators. Personal and environmental factors may also affect students’ ability to attend. Thankfully, indicators of students’family and neighbourhood influences can be obtained from the Office for Students’ area-based classification of young people’s participation and under-representation in higher education. Online behavioural statistics were collected halfway through a 12-week semester from 160 students across 4 computer science modules on Blackboard. Area-based classification data was also collected for each student. The aim was to predict, at week 6, the likelihood of each student passing or failing by semester end (week 12). Two variations of the dataset—(a) online behavioural statistics only, and (b) combination of online behavioural statistics and neighbourhood influence—were used to train and evaluate the performance of several machine learning algorithms in predicting (at week 6) the likely outcome (pass or fail) for each student at the end of the semester. The predictive performance was measured in terms of macro f-score and compared with traditional attendance-based method of identifying at-risk students. Machine learning predictions using only online behavioural statistics resulted in a better performance (macro f-score = 81%) compared to using a combination of online behavioural statistics and neighbourhood influence (macro f-score = 79%) . Further comparison shows 14% superiority of the better machine learning predictions over the traditional attendance-based approach (macro f-score = 67%) . The results demonstrate literature suggestions that online behavioural statistics are valid indicators for predicting student success (or failure) . Further research is required to establish effective use of neighbourhood influences as a complementary element.  \nKeywords Online behavioural statistics · Student retention · Machine learning · Academic performance · Student neighbourhood influence · Virtual learning environment  \nIntroduction  \nIn an increasingly competitive and results-driven higher education (HE) landscape, student retention and completion rates are important performance measures of HE Institutions (HEIs) in the United Kingdom (UK) as they look to decrease revenue loss through improved graduation rates; but more importantly meet the conditions of HEI registration  \nExtended author information available on the last page of the article  \nset out by the Office for Students 1 [44] . Specifically, ‘student continuation and completion’ is one of the indicators assessed during registration under condition B3 of the OfS regulator framework [45, p. 88] . Thus, UK HEIs are always looking for innovative ways of identifying students at risk of dropping out.  \nResearch into student retention has identified a myriad of reasons why students withdraw from their chosen programme  \n[1](1","cbCaifmUfWH5JsuB","https://ap.wps.com/l/cbCaifmUfWH5JsuB","pdf",3211815,1,22,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is attendance-based monitoring insufficient for predicting student academic outcomes?\",\"answer\":\"Attendance alone cannot reliably predict academic results, as other documented factors influence disengagement and grades.\"},{\"question\":\"What data sources were used in the prediction study?\",\"answer\":\"Online behavioural statistics from Blackboard and area-based classification data reflecting neighbourhood influences were collected for each student.\"},{\"question\":\"How was student success or failure predicted in the study?\",\"answer\":\"Models were trained at week 6 to predict pass/fail by week 12, using either online behavioural statistics alone or a combination with neighbourhood influence.\"}]","Predicting Student Academic Outcome Using Online Behavioural Statistics and Neighbourhood Influences - A Machine Learning Approach | PDF",1785897986,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-student-academic-outcome-using-online-behavioural-statistics-and-neighbourhood-influences-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-student-academic-outcome-using-online-behavioural-statistics-and-neighbourhood-influences-a-machine-learning-approach/125290/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is attendance-based monitoring insufficient for predicting student academic outcomes?","Question",{"text":75,"@type":76},"Attendance alone cannot reliably predict academic results, as other documented factors influence disengagement and grades.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources were used in the prediction study?",{"text":80,"@type":76},"Online behavioural statistics from Blackboard and area-based classification data reflecting neighbourhood influences were collected for each student.",{"name":82,"@type":73,"acceptedAnswer":83},"How was student success or failure predicted in the study?",{"text":84,"@type":76},"Models were trained at week 6 to predict pass/fail by week 12, using either online behavioural statistics alone or a combination with neighbourhood influence.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]