[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-205403-105":59,"doc-detail-205403-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","using-university-enrolment-data-to-identify-underperforming-students","Using University Enrolment Data to Identify Underperforming Students","","A pilot program evaluates whether routinely collected enrolment data can predict students’ academic performance and enable early identification of subgroups needing additional support before semester start. The approach uses a sample of 4,951 students from Swinburne University’s student database, combining demographic variables with semester average marks. Findings show Australian-born NESB students underachieve relative to Australian-born ESB students, even after controlling for SES, age, and prior achievement (ATAR), though missing data limits reliable predictor detection. Recommended actions emphasize stricter data collection guidelines.",{"@graph":69,"@context":123},[70,84,106],{"@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-university-enrolment-data-to-identify-underperforming-students/205403/",{"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-university-enrolment-data-to-identify-underperforming-students/205403.png","ImageObject",300,407,{"name":92,"@type":93},"Jordan Avery","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What was the purpose of the pilot program?","Question",{"text":113,"@type":114},"To test whether enrolment data collected during admission can predict students’ academic performance and help identify groups needing support before the semester begins.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which student group showed lower achievement in the results?",{"text":118,"@type":114},"Australian-born NESB students achieved lower outcomes than Australian-born ESB students, even after controlling for SES, age, and ATAR.",{"name":120,"@type":111,"acceptedAnswer":121},"Why were predictor identification results limited?",{"text":122,"@type":114},"Missing data made it difficult to identify reliable predictors, leading to a recommendation for more stringent data collection guidelines.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},205403,1788579248,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"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":130,"read_time":144,"preview_page_count":4},1099523882367,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Using university enrolment data to identify underperforming students.  \nGlen W. Bates  \nMatthew Farrugia  \nJames S. Williams  \nSwinburne University of Technology  \nAbstract  \nA program was piloted to examine the feasibility of using data collected during enrolment to predict the academic performance of students. The rationale was that if data collected routinely were able to significantly predict academic outcomes, then this would provide an easy way to identify subgroups of students to whom additional support or assistance could be offered before the start of the semester. This is important because improved student performance will aid in preventing attrition, and will likely be related to increased student self-efficacy and student satisfaction. Participants (N = 4,951) were selected from a large student database system maintained by Swinburne University. Demographic data, and results data (average marks for the semester) were recorded. Results indicated that Australian born NESB students had lower achievement than Australian born ESB students. This was significant even controlling for SES, age, and previous educational achievement (i.e., ATAR). While promising, identification of predictors was difficult owing to missing data. Therefore, it is recommended that universities adopt more stringent data collection guidelines, in order to ensure that important predictors of student success are captured for all enrolled students.  \nBackground and rationale.  \nThe past few years have witnessed substantial changes to higher education (Buzwell et al., 2015) . In Australia, changes following the Bradley Report (Bradley, Noonan, Nugent,& Scales, 2008) led to a student cohort which is more diverse, and arguably more complex. Practically, this was achieved by uncapping student numbers at the start of 2012. This resulted in a higher proportion of students from lower SES areas, and students with lower ATARs (Australian Tertiary Admission Rank) (Norton, 2014) .  \nFollowing this, research has focused on exploring the factors which predict the success of this more diverse group of students. The two outcomes used in this research are student retention, and student’s academic performance. Additionally, there is evidence that academic performance is one predictor of retention (Grebennikov & Skaines, 2009), meaning that students who gain better marks in their university subjects are more likely to complete their university courses, and those who perform poorly are more likely to ‘drop-out’of their university courses.  \nWhile using retention as an outcome is appealing, this is made difficult because retention can often only be inferred. For example, in the ‘attrition, success and retention’appendix of the Australian Government Department of Education and Training (2014) student data collection, retention was classified as instances in which students who had been enrolled in a given year (x) were not enrolled one year later (x + 1) . This is problematic because the group of non-enrolling students is likely to be heterogonous, including, both students who have dropped out, and students who have taken a leave of absence but intend to  \nreturn to study. For this reason, academic performance reflects a more robust measure, especially in light of Grebennikov and Skaines assertion that performance is a signficant predictor of rention.  \nOf demographic predictors of student achievement, Richardson et al. (2012) found Socio-Economic Status ( SES) and age were both significant predictors, with older students outperforming younger student, and higher SES being associated with better outcomes. Messinis and Sheehan (2015) found that students coming from a non-English speaking background achieved worse outcomes than students from English speaking backgrounds, although the impact of this was mediated by SES, with a non-English speaking background having a larger impact on low SES than high SES students. Wurf and Croft-Piggin (2015) found that ATAR scores were a s","cbCaiv9utWGeNSlU","https://ap.wps.com/l/cbCaiv9utWGeNSlU","pdf",117843,"English","# Abstract\n# Background and rationale\n## Student cohort changes in higher education\n## Outcomes: retention and academic performance\n# Method\n## Participants & procedure","[{\"question\":\"What was the purpose of the pilot program?\",\"answer\":\"To test whether enrolment data collected during admission can predict students’ academic performance and help identify groups needing support before the semester begins.\"},{\"question\":\"Which student group showed lower achievement in the results?\",\"answer\":\"Australian-born NESB students achieved lower outcomes than Australian-born ESB students, even after controlling for SES, age, and ATAR.\"},{\"question\":\"Why were predictor identification results limited?\",\"answer\":\"Missing data made it difficult to identify reliable predictors, leading to a recommendation for more stringent data collection guidelines.\"}]","Using University Enrolment Data to Identify Underperforming Students | PDF",13]