[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123941-en":3,"doc-seo-123941-105":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},123941,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine Learning-Based Software to Group Heterogeneous Students for Online Peer Assessment Activities","Since 2017/2018, an online peer assessment activity was embedded in the Genomics laboratory for a master’s course in Biological Sciences at the University of Camerino to strengthen learning outcomes and key soft skills such as teamwork, communication, and critical thinking. Random Moodle-based grouping often produced unbalanced cohorts, failing to ensure heterogeneity and leading some students to perceive peer review negatively, reducing engagement and motivation. This work proposes a machine-learning approach and a dedicated software tool to form effective heterogeneous groups, then evaluates its impact across two editions by analyzing students’ results and perceptions.","Machine-learning-based software to group heterogeneous students for online peer assessment activities  \nDaniela Amendola1[0000-0003-3039-260X], Giacomo Nalli1 [0000-0002-5667-3429] and Cristina Miceli1[0000-0002-7829-8471]  \n1 University of Camerino, Camerino, Via Andrea D’Accorso 16-62032, Italy  \n[daniela.amendola@unicam.it](daniela.amendola@unicam.it)  \nAbstract. Since the academic year 2017/2018, a peer assessment activity was included in the online Genomics laboratory for the master’s degree course in Biological Sciences of the University of Camerino, with the aim of improving learning outcomes and soft skills in students, such as team building and critical thinking. Creating groups in university courses is not easy because of the large number of students, that leads teachers to realize groups totally randomly, a procedure that is not always effective. One of the factors that influences the success of collaborative learning is the creation of heterogeneous groups based on the students’behaviors. Despite little improvements, the online genomics laboratory highlighted some gaps. Random groups didn’t ensure that each group was composed of heterogeneous students, and it leads some students to have a bad perception of the peer review activity, negatively affecting their engagement and motivation.  \nThis work proposes a new Machine Learning Approach and the realization of a specific software, able to create effective heterogeneous groups to be involved in the online peer assessment process, in order to improve learning outcomes and satisfaction in the students. The aim is to check the improvement of the peer assessment effectiveness using heterogeneous groups compared to random groups of students. Two editions of the online laboratory of Genomics were analysed, examining the students’ results and perceptions to verify the impact of the Machine Learning approach designed in this work.  \nKeywords: On-line Peer Assessment, Working group, Machine learning.  \n1 Introduction  \nUniversities promote innovative teaching that allows an improvement of learning in terms of knowledge and soft skills, including the student as an active actor in the training process.  \nCollaborative activities such as peer assessment are effective teaching methodologies since they improve learning outcomes by promoting active learning [1]. They also develop the students’ social skills such as decision making, communication, collaborative and critical thinking [2][3] .  \nPeer assessment is a collaborative learning technique based on a critical analysis by learners of a task or artefact previously undertaken by peers [4]. In the peer assessment process, students reciprocally express a critical judgment about the way their peers performed a task assigned by the teacher and give a grade to it. Furthermore, students provide their peers with detailed qualitative feedback to guide and help them in the constructive revision of their work for the teacher evaluation.  \nTo produce the feedback, the students use a rubric [5], which is a schema of the criteria for assigning marks for each step of the task. The rubric is usually prepared by the teacher in collaboration with the students themselves, thus promoting metacognitive reflection on the quality of the task or artefact to be produced.  \nThe literature shows how peer assessment supports and improves learning, both for the students who receive the feedback and those who give it, because the activity triggers self-assessment and critical reasoning with a focus on the tasks produced by both [6] .  \nSince the academic year 2017/2018, a collaborative activity of peer assessment, used as an evaluation process with a training function, was included in the online laboratory of Genomics for the master’s degree course in Biological Sciences of the University of Camerino (Italy), thanks to the use of digital technologies. This experimental procedure was entirely conducted online, using the University's Moodle e-learning platfor","cbCaisXZYNM2bsfH","https://ap.wps.com/l/cbCaisXZYNM2bsfH","pdf",504189,1,14,"English","en",105,"# Introduction\n## Peer assessment in online collaborative learning\n## Limits of random grouping in the Genomics laboratory\n## Role of heterogeneity in group success\n## Proposed machine learning approach and evaluation (overview)","[{\"question\":\"What problem does random student grouping create in online peer assessment?\",\"answer\":\"Random groups may not ensure heterogeneity in students’ knowledge and behaviors, which can lead to unbalanced teams. This imbalance can negatively affect some students’ perception of the peer review activity and reduce engagement and motivation.\"},{\"question\":\"What does the proposed work contribute?\",\"answer\":\"It presents a new machine learning approach and implements specific software to automatically create effective heterogeneous groups. The goal is to improve learning outcomes and student satisfaction in online peer assessment.\"},{\"question\":\"How is the machine learning approach evaluated?\",\"answer\":\"The study analyzes two editions of the online Genomics laboratory. It compares student results and perceptions to verify the impact of heterogeneous groups created with the machine learning approach versus random groups.\"}]","Machine Learning-Based Software to Group Heterogeneous Students for Online Peer Assessment Activities | PDF",1785819361,35,{"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},"machine-learning-based-software-to-group-heterogeneous-students-for-online-peer-assessment-activities","",{"@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/machine-learning-based-software-to-group-heterogeneous-students-for-online-peer-assessment-activities/123941/",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","2026-08-04",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},"What problem does random student grouping create in online peer assessment?","Question",{"text":75,"@type":76},"Random groups may not ensure heterogeneity in students’ knowledge and behaviors, which can lead to unbalanced teams. This imbalance can negatively affect some students’ perception of the peer review activity and reduce engagement and motivation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed work contribute?",{"text":80,"@type":76},"It presents a new machine learning approach and implements specific software to automatically create effective heterogeneous groups. The goal is to improve learning outcomes and student satisfaction in online peer assessment.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the machine learning approach evaluated?",{"text":84,"@type":76},"The study analyzes two editions of the online Genomics laboratory. It compares student results and perceptions to verify the impact of heterogeneous groups created with the machine learning approach versus random groups.","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"]