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Using quantitative research and classification tree data-analytics models, it finds employed and married learners outperform peers. Keller’s ARCS (Attention, Relevance, Confidence, Satisfaction) framework highlights course relevance and satisfaction as key motivation drivers. The study also examines why employed learners sustain motivation and how unmet expectations affect academic performance.",{"@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/influence-of-socio-demographic-profile-on-the-motivational-characteristics-and-academic-performances-of-distance-learners-kellers-arcs-framework-and-data-analytics-study/592019/",{"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/influence-of-socio-demographic-profile-on-the-motivational-characteristics-and-academic-performances-of-distance-learners-kellers-arcs-framework-and-data-analytics-study/592019.png","ImageObject",300,407,{"name":92,"@type":93},"Mabel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which learner groups showed better academic performance in distance education?","Question",{"text":112,"@type":113},"The classification tree models indicate that employed and married learners perform better in distance education, compared with others.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the Keller’s ARCS framework explain motivation in the study?",{"text":117,"@type":113},"The ARCS analysis shows that course relevance and learners’ satisfaction while pursuing study are the two most important factors in maintaining motivation and improving academic performance.",{"name":119,"@type":110,"acceptedAnswer":120},"Why do the authors argue that motivation and satisfaction affect course completion?",{"text":121,"@type":113},"In the ODL setting, learners manage studies largely in isolation; therefore motivation helps them set goals, work consistently, and seek help when needed, while satisfaction supports a more effective learning environment and helps sustain motivation, influencing retention and completion.","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},592019,1792190765,{"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":19,"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},7971474920318,"https://ap-avatar.wpscdn.com/avatar/74000ee537e6b0ca360?x-image-process=image/resize,m_fixed,w_180,h_180&k=1788430171632181674","Influence of Socio-Demographic Profile on the Motivational Characteristics and Academic Performances of Distance Learners  \nYasmin  \nAbstract: The paper explores and assesses, through quantitative research and data analytics models, as to how socio-demographic profile, work, and family responsibilities may be associated with motivational level and educational attainment or course completion rate of learners enrolled in the undergraduate courses of Indira Gandhi National Open University (IGNOU), the largest distance education University in India. The models, generated using classification tree methods, show that employed and married learners perform better in distance education. Assessing underlying reasons, an empirical study using the Keller’s ARCS (Attention, Relevance, Confidence, Satisfaction) framework reveals that course relevance and level of satisfaction while pursuing the study are the two most important factors in keeping distance learners motivated and achieving better academic performance. The quantitative survey also shows that the employed learners have significantly higher levels of motivation, while the others may have difficulty in maintaining their self-motivation during distance learning. The findings are important for Open and Distance Learning (ODL) institutions to understand students' varied expectations of the distance learning experience and the consequences on their motivation levels and academic performances when these expectations are not met.  \nKeywords: Academic Performance, ARCS Model, Classification Tree , Data Analytics, Educational Data Mining, Motivation, Learners’ satisfaction, Open and Distance Learning, Socio-demographic Profile , Generative research  \nHighlights  \nWhat is already known about this topic:  \n• The underlying reasons for better academic performance in distance education are related to learners’ motivation.  \n• Low motivation levels are also contributing factor to low retention rates in distance education.  \n• Learners’ satisfaction plays a pivotal role in improving motivation for distance learners. What this paper contributes:  \n• This study employs a hybrid research methodology deploying the undergraduate student database of IGNOU learners as a data source for the generative research using classification tree models for formulating hypotheses.  \n• These hypotheses are then validated through evaluative research by analysing the empirical data obtained through a structured questionnaire sample.  \n• This paper identifies key demographic variables that potentially influence motivation level of distance learners.  \nImplications for theory, practice and/or policy:  \n• The findings of this paper provide valuable insights in understanding students’ varied expectations from the distance learning experience and the consequences on their motivation and drop out levels when these expectations are not met adequately.  \n• The findings can be utilised for initiating appropriate policy and programmatic interventions for enhancing motivational level of distance learners and thus reducing their dropout rate.  \n• The paper demonstrates the potential of data analytics as a generative research tool in the domain of Distance Education.  \nIntroduction  \nIn the Open and Distance Learning (ODL) system, the learners’ active involvement, enduring commitment and academic success depends upon their motivation level. The ODL learners have greater responsibility for their own learning as they study in isolation with limited support from teachers or peer groups (Schamber, 1994) . In such a challenging situation, motivation plays a crucial role in ensuring that learners take an active role in their studies, set achievable goals, and work consistently towards achieving them.  \nODL learners are generally of a higher age group, having greater work and family responsibilities than students enrolled in on-campus courses (Harris & Gibson, 2006; Ortagus, 2017) . Multiple roles of ODL learners are often attributed t","cbCaiaingpHwjJa7","https://ap.wps.com/l/cbCaiaingpHwjJa7","pdf",360890,15,"English","# Abstract\n# Highlights\n## What is already known about this topic\n## What this paper contributes\n## Implications for theory, practice and/or policy\n# Introduction\n# Literature","[{\"question\":\"Which learner groups showed better academic performance in distance education?\",\"answer\":\"The classification tree models indicate that employed and married learners perform better in distance education, compared with others.\"},{\"question\":\"How does the Keller’s ARCS framework explain motivation in the study?\",\"answer\":\"The ARCS analysis shows that course relevance and learners’ satisfaction while pursuing study are the two most important factors in maintaining motivation and improving academic performance.\"},{\"question\":\"Why do the authors argue that motivation and satisfaction affect course completion?\",\"answer\":\"In the ODL setting, learners manage studies largely in isolation; therefore motivation helps them set goals, work consistently, and seek help when needed, while satisfaction supports a more effective learning environment and helps sustain motivation, influencing retention and completion.\"}]","Influence of Socio-Demographic Profile on the Motivational Characteristics and Academic Performances of Distance Learners - Keller’s ARCS Framework and Data Analytics Study | PDF",1792073688,38]