MultiLA - An Authoring and Learning Analytics Tool for e-Learning Applications in Data Science Education

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Abstract Interactive e-learning applications (eLAs) are proving to be a useful addition to teaching material for data science courses, as they allow to combine mathematical theory, interactive visualizations, coding and other exercises in a single environment. In this paper, we present our tool MultiLA, which comprises an authoring tool to build eLAs and a backend for data collection. The eLAs can track learner behavior from mouse clicks and pointer movements up to the success in completing exercises, all being stored in the backend. Using learning analytics learning behavior and success can be analysed aiming to improve eLAs and support learners. eLAs can easily be adapted to different learner groups by [de]selecting their individual learning blocks in the backend, also providing an easy way to run A/B tests for comparing different versions of an eLA.
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MultiLA - An Authoring and Learning Analytics Tool for e-Learning Applications in Data Science Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MultiLA - An Authoring and Learning Analytics Tool for e-Learning Applications in Data Science Education Markus Konrad, Maria Osipenko, Martin Spott, Andre Beinrucker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9011703/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Interactive e-learning applications (eLAs) are proving to be a useful addition to teaching material for data science courses, as they allow to combine mathematical theory, interactive visualizations, coding and other exercises in a single environment. In this paper, we present our tool MultiLA, which comprises an authoring tool to build eLAs and a backend for data collection. The eLAs can track learner behavior from mouse clicks and pointer movements up to the success in completing exercises, all being stored in the backend. Using learning analytics learning behavior and success can be analysed aiming to improve eLAs and support learners. eLAs can easily be adapted to different learner groups by [de]selecting their individual learning blocks in the backend, also providing an easy way to run A/B tests for comparing different versions of an eLA. interactive learning application learning analytics data collection Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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