<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-14T17:45:04Z</responseDate><request verb="GetRecord" identifier="oai:uvadoc.uva.es:10324/49205" metadataPrefix="mods">https://uvadoc.uva.es/oai/request</request><GetRecord><record><header><identifier>oai:uvadoc.uva.es:10324/49205</identifier><datestamp>2025-02-25T08:20:53Z</datestamp><setSpec>com_10324_985</setSpec><setSpec>com_10324_954</setSpec><setSpec>com_10324_894</setSpec><setSpec>col_10324_988</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Ortega Arranz, Alejandro</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Martínez Monés, Alejandra</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Asensio Pérez, Juan Ignacio</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Bote Lorenzo, Miguel Luis</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2021-10-20T08:26:42Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2021-10-20T08:26:42Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2021</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="citation">Scheffel, M.; Dowell, N.; Joksimovic, S.; Siemens, G. Companion Proceedings of the 11th International Conference on Learning Analytics &amp; Knowledge (LAK21). Society for Learning Analytics Research, Online, 2021,  p. 215-222</mods:identifier>
<mods:identifier type="isbn">978-1-4503-8935-8</mods:identifier>
<mods:identifier type="uri">https://uvadoc.uva.es/handle/10324/49205</mods:identifier>
<mods:abstract>Learning Analytics enable a better understanding of teaching and learning&#xd;
processes by identifying and monitoring indicators based on students’ activity. These same&#xd;
indicators can also be used by reward-based gamification strategies as conditions that&#xd;
students should satisfy to earn rewards, with the purpose of increasing their engagement with&#xd;
the learning contents and activities. Hence, gamification systems must enable the digital&#xd;
representation and interpretation of indicators based on students’ activity, similarly as&#xd;
learning analytics tools do. This position paper introduces GamiTool, a gamification system to&#xd;
support the design and the computer-interpretable representation of a wide variety of&#xd;
learning analyticsindicatorsthat can be configured by practitioners as gamification conditions.&#xd;
Additionally, the paper discusses five potential lines of work regarding joint research with&#xd;
GamiTool and LA.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by-nc-nd/3.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">© 2021 Society for Learning Analytics Research</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Attribution-NonCommercial-NoDerivs 3.0 Unported</mods:accessCondition>
<mods:titleInfo>
<mods:title>GamiTool: Towards actionable learning analytics using gamification</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/conferenceObject</mods:genre>
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