Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
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Deze whitepaper is een vervolg op deze eerdere reeks over People Analytics en bespreekt de nieuwste trends. Inhoud: • Inleiding 1. Medewerkerswelzijn krijgt meer aandacht 2. HR Analytics wordt People Analytics 3. Het inzicht groeit dat People Analytics geen kant-en-klare oplossingen levert 4. De kloof tussen de vaardigheden en ambities wordt minder groot 5. Analyticsteams herbergen steeds meer expertise 6. Meer data worden gekwantificeerd 7. Steeds meer data worden van buiten de organisatie betrokken 8. Kunstmatige intelligentie kan voor onverwachte inzichten zorgen 9. Het aantal interne databronnen neemt toe 10. Er komt meer aandacht voor privacybescherming • Conclusie
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From the article: "The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in."
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Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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Verslag van een presentatie. In onderzoeken naar de prioriteiten van HR-professionals staan analytics dan ook steevast onderaan het prioriteitenlijstje. Echter, nu elke dag meer data beschikbaar komen en alles is te meten, is dit niet langer een houdbaar standpunt. HR-professionals zullen op zijn minst moeten beseffen dat data waardevol zijn. Een Engelstalige definitie van People Analytics luidt: ‘The systematic identification and quantification of the people drivers of business outcomes, with the purpose of making better decisions.‘ Daarbij is het belangrijk om een goede businessvraag te stellen én – vervolgens –de resultaten van de analyse op overtuigende wijze over te brengen.
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Interview. “We gaan People Analytics gebruiken om duurzame inzetbaarheid te bevorderen.” Die zin zet je snel in het strategisch plan, maar hoe moet het dan? Hoe beginnen we? En hoe pas je het goed toe? Volgens Sjoerd van den Heuvel is het belangrijkste doel van people analytics niet antwoorden vinden, maar de juiste vragen stellen.
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This interview focuses on employee performance and HR Analytics.
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Hoofdstuk 10 in HRM Heden en Morgen. Dit hoofdstuk is geschreven vanuit de overtuiging dat een gemeenschappelijke taal en begrip van people analytics, evenals enkele basale wetenschappelijke principes waarop het gestoeld is, het jonge vakgebied in de praktijk naar een hoger niveau kunnen tillen. En daarmee de (toekomstige) HRM-professionals werkzaam op en rondom dit uitdagende thema in staat kunnen stellen (nog meer) impact te maken in hun organisatie. Het primaire doel van dit hoofdstuk is om de (toekomstige) professional die dit leest, aan het denken te zetten. Dit kan betekenen inspireren, verwarren, of duiden. Maar ook aanzetten tot het concreet aan de slag gaan met people analytics in de eigen organisatie, op de grens van wetenschap en praktijk, because that’s where the magic happens.
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Educational institutions in higher education encounter different thresholds when scaling up to institution-wide learning analytics. This doctoral research focuses on designing a model of capabilities that institutions need to develop in order to remove these barriers and thus maximise the benefits of learning analytics.
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