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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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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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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Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
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In deze lezing worden vier onderwerpen besproken: 1. Wat is Learning Analytics (LA)? 2. Hoe begin je met LA? 3. Wat is het verschil tussen adoptie en implementatie van LA? 4. Waarom zijn veel LA-projecten niet succesvol en moeilijk om op te schalen? Wat zijn randvoorwaarden voor succes?
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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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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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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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Whitepaper in een serie over HR Analytics. Steeds vaker worden HRM-beslissingen gebaseerd op voorspellende modellen die ontwikkeld zijn op basis van historische data. In deze whitepaper bespreken we een aantal best practices die organisaties daarbij kunnen helpen. Zo is het belangrijk om goed te letten op de oorsprong van gegevens. Objectieve meetgegevens zijn bijvoorbeeld vaak van grotere waarde dan subjectieve antwoorden uit enquêtes. Wanneer een organisatie data wil verzamelen voor een People Analytics-project, is het daarnaast belangrijk om zeker te weten dat er meetinstrumenten worden gekozen die ook echt meten wat ze beogen te meten. Inhoud: • Inleiding 1. Kies de juiste steekproef 2. Let op de grootte van de steekproef 3. Geef de voorkeur aan objectieve gegevens 4. Zorg voor valide meetinstrumenten 5. Koppel data op een privacyvriendelijke manier 6. Denk na over het gebruik van gemiddelden 7. Verwar oorzaak en gevolg niet 8. Laat je niet foppen door percentages 9. Let op verklaarde variantie 10. Kijk altijd naar de netto opbrengst 11. Voer waar nodig extra analyses uit 12. Maak voldoende tijd vrij voor Analytics • Conclusie
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