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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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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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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Learning analytics is the analysis of student data with the purpose of improving learning. However, the process of data cleaning remains underexposed within learning analytics literature. In this paper, we elaborate on choices made in the cleaning process of student data and their consequences. We illustrate this with a case where data was gathered during six courses taught via Moodle. In this data set, only 21% of the logged activities were linked to a specific course. We illustrate possible choices in dealing with missing data by applying the cleaning process twelve times with different choices on copies of the raw data. Consequently, the analysis of the data shows varying outcomes. As the purpose of learning analytics is to intervene based on analysis and visualizations, it is of utmost importance to be aware of choices made during data cleaning. This paper's main goal is to make stakeholders of (learning) analytics activities aware of the fact that choices are made during data cleaning have consequences on the outcomes. We believe that there should be transparency to the users of these outcomes and give them a detailed report of the decisions made.
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De meest gebruikte opbouw in business intelligence, predictive analitics en analytics modellen is de moeilijkheidsgraad: 1) descriptive, 2) diagnostic, 3) predictive en 4) prescriptive. Deze schaal vertelt iets over de volwassenheid van het gebruik van data door de organisatie. Een model dat niet op zichzelf staat en een achterliggende methode kent is de data driehoek van EDM (Figuur 1), welke in dit artikel zal worden toegelicht.
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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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The hospitality industry, comprising diverse Small and Medium Enterprises (SMEs) such as restaurants, hotels, and catering facilities plays an important role in local and regional communities by providing employment opportunities, facilitating the organization of community events, and supporting local social activities and sports teams (Panteia, 2023). The hospitality industry thereby represents a major source of income in Europe, but also a commensurate burden on the environment because of its relatively high usage of water and energy consumption, and food waste, leading to the formulation of several initiatives to increase the sustainability of hotels, restaurants, and resorts, such as farm to fork and towel reuse (Bux & Amicarelli, 2023). Another avenue for hospitality organizations to make progress towards sustainability goals is through circular economy strategies (Bux & Amicarelli, 2023) based on the creation of small regenerative loops that require the involvement of multiple stakeholders (Tomassini & Cavagnaro, 2022). Nevertheless, hospitality operators need to track their progress towards sustainability goals while keep sight of their financial goals (Bux & Amicarelli, 2023), requiring a data-driven decision-making approach to sustainability and circularity. Big data analytics have therefore been identified as an enabler of the circular economy paradigm by reducing uncertainty and allowing organizations to predict results (Awan et al., 2021; Gupta et al., 2019). Hospitality organizations however remain behind in leveraging data analytics for decisionmaking (Mariani & Baggio, 2022). The purpose of the study is therefore to examine how hospitality organizations can leverage data analytics to make data-driven decisions regarding circularity. Using a multiple case study approach of three Dutch hospitality SMEs, enablers and inhibitors of data analytics for datadriven decisions regarding circularity are examined. This addresses the call by Tomassini and Cavagnaro (2022) for more exploration of the circularity paradigm in hospitality. Despite the ongoing interest in increasing the sustainability of the hospitality industry (European Commission, 2013), relatively little attention has been paid to the development of circularity strategies and what is needed to implement them.
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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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