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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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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Hbo-studenten doen tijdens hun opleiding werkervaring op, bijvoorbeeld door stage te lopen. Wij onderzoeken op welke manier technologie het leerproces van studenten op de werkplek kan ondersteunen. We ontwikkelen ontwerpprincipes en de daarop gebaseerde Stage-App.Doel Studenten leren op de werkplek heel anders dan op de hogeschool. Het leren gebeurt vaak onbewust en impliciet. De Stage-App helpt studenten bewuster te worden van dit leerproces en hier actiever mee bezig te zijn, om uiteindelijk meer uit hun stage te halen. Resultaten Dit onderzoek loopt. We hebben de resultaten tot nu toe gedeeld via posters, presentaties en artikelen. Gepubliceerde artikelen Exploring Design Principles for Technology-Enhanced Workplace Learning Design Propositions for Technology-Enhanced Workplace Learning Design & Implementation of Technology-Enhanced Workplace Learning Learning Analytics voor Stages en Werkplekleren Workplace Learning Analytics in Higher Engineering Education Automated Feedback for Workplace Learning in Higher Education De open-source Stage-App is beschikbaar via Github.com. Looptijd 01 november 2015 - 31 december 2020 Aanpak In het eerste deel van onderzoek hebben we uitgezocht wat er nodig is om een app voor het leerproces te ontwerpen. Vervolgens hebben we de Stage-App ontwikkeld. Daarin kunnen studenten registreren wat ze hebben geleerd en dit koppelen aan de leerdoelen die ze vanuit hun opleiding meekrijgen. We ontwikkelen de app zoveel mogelijk vanuit het perspectief van de student. Om de app aan te laten sluiten op de wensen en eisen van studenten houden we interviews, enquêtes, gebruikerstesten en co-design-sessies. Tegelijkertijd baseren we de functionaliteiten op literatuuronderzoek over werkplekleren en 'technology enhanced learning', om te zorgen dat de app het leerproces zo goed mogelijk ondersteunt.
