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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Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome.
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De analyse van data over het leren van studenten kan waardevol zijn. 'Learning analytics' gebruikt studentdata om het leerproces te verbeteren. Welke organisatorische vaardigheden hebben Nederlandse instellingen voor hoger onderwijs nodig om learning analytics succesvol in te zetten?Doel We onderzoeken welke organisatievaardigheden er nodig zijn om in het hoger onderwijs met 'learning analytics' te werken. Met learning analytics krijgen studenten, docenten en studiebegeleiders inzicht in het leerproces. Dit doen ze door data van studenten te analyseren. In de praktijk blijkt het lastig voor onderwijsinstellingen om hier over de hele breedte van de organisatie mee te gaan werken. We kijken in dit onderzoek welke vaardigheden er nodig zijn binnen een organisatie om 'learning analytics' slim in te zetten. Resultaten Dit onderzoek loopt. Tot nu toe hebben we drie wetenschappelijke artikelen gepubliceerd: A First Step Towards Learning Analytics: Implementing an Experimental Learning Analytics Tool Where is the learning in learning analytics? A systematic literature review to identify measures of affected learning From Dirty Data to Multiple Versions of Truth: How Different Choices in Data Cleaning Lead to Different Learning Analytics Outcomes Looptijd 01 december 2016 - 01 december 2020 Aanpak Het onderzoek bestaat uit literatuuronderzoek, een case study bij Nederlandse onderwijsinstellingen en een validatieproject. Dit leidt tot de ontwikkeling van een Learning Analytics Capability Model (LACM): een model dat beschrijft welke organisatorische vaardigheden nodig zijn om learning analytics in de praktijk toe te passen.
