Samenvatting

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.