Abstract
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 “<em>What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?”</em> 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 – <em>Data, Management, People, Technology</em>, and <em>Privacy &amp; Ethics.</em> Capabilities presently absent from existing learning analytics frameworks concern <em>sourcing and integration, market, knowledge, training, automation, </em>and <em>connectivity</em>. 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.
Highlights
Learning analytics aim at optimizing learning and the environment in which learning occurs by analyzing and intervening on learner-generated data [1]
We will answer the main 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?” The following sub-questions operationalize the main research question: RQ1: “What capabilities necessary for the successful adoption of big data analytics and business analytics within an organization can be identified in existing literature?”
To make sure no relevant models are missed, we perform an additional search in two major databases in which, among others, papers from the Journal of Learning Analytics and the Learning Analytics and Knowledge (LAK) conference proceedings papers are published: Education Resources Information Center (ERIC) and Association for Computing Machinery (ACM)
Summary
Learning analytics aim at optimizing learning and the environment in which learning occurs by analyzing and intervening on learner-generated data [1]. Learning analytics can bring competitive advantages to the educational domain, but to do so, institutions must invest in resources and institutional capacities [2]. To address the strategic investment higher educational institutions need to make, we take the lens of the resource-based view theory as our main perspective. The resource-based view has been used to study, among others, big data analytics and business analytics – two research fields similar to learning analytics. We consider it useful to the learning analytics community and use this theory to study learning analytics adoption
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