## 1 Introduction  
The digitization of education led to the increased availability of learner data ([PERSON], 2012). These learner data are collected, analyzed, and used to perform interventions to improve education [(1)]. This process is called learning analytics (LA). LA is effective in improving student outcomes [(2)]. For this reason, many higher educational institutions (HEIs) seek to adopt LA into their educational processes. Over the years, researchers designed models, frameworks, and other instruments to support LA adoption [(3; 4; 5; 6; 7; 8)]. However, despite these models' existence, only a few institutions successfully implemented LA at scale [(9; 10; 11)]. The extant implementation models do not offer practical insights into operationalizing the critical dimensions for LA adoption [(12)]. They also only focus on specific elements like policy development and do not provide solutions to challenges identified by institutions while using the models [(8)]. This calls for a model that incorporates all dimensions important to successful LA uptake as well as prescribing in detail how to operationalize these dimensions. During previous research, we designed a capability model for learning analytics based on literature [(13)]. This model was refined by conducting interviews [(14)]. The model comprises 30 different organizational capabilities, divided into five categories--see Figure 1. Definitions for each capability are available as Supplementary Materials (Table S1). To increase its practical relevance, the model prescribes multiple ways to operationalize each capability. For example, the capability Data Quality prescribes the use of common definitions, standardized labelling of data, automation, timely input, et cetera. This way, users do not only obtain insight into what capabilities they need to (further) develop at their institutions, but also what this means in practice. All operationalizations are available as Supplementary Materials (Table S2). So, the model not only describes the 'what', but also prescribes the 'how'. Intended users of the model are practitioners such as program managers, policymakers, and senior management who need to plan LA adoption at their institution. The model's development process followed the steps of the Design Science Research framework [15]. An essential step in design science research is evaluating the designed artefact [15; 16; 17; 18]. After its design, formative evaluation, and refinement, the model's validity must be demonstrated by returning it \"into the environment for study and evaluation in the application domain\" [19]. This paper describes the artefact's evaluation with real users in a real-world context. It answers the main research question, \"Does the Learning Analytics Capability Model help practitioners with their task of planning learning analytics adoption at higher educational institutions?\" The objective of this research is to (1) evaluate the effectiveness of the LACM, (2) measure the users' perceptions of the model's usefulness, and (3) evaluate the model's completeness. To achieve this goal, we organized five pluralistic walkthroughs with 26 participants responsible for LA implementation at their institution, conducted a survey, and interviewed seven experts during a group interview. The relevance of our work is twofold. First, it provides an empirically validated capability model by applying it in a real-world setting. Our research shows what capabilities are often overlooked when planning the implementation of LA. Second, it further established pluralistic walk-throughs as an evaluation method in design science research. Most often, pluralistic walk-throughs are applied to research user interfaces' usability [20]. However, we used pluralistic walk-throughs to evaluate the capability model. Our work substantiates pluralistic walk-throughs' applicability as an evaluation method in situations where artefacts need to be used in real-world settings while time is constrained. This paper is structured as follows.