Leveraging our experience in educational co-creation and successfully building and implementing multiple pedagogical artefacts, we have designed a Simple Hermeneutics-inspired Learning Analytics Model for Modules (SHLAM²) that has the potential to transform how learning is assessed. Hermeneutics, (the process of ‘meaning making’) has enabled us to unpack the complexities of learning into the basic activities of reading, talking, and drawing, which enables us to identify three fundamental learning patterns: (i) seeking clarity on new concepts/content, (ii) finding voice through verbalising interpretations, and (iii) building confidence by translating interpretations into visual artefacts. Therefore, using SHLAM² to capture learner-generated data around these three patterns affords both learners and lecturers the opportunity to see and appreciate: (i) a learner’s personal efforts at meaning making, and (ii) the outputs of a learners’ personal efforts, and (iii) their willingness to share these outputs. In essence, SHLAM² is an innovative hermeneutical data-driven approach to assessing learning that provides a dataset currently unavailable in UCC (and across many HEIs). Our aim is to take the initial SHLAM² prototype and further refine its design through rigorous evaluation in different learning environments. The initial prototype was developed from a confessional analysis of BU7007 student experiences (a five-credit module on Action Design Research).