Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment "of" and "for" learning. As such it has the potential to deliver value in the form of (1) "understanding" student learning, (2) "analysing" learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) "predicting" students-at-risk (or identifying where students have specific learning needs), and (4) "prescribing" elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students' chances of success, deepening the understanding of learning.