Learning Analytics: The New Black

Abstract

Learning analytics is the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs,” according to the 1st International Conference on Learning Analytics and Knowledge.1 The NMC Horizon Report: 2012 Higher Education Edition notes that this promising set of practices and tools aims to “harness the power of advances in data mining, interpretation, and model- ing to improve understandings of teaching and learning, and to tailor education to individual students more effectively.”2 Finally, George Siemens and Phil Long have even proposed that learning analytics should ultimately be focused on dis- ruption and transformation in education, changing the very nature of teaching, learning, and assessment as we know it.3 Indeed, learning analytics may hold great promise as a way to support learning assessment and as a higher education “movement.” The potential of learning analytics to combine information from multiple and disparate sources, to foster more-effective learning conditions in real time, and to enable multiple focal points for analysis and improvement is entic- ing. However, even though learning analytics offers powerful tools and practices to improve the work of learning and assess- ment, well-considered principles and propositions for learn- ing assessment should inform its careful adoption and use. Otherwise, learning analytics risks becoming a reductionist approach for measuring a bunch of “stuff” that ultimately doesn’t matter. In my world, learning matters.