Abstract

AbstractThis article sets out to explore a shift in the sources of evidence-of-learning in the era of networked computing. One of the key features of recent developments has been popularly characterized as ‘big data'. We begin by examining, in general terms, the frame of reference of contemporary debates on machine intelligence and the role of machines in supporting and extending human intelligence. We go on to explore three kinds of application of computers to the task of providing evidence-of-learning to students and teachers: (1) the mechanization of tests—for instance, computer adaptive testing, and automated essay grading; (2) data mining of unstructured data—for instance, the texts of student interaction with digital artifacts, textual interactions with each other, and body sensors; (3) the design and analysis of mechanisms for the collection and analysis of structured data embedded within the learning process—for instance, in learning management systems, intelligent tutors, and simulations. A conse...

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