Sources of Evidence-of-Learning: Learning and assessment in the era of big data

Abstract

This 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 consequence of each and all of these developments is the potential to record and analyze the 'big data' that is generated. The article presents both an optimistic view of what may be possible as these technologies and pedagogies evolve, while offering cautionary warnings about associated dangers.

Scaffolding for Optimal Challenge in K–12 Problem-Based Learning

“…Most computerbased scaffolding that incorporates fading employs fixed fading, in which scaffolds are removed after a fixed time interval and are thus not completely adapted to student ability. Many intelligent tutoring systems and advanced learning analytics implement performance-adapted fading based on assessment of student performance (Cope & Kalantzis, 2015;VanLehn, 2008;West, 2012), but many scholars criticized the use of fading by computer systems due to inaccurate diagnosis of students' behavior, intention, and learning progress (Jackson, Krajcik, & Soloway, 1998;Jonassen & Reeves, 1996;Madaio, 2015). In the case of fading based on teachers' judgment, teachers need to determine the timing of fading as a result of examining each student's learning process.…”

Learner differences in theory and practice†

“…How do you measure progress in achieving education's most basic promises, for individuals and the groups to which they belong? The answer must mean using innovative assessment and evaluation practices that provide meaningful feedback such as portfolio evaluation, peer-or self-review, and 'big data' analyses of learner progress (Cope & Kalantzis, 2015c). The measure for all of these innovative assessment modes should not be identical 'standards' but comparabilities -evidence in the form of learning activity and knowledge representations which can never be the same, but may be comparable in terms of epistemic effort and intellectual outcomes.…”

Education 2.0: Artificial Intelligence and the End of the Test

“…We want to argue in this paper that artificial intelligence will be a pivot point towards an Education 2.0. But first, we want to define what we mean by this phrase (Cope & Kalantzis. 2015c).…”