The Future of Data–Enriched Assessment

Candace Thille, Emily Schneider, René F. Kizilcec, Christopher Piech, Sherif A. Halawa & Daniel K. Greene   |    Volume Nine  |   

The article addresses the question of how the assessment process with large–scale data derived from online learning environments will be different from the assessment process without it. Following an explanation of big data and how it is different from previously available learner data, we describe three notable features that characterize assessment with big data and provide three case studies that exemplify the potential of these features. The three case studies are set in different kinds of online learning environments: an online environment with interactive exercises and intelligent tutoring, an online programming practice environment, and a massive open online course (MOOC). Every interaction in online environments can be recorded and, thereby, offer an unprecedented amount of data about the processes of learning. We argue that big data enriches the assessment process by enabling the continuous diagnosis of learners’ knowledge and related states, and by promoting learning through targeted feedback.

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