Earth and Planetary Science Letters 119 (1993) 85-101. doi:10.1016/0012-821X(93)90008-WReceived by publisher: 1992-08-18Harvest Date: 2016-01-04 12:19:37DOI: 10.1016/0012-821X(93)90008-WPage Range: 85-10

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The printing press long ago and the computer today have made widespread access to information possible. Learning theorists have suggested, however, that mere information is a poor way to learn. Instead, more effective learning comes through doing. While the most popularized element of today's MOOCs are the video lectures, many MOOCs also include interactive activities that can afford learning by doing. This paper explores the learning benefits of the use of informational assets (e.g., videos and text) in MOOCs, versus the learning by doing opportunities that interactive activities provide. We find that students doing more activities learn more than students watching more videos or reading more pages. We estimate the learning benefit from extra doing (1 SD increase) to be more than six times that of extra watching or reading. Our data, from a psychology MOOC, is correlational in character, however we employ causal inference mechanisms to lend support for the claim that the associations we find are causal.

An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board.