EMERGENT TECHNOLOGIES FOR LEARNING ANALYTICS
EMERGENT TECHNOLOGIES FOR LEARNING ANALYTICS

Summary/Abstract: In this paper, a comprehensive definition of learning analytics is offered. After analytics methods were applied with success in business and in the industry, a natural step is for these techniques to be applied in other fields, such as teaching. We start by exploring the origins of learning analytics and the current implementations. We note that the learning analytics systems currently in use tap into data collected from two main sources. The first source of data is the data gathered while the learners were directly interacting with the system by navigating pages, posting on forums, discussing with the other users of the platform, completing quizzes or different virtual laboratories. The second source of data comes from the real-world interactions that the learners have such as course presence, grades, and submitted assignments. We advance the idea that another important source of data could be obtained by direct measurement of the learner’s behavior. We discuss the potential of tapping into the neurological and biological responses to the different activities involved in learning. This would allow for an unbiased and instant feedback of the user’s interactions. We discuss possible future developments based on technologies such as brain-computer interfaces (BCI), giving electroencephalography (EEG) as an example, and other methods that could be used for directly assessing the mental state as gaze tracking technologies, galvanic skin response (GSR), and heart rate. For the measurement of heart rate and GSR, inexpensive devices such as fitness bands and smartwatches could be used, while for gaze tracking, classical webcams could provide a solution.

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