18th Aug 2015, 3:30pm to 5:00pm

Combining Empirical and Machine Learning Techniques to Predict Math Expertise using Pen Signal Features

Presented by Dr Jianlong Zhou – NICTA

UNSW Learning Analytics & Data Science in Education Research Group

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Multimodal learning analytics aims to automatically analyze students’ natural communication patterns based on speech, writing, and other modalities during learning activities. This research used the Math Data Corpus, which contains time- synchronized multimodal data from collaborating students as they jointly solved problems varying in difficulty. The aim was to investigate how reliably pen signal features, which were extracted as students wrote with digital pens and paper, could identify which student in a group was the dominant domain expert. An additional aim was to improve prediction of expertise based on joint bootstrapping of empirical science and machine learning techniques. To accomplish this, empirical analyses first identified which data partitioning and pen signal features were most reliably associated with expertise. Then alternative machine learning techniques compared classification accuracies based on all pen features, versus empirically selected ones.

If you would like to join this research group or would like more information please contact Lorenzo Vigentini l.vigentini@unsw.edu.au

Event Details
Public Event open to all
75 Seats available


Room 310, Mathews Building

Key Contact
Lorenzo Vigentini