Investigating collaborative learning success with physiological coupling indices based on electrodermal activity
作者： Héctor J. Pijeira-Díaz , Hendrik Drachsler , Sanna Järvelä , Paul A. Kirschner
关键词: Computer science 、 Set (psychology) 、 Dashboard (business) 、 Regression 、 Matching (statistics) 、 Process (engineering) 、 Machine learning 、 Learning analytics 、 Artificial intelligence 、 Dual (category theory) 、 Collaborative learning
摘要: Collaborative learning is considered a critical 21st century skill. Much known about its contribution to learning, but still investigating process of collaboration remains challenge. This paper approaches the investigation on collaborative from psychophysiological perspective. An experiment was set up explore whether biosensors can play role in analysing learning. On one hand, we identified five physiological coupling indices (PCIs) found literature: 1) Signal Matching (SM), 2) Instantaneous Derivative (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and 5) Fisher's z-transform (FZT) PCC. other three measurements were used: will (CW), product (CLP) dual gain (DLG). Regression analyses showed that out PCIs, IDM related most CW best predictor CLP. Meanwhile, DA predicted DLG best. These results determining informative measures for designing analytics, biofeedback dashboard.
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