Analyzing novice programmers\u27 EEG signals using unsupervised algorithms

Ten (10) first year college programming students participated in the study and reported their emotions during the learning session. Emotiv EPOC headset was used to gather EEG brainwave signals. Digital signal processing filtering technique was used to filter the data. The reported academic emotions were engaged, confused, frustration and boredom. A square SOM map with 10 rows by 10 columns was built to visualize the EEG data set, a total of 100 nodes. The weights of the final SOM nodes were clustered using k-medoids and k-means algorithms, both derived two main clusters; one cluster aptly named “State of hope and enthusiasm” because it is primarily composed of clusters of confused emotion nodes surrounded by a topographical arrangement of engaged emotion nodes; the other cluster named “State of frustration and boredom” because it is primarily composed of frustrated and boredom emotion nodes. These observations of the topographical arrangements of the SOM nodes and its subsequent clustering of the SOM nodes by k-medoids and k-means, seem to be in accordance with previous findings by (Kort, Reilly & Picard, 2001; D\u27Mello & Graesser, 2011) ultimately making SOM to be a viable and good alternative representation/visualization tool for D\u27Mello\u27s theory of academic affect transition model. We also observed that k-medoids required much lesser number of k to derive similar clusters of SOM nodes as k-means, moreover, execution time for k-medoids is the same as k-means, making k-medoids a very attractive option for clustering algorithm of choice for clustering of SOM nodes. © 2017 Asia-Pacific Society for Computers in Education. All rights reserved