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Exploring the Behavior of Novice Programmers’ EEG Signals for Affect-based Student Modeling Tita R. Herradura1, Joel P. Ilao2, Merlin Teodosia C. Suarez2 1

College of Science and Computer Studies, De La Salle University-Dasmarińas City of Dasmarińas, Philippines [email protected] 2

College of Computer Studies, De La Salle University, Philippines {joel.ilao, merlin.suarez}@delasalle.ph

Abstract. Ten (10) first year college programming students participated in the study and reported their emotion during the learning session. The Emotiv EPOC sensor is used to gather brainwave signals. Digital signal processing techniques such as filtering and transformation were used to preprocess the data. The study visualizes the behavior of EEG signals for each academic emotion. Using spectral plots, engagement exhibits higher amplitude for most of the participants while boredom exhibits lower amplitude. Confusion and frustration shows inconsistencies in their behavior. Statistical features were used for feature extraction. Several machine algorithms are used to classify emotions. C4.5 has an accuracy rate of 97.91%.

1

Introduction

The study focuses on the characterization of behavior of student’s emotions using brainwave signals. Using digital signal processing techniques, it visualizes the behavior of EEG signals for each academic emotion. It is limited to beta waves since it is responsible for cognitive activities [1]. The study also explores the behavior of frontal lobes on these beta waves. Recent EEG studies have shown that when a subject performs cognitive or judgment tasks that require keeping something in mind over a short period, a number of areas in the prefrontal cortex are active [2]. Since no research has been done to explore the behavior of frontal lobes on beta waves in a learning context, this work aims to fill the gap.

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Methodology and Results

The engaged academic emotion shows higher energy for most of the students as compared with other emotions, which may imply that the student shows interest in the

activity. Figure 1 shows the average amplitude of student’s reported emotion for the eight (8) frontal nodes. Boredom signals have lower amplitude. This may imply that boredom is associated with the absence of change in cognitive action (see Fig. 1). Confusion and frustration have shown inconsistencies in their behavior. This may imply that students have difficulty differentiating the two academic emotions or confusion and frustration signals may have similarities in their behavior. Six (6) statistical features are extracted to produce the dataset. Several classification algorithms were used to determine the accuracy of performance. C4.5 has an accuracy rate of 97.91%. It has higher accuracy results compared with [3]. This may be attributed to data preprocessing techniques used to generate the dataset.

Fig. 1. Average amplitude of student’s academic emotion

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Conclusion

Using digital signal processing techniques on EEG data provided relevant results to determine the behavior of learner’s academic emotion. Engagement is found to have higher amplitude for most of the participants while boredom exhibited lower amplitude. Confusion and frustration have shown inconsistencies in their behavior. These findings are dependent on the data obtained from the ten participants. Future works include increasing the number of participants and identifying the personality traits of the students. Acknowledgements. We thank all the participants in this study. We also thank Ms. Judith J. Azcarraga, the Center for Empathic Human-Computer Interaction and the Commission on Higher Education for all the support in conducting this research.

References 1. Boutros, N., Galderisi, S., Pogarell, O.: Standard Electroencephalography in Clinical Psychiatry: A Practical Handbook. Wiley (2011) 2. Ackerman, S.: Discovering the Brain. Washington, DC, USA: National Academies Press (1991) 3. Mampusti, E.T., Ng, J.S., Quinto, J.J.I., Teng, G.L., Suarez, M.T.C., Trogo, R.S.: Measuring Academic Affective States of Students via Brainwave Signals. In: 2011 Third International Conference on Knowledge and Systems Engineering (KSE), pp. 226–231. IEEE (2011)