# Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features
## 1 Introduction
For example, [PERSON] [14] observed the influence of SI in increasing an individual's attention but did not examine the effects on the effective reactions. In an EEG-based study on attention, [PERSON] [15] found that alpha wave synchronization reduces during the attentional process. Both studies did not observe the effects of SI on effective reactions. Other studies have examined students' motivation to learn in response to positive feelings presented in class. For example, [PERSON] et al. [16] examined effective states on learning performance in a multimedia-based mobile learning scenario. However, this study did not examine the influence of attentional reactions on SI. Due to these constraints, this study is motivated to provide findings that integrate the cognitive attention and effective motivation of interest during learning. Hence, by measuring motivation and attention, both elements of situational interest will be measured. In monitoring and examining the level of interest, most of the studies used a combination of questionnaire and classification models to approve, classify, or validate the findings based on physiological sensor data. Among them is the study from [PERSON] et al. [17], which claimed that using neural network multi-layer perceptron (MLP) to examine the features of EEG activity captured while answering mathematics and science questions, it might be able to know the student's interest. A support vector machine (SVM) and MLP were used with self-report on academic emotion prediction based on brain signals, mouse habits, and personality profiles [18]. The classification method's robustness is undeniable; however, this technique requires a complex computation to obtain the output. Furthermore, this method needs predefined classes mainly based on subjective evaluation, which make it impossible to group the data immediately. Thus, to seek the most favorable balance method between uncomplexity and resolve the limitations of existing methods that rely on subjective measures, we suggest the usage of clustering methods to obtain an automatic clustering scheme of situational interests using EEG. Clustering had been used in classroom assessments. The k-means was used as a prediction of students' academic performance [19], as well as an evaluation model of student's learning outcomes [20]. A study used DBSCAN clustering to cluster the students based on their examination performance in various subjects [21], while fuzzy k-means were used in measuring the accomplishment of Course Outcome in Higher Educational Institutes [22]. In this study, we applied k-means and DBSCAN based on EEG features to assess the SI level of students. The choice of DBSCAN and k-means was motivated by their simplicity and their performances in various fields of previous studies. Since we are expecting the students will be clustered into three classes (high, medium, and low SI), the use of k-means is appropriate. However, if the number of SI classes is unknown, there are other possible clustering methods like hierarchical clustering and Gaussian model that could be used. Good works of literatures can be referred to in [PERSON] [23] and [PERSON] [24]. The advantage DBSCAN is known to be good in outlier and noise detection. We use density-based clustering specifically DBSCAN to investigate whether the noise will reduce the performance of overall results. This paper proposed a simple framework by replacing subjective bias of psychological measures with clustering method using established k-means and DBSCAN algorithm to be used as predefined classes to classify the students based on their SI level during learning in the class. The contributions of this paper are summarized as follows:  
* Contribute to further efforts to integrate EEG and clustering methods on automatically clustering the students by the level of SI in real classrooms.