## 5 Discussion  
This study developed a method for exploring the EEGs' measured flow construct and factors of an idiographic student's cognitive load, self-efficacy, test performance, and test difficulty, and answered the two RQs. For RQ1, results showed that an EEG analysis revealed a significant correlation between the student's flow states with cognitive load, self-efficacy, test performance, and difficulty factors. These findings are in line with the flow theory [1] and other studies that reported the influence of cognitive load [2], self-efficacy [3; 4; 5; 6], performance [7; 8], and difficulty [9] on the flow state and the possible prediction of such a state through the said factors. For RQ2, the study synthesized the findings from the J48 decision tree classification and ORs from the logistic regression analysis (Figure 3), which presented the construct of this student's flow states during tests through a flow construct the size of 13 with 6 nodes, 7 leaves, and 12 branches. This construct was supported by two different statistical analyses. In Level 1, the student's flow experience is first decided by the cognitive load (Level 1, Node 1), and lower cognitive loads often induced a high EEG-F with a 5.6 OR compared to the 1.0 OR of high cognitive load, supporting the claim that cognitive load heavily influences flow experiences [2]. Next, Level 2 indicated the commencement of self-efficacy (Nodes 2 and 3). The OR reached 11.8 when this student had a high cognitive load with a high self-efficacy (Node 2), while the OR reached 3.7 when this student had a low cognitive load and a high self-efficacy (Node 3). Given these observations, those with high self-efficacy are more likely to reach the flow state [3; 4; 5; 6]. In addition, comparing the OR of Nodes 2 and 3, it was found that a high self-efficacy was more conducive to the emergence  
\begin{table} \begin{tabular}{c c c c c c c} \hline \hline
**Node** & **Variables** & **Omnibus\(\chi^{2}\)** & **Sig.** & **Group** & **Wald’s\(\chi^{2}\)** & **Sig.** & **OR** \\ \hline \multirow{4}{*}{3} & L load (OR = 5.6) & & & & & \\  & Self-efficacy & 42.82 *** & \textless{}0.001 & H vs. L & 38.73 *** & \textless{}0.001 & 3.7 \\  & (L efficacy) LT-tp & 4.00 * & 0.046 & H vs. L & 4.00 * & 0.047 & 0.6 \\  & (H LT-tp) Difficulty & 2.26 & 0.133 & L vs. H & 2.25 & 0.134 & 1.6 \\ \hline \hline \end{tabular}  Note. * * * * * * * * * * * * * * * * * * * * * * * *  
of a high EEG-F in a high cognitive load (OR = 11.8) than in a low cognitive load (OR = 3.8). This reminds us that when a student faces more complex test items, it is necessary to provide guidance to build her skills and self-confidence. After cognitive load and self-efficacy, Levels 3 and 4 corresponded to the factors of test performance and item difficulty, respectively. In particular, Level 3 indicated that the student's flow experience was decided by test performance in certain situations (Nodes 4 and 5). When the student had both a high cognitive load and high self-efficacy (Node 4), the high LT-tp group was more likely to reach a high EEG-F with a 7.1 OR than the low LT-tp group. This result showed that a high sense of long-term control with both high cognitive load and high self-efficacy was more powerful for high EEG-F. However, when the student had both low cognitive load and low self-efficacy (Node 5), the high LT-tp group was not more likely to reach a high EEG-F, with only a 0.6 OR. This result showed that cognitive load and self-efficacy are more powerful for EEG-F than SC, especially low EEG-F caused by low self-efficacy. Lastly, Level 4 indicated that the student's flow experience also depended on item difficulty (Node 6). The OR reached 3.5 when the participant faced low-difficulty items while having high LT-tp with high self-efficacy and high cognitive load (Node 6).