## 5 Discussion
However, the difficulty omnibus was not significant when the participant had low LT-tp with low self-efficacy and low cognitive load in the decision tree, as seen in Figure 2 (Node 7), so it was not included in Figure 3.  
Ultimately, two patterns for the emergence of higher EEG-Fs (Leaf 2 and 5) and four patterns that may have had the emergence of lower EEG-Fs (Leaf 1, 3, 6, 4) were identified. First, as in Leaf 2 (in Figure 3), a high EEG-F was frequently detected when the student had a high self-efficacy and low cognitive load, as observed in a study's findings [35]. Second, as in Leaf 5 (in Figure 3), when the student had a high cognitive load, and it was combined with a high self-efficacy and high performance, a high flow state may have emerged when facing low-difficulty items, in line with a study's results [34]. Similar to previous research [8, 15, 16], this study used the participant's test performances to correspond to her SC in the flow state. Notably, the previous study [15] identified both LT-tp and ST-tp as factors affecting the students' flow construct, supporting previous studies that claimed that SC is essential to flow experiences [1, 7, 8]. In addition, one study's final construct of flow during tests in a technological interactive learning environment included ST-tp [16]. However, in the present study's chi-square analysis (Table 1), ST-tp did not have a significant correlation with the participant's flow state, and the final construct of flow in tests did not include ST-tp. As such, this result was not entirely in line with previous studies. This may be attributed to the fact that the participant did not receive immediate feedback after answering each question, rendering the effect of the ST-tp insignificant. In addition, there are too few cases of incorrect answers (only 67, as seen in Table 1) in this study, so the short-term SC's effect requires further exploration. In addition, following flow theory [1], self-efficacy was used to indicate the participant's self-reported skill sense, and item difficulty signified the challenges the participant faced. Leaf 5's result showed that a student with a high skill sense may reach a high EEG-F when facing low challenges under a high cognitive load. Notably, previous studies [15, 16] identified that when high-skill students were matched with a high-challenge task, BCS positively affected students' flow experiences measured with an EEG. This was consistent with the claim that BCS is a key to flow experiences [1]. In contrast with previous studies, the present study's results were more subtle. Leaf 5's result may have been caused by low-difficulty items reducing the stress caused by a high cognitive load, allowing the participant to concentrate and engage in tests while reducing threats to her performance. Eventually, this led the participant into a flow state. Furthermore, even though the idiographic student had both low cognitive load and a high sense of long-term control, low self-efficacy significantly impacted the emergence of lower EEG-Fs (Leaf 4). This reflects the findings that people with low perceived abilities experienced a low flow level [6]. This study had theoretical and practical values. Taking this ideographic student's flow construct (Table 1 and Figure 3) as an example, the four factors can be categorized into two dimensions (student-related and test-related), with which strategies for improving a student's flow experience in learning can be implemented and are explained as the following:  
* To student-related factors (self-efficacy and performance): the results indicated that enhancing this student's self-efficacy and learning performance also improved her flow experience. Thus, teachers may adopt strategies, such as encouraging positive metacognition (e.g., self-regulation), motivation (e.g., the value of learning, belief in her capacity, etc. ), and behavior (e.g., attending and concentrating, using effective learning strategies) in her learning [9; 49].