| Abstract: |
Introduction
The demarcation between curiosity and interest is creating intensive discussion (Grossnickle, 2016; Renninger & Hidi, 2016). Is curiosity synonymous with interest (Silvia, 2008), or do they have some crucial differences? To solve this problem, we need studies that examine these two concepts at the situational level (i.e., using the experience sampling method [ESM]; Hektner et al., 2007). Moreover, the data analytical method should also go beyond the conventional analysis such as multi-level modeling.
The purpose of this study was to examine the differences between curiosity and interest using network analysis (i.e., co-occurrence analysis) based on ESM data. By comparing the differences between curiosity networks and interest networks, we were able to determine the extent to which and the aspect from which curiosity is distinct from interest.
Method
Fifty-nine first-year high school students in Helsinki participated the study. The data were obtained via ESM questionnaires via smartphones. Students were asked to report academic emotions and motivations when they received the alert. Over a period of two weeks, they received 3–4 signals randomly per day (at least once when they had science lessons; see appendix for full list of items). All the items were rated on a scale of 1 (not at all) to 4 (very much). The data comprised of 1704 responses/situations (average response per person = 28.88).
Two types of network analyses were conducted: the co-occurrence network analysis and the correlation-based network analysis. The co-occurrence network analysis, according to Moeller et al. (2018), can show how often (or rarely) two variables co-occur at high levels. It can help us avoid misinterpretation of correlations. Correlations mean how consistently the ratings of two variables are aligned, but how these two variables occur together at a high level is not necessarily revealed. Also, frequent co-occurrence may occur even when two variables correlate negatively.
Findings
Tables 1 and 2 show the networks of interest and curiosity, respectively. A relative index of edge weight was calculated for both interest and curiosity. The interest network results (see Table 1) showed that high-level interest occurred 1204 times. When interest occurred, feelings of enjoyment, control, success, importance to self, and concentration were the top five co-occurring motivations. Curiosity only occurred at a probability of 0.54. However, when curiosity occurred (self-edge=741; see Table 2), feelings of interest, control, enjoyment, inquisitiveness, and meeting self-expectations typically occurred at the same time.
The correlation-based network analysis (See Tables 1&2, Figures 2a&2b) showed that interest was closer to enjoyment, feelings of being skilled and concentration, whereas curiosity was closer to feelings of inquisitiveness and wanting to know more.
Discussion
The network analysis (i.e., both co-occurrence and correlation-based analysis) used in this study has important implications for intensive data studies. First, the use of co-occurrence analysis in addition to correlation-based analysis provides a more comprehensive picture of the relationships of variables. Second, the comparison of multiple networks helps better to understand the differences between variables and avoid the jingle-jangle fallacy that often occurs in the field of education and psychology.
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