This study was conducted at an urban university in Taiwan. Ninety university students majoring in chemistry (n = 90, 42 males, 48 females; mean age ± S.D. = 22.3 ± 4.0 years) participated in the study. A questionnaire developed by the authors was administered to the participants. Chiu and Fu (1993) have developed a questionnaire to explore how Asian students solve stereochemistry problems and identify 2D and 3D chemical structural representations. Frailich et al. (2009) used integrated visualization tools to investigate students’ strategies for learning chemical structural formulas and found that many important concepts of chemical bonding help students to learn these formulas. Based on these two studies, a Chemical Structure Conceptual Questionnaire (CSCQ) was developed in this study. This questionnaire was also shown in Huang and Liu (2014). The CSCQ (perfect score = 100) consisted of 10 questions (perfect score for each question = 10). CSCQ example questions are shown in Table 1. CSCQ was used to gain an understanding of the learning performance of the students related to chemical structural formulas. The questionnaire was constructed using the Delphi method and was determined by reaching consensus. The expert panel was made up of two science educators, two science teachers, one chemist and two psychologists. All of the experts revised the CSCQ individually and exchanged opinions about revisions until they reached consensus. Then, the constructed questionnaire was tested on thirty university students majoring in chemistry to validate the content, obtaining a Cronbach’s α value of .935. Based on the research questions, we designed two types of ERP experiments that included 2D chemical structural formulas and 3D chemical structural formulas. Each experiment included a short instructional statement and 62 trials. All experiments were repeated twice to determine the repeatability of the ERP signals. The participants’ responses were recorded by pressing different buttons automatically on the computer keyboard (matched: press ○; mismatched: press ×). The scores and the neuroscience data were collected for analysis. Each correct response in every trial was scored as 1 point, and the best possible score was 62 points for one set of trials. The extracted data were analyzed using a t-test (SPSS version 12.0). The interviews were conducted after the students completed the neuroscience experiments. Each interview lasted 40 minutes and the response data were combined with the findings of the ERP data and the scores of the questionnaire.

From the analysis of neuroscience data, the main result in this study shows that the high-achieving students performed mental rotation more often in recognizing 2D chemical structural formulas 3D chemical structural formulas. Besides, the high-achieving students used mental rotation more often than low-achieving students. The interview data showed that the low-achieving students used similar strategies to identify 2D chemical structural formulas and 3D chemical structural formulas. However, the HSG students used different strategies to identify 2D chemical structural formulas and 3D chemical structural formulas. The interview data show that the reasons might because the low-achieving students do not understand the relationship between 2D and 3D chemical structural formulas. This study implied that the science teachers need teach students to recognize chemical structural formulas by knowing the identification of chemical structures first. Then, the teachers could use analytical strategies and mental rotation strategies to teach students understand the differences between 2D and 3D chemical structural formulas.