Using item option characteristics curve to analyze misconceptions on chemical reactions of pre-service chemistry teachers in Indonesia / Hilman Qudratuddarsi

Hilman , Qudratuddarsi (2020) Using item option characteristics curve to analyze misconceptions on chemical reactions of pre-service chemistry teachers in Indonesia / Hilman Qudratuddarsi. Masters thesis, Universiti Malaya.

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Abstract

Chemical reactions are an important component when studying chemistry but it is difficult to master them. The presence of three-level of representations (macro, sub-micro and symbolic) in chemistry add to this difficulty and as a result, misconceptions do take place. Misconceptions interfere with cognitive development of students and prevent them from having meaningful learning experiences. Thus, identifying accurately these misconceptions are paramount. These misconceptions are analysed using two-tier multiple-choice questions (2TMC). Some researchers argue its limiting diagnostic power and introduced three-tier and four-tier multiple-choice questions (3TMC and 4TMC). However, 3TMC and 4TMC have its own challenges in term of time and resources and is not very helpful in helping teachers to diagnose students‘ misconceptions. To counter this, researchers tried to enhance the diagnostic power of ordinary multiple-choice questions. This study aims to propose the use of item option characteristics curve (IOCC) to analyze strongest distractors to show misconceptions and promote 2TMC diagnostic power. The study was conducted by adopting the Representational Systems and Chemical Reactions Diagnostic Instrument (RSCRDI) and 185 Indonesian pre-service chemistry teachers participated in the study. The data was analysed using SPSS and Winstep software to create IOCC. The findings revealed, both IOOC and alternative answers for analysis using traditional method, showed comparable results, implying that using IOCC can detect pre-service chemistry teachers‘ misconceptions. In addition, IOCC analysis also highlighted aspects such as the unexpected curve after 0 logit of student measure, the inconsistency of alternative answers or distractor with the highest probability, and provided additional information to judge the strongest distractor to show misconceptions. The findings implied that distractor analysis using IOCC reveals information that are more detailed and richer, therefore has the ability enhance diagnostic power of 2TMC. This method is simpler and it allows teachers and students to diagnose their misconception to improve the quality of teaching and learning.

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