Augmented Reality-based Rebuttal Texts (ARaRaT) on Momentum-Impulse: Rasch Analysis on Students’ Conceptual Change
DOI:
https://doi.org/10.48161/qaj.v5n1a1163Abstract
This study aimed to implement Augmented Reality-based Rebuttal Texts (ARaRaT) on momentum-impulse in the Predict Discuss Explain Observe Discuss Explain (PDEODE) strategy to identify students’ conceptual change. The design used in this study was an embedded mixed method. The research instrument used was 10 diagnostic test questions in a multi-tier format on momentum and impulse. The respondents in the study were 31 students (9 males and 22 females) of grade XI at one of the state high schools in Central Java, Indonesia. Data analysis was carried out with three categories of conceptual change, namely Acceptable Change (AC), Unacceptable Change (UC), and No Change (NC). Rasch analysis was used to map the comparison between the quality of respondents to the instruments used. The results showed that there was a change in conception in the AC category (32%), NC (44%), and UC (25%). Meanwhile, the highest change in misconceptions occurred in the AC category in question T5 (26%) and the lowest in questions T4 and T10 (10%). These results are supported by Rasch analysis which shows that in general there is a change from pretest to posttest. However, the probability of a change in conception can also be seen from the results of the analysis of Andrich Thresholds. Likewise, for the confidence level, students become more confident than before in answering questions. But this probability only shows the possibility that can occur when there is a category change. Furthermore, these results can be recommendations for other researchers in developing and implementing AR in physics learning.
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