Plug and Unplugged Activities to Enhance Computational Thinking Self Efficacy of Pre-service Biology Teachers

Abstract

Currently, plug-and-unplugged activities for learning statistics are needed to determine students' mindsets when solving statistical problems manually, using calculations without programming as an unplugged activity and for data analysis as a plug activity. This research analyzes computational thinking self-efficacy in pre-service biology teachers by implementing plug-and-unplugged activities in statistics learning. The research method used is a case study with a computational thinking self-efficacy questionnaire instrument. The sample used was 107 pre-service biology teachers at a university in Indonesia who received plug-and-unplugged activities in statistics learning for one semester. The programs used in plug activities are the R and SPSS applications. The research results show that 43% of pre-service biology teachers' computational thinking self-efficacy is in the good category, 26% is in the moderate category, 16% is in the very good category, 10% is in the low category, and the rest are very low. Meanwhile, if we look at each component of self-efficacy, computational thinking, decomposition, pattern recognition, abstraction, and algorithmic thinking are each in the moderate category. This indicates that plug-and-unplugged activities can initiate computational thinking skills in pre-service biology teachers. It is necessary to routinely provide activities or learning activities that can improve the computational thinking of pre-service biology teachers because of the need in this digital era