The Relationship of Students’ Thinking Level and the Ability to Develop Proposition Network Representation of Human Nervous System in Modeling Based Learning (MbL)

L. Kadarusman, A. Rahmat, D. Priyandoko


The purpose of this research is to reveal the relationship of thinking level with the students’ ability to form a representation of proposition network on the human nervous system concept using modeling based learning. This was quantitative research with 30 science class’ students of grade XI from one private school in Bandung as the subject research, who learned using modeling-based learning (MbL). The instruments used to measure the thinking level were 19 numbers of multiple choices and 2 essays that were developed based on Marzano and Kendall’s level thinking indicator. The result of this research shows that the thinking level of senior high school’ students has reached L3 (analysis) with minimum standard mastery ≥70. The higher the expectation of students’ thinking level, the lower the minimum standard mastery will be reached. The correlation result showS no significant relationship between thinking level and the students’ ability to form a proposition network on the study of neuron structure and function (r= 0,075; p=0,692) with low concept complexity. The significant relationship between thinking level and the ability to form proposition representation is obtained during the study of the central nervous and peripheral nervous system (r= 0,506; p= 0,004) with higher concept complexity. It means the higher students’ thinking level, the better their abilities to form a proposition network. MbL could be recommended for learning biology concept especially abstract concept like the human nervous system. This research concluded that students’ thinking level reached level 3 (analysis) and MbL can facilitate a significant relationship between thinking level and the ability to form proposition networks if the concept taught has a higher complexity compared to the lower complexity concept.


thinking level; proposition network representation; modeling based learning; human nervous system

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