IMPROVING INFORMATICS STUDENTS’ MATRIX ALGEBRA CONCEPTUAL UNDERSTANDING THROUGH A PYTHON-BASED LEARNING TRAJECTORY : Padang, Indonesia - Syelfia Dewimarni; Elmi Rahmawati; Lili Rismaini (Jurnal Math-UMB.EDU, 2026)
In matrix algebra courses, conceptual understanding remains low. This is due to the lack of integration and relevance of mathematics learning to their competency, namely informatics engineering. Learning trajectory instruction, characterized by sequential learning and Python computing applications relevant to students, provides a solution to improve students' matrix algebra conceptual understanding. The purpose of this study was to examine students' understanding of mathematical concepts in algebra by implementing a Python-based learning trajectory model. This study was a quasi-experimental study with posttest-only control group design. The sample was taken using a random sampling technique. A sample size of 69 students was selected, consisting of 33 students from the experimental class and 36 students from the control class. The results of this study present the difference in the average test scores for understanding matrix algebra concepts between the experimental and control classes. The t-test showed that t-test (3.469) > t-table (1.997), with α = 0.05 (df = 67), indicating that H1 was accepted, stating that the Python-based Learning trajectory model is effective as an alternative learning strategy to strengthen students' matrix algebra conceptual understanding. Keywords: Learning Trajectory; Python; Conceptual Understanding; Matrix Algebra