Profile of Students' Computational Thinking Using Problem-Based Learning Models In Review From Learning Style at SMA N 1 Langsa

Abstract

This research examines students' computational thinking abilities in problem-based learning (PBL) classes. (2) Kolb's learning style profile towards students' computational thinking in the PBL context. This type of qualitative research is descriptive in nature, analyzing class XI MIA 4 students at SMAN 1 Langsa using data reduction techniques in assessing Kolb-type learning styles, computational thinking tests, interviews, and documentation. (1) The results of the computational thinking test using the problem-based learning (PBL) learning model showed that the low category was 9 people (25%), the medium category was 15 people (41.44%), and the high category was 12 people (33.33%). Nine low categories, 15 medium categories, and 12 high categories show this. Most students are intermediate computational thinkers. (2) Profile of MS participants with a convergent learning style with indications of decomposition (4 and 90.33%); pattern recognition (3 and 88.35%); thinking algorithm (3 and 72.85%); and patterns of abstraction and generalization (3 and 69.71%). Various learning styles with indications of decomposition (4 and 90.33%); pattern recognition (3 and 80.15%); thinking algorithm (3 and 71.85%); and pattern abstraction and generalization (3 and 69.40%) constitute the profile of DYZ subjects.

Keywords: Students' Computational Thinking, Problem Based Learning, Kolb Learning Style.