Lecturer's Strategy In Developing Higher-Order Thinking Skills (HOTS) Through The Deep Learning Approach In Macroeconomic Theory Courses

Abstract

The development of higher education in the Industrial Revolution 4.0 era requires students to have higher-order thinking skills (HOTS). However, in practice, HOTS development still faces various challenges in the university environment. Several studies show that students still tend to memorize economic concepts without being able to connect them to real phenomena. Based on this context, this study aims to analyze in-depth lecturers' strategies in developing higher-order thinking skills (HOTS) through a deep learning approach in Macroeconomic Theory courses. This study applies a qualitative method with a case study approach. Data were collected through observation and in-depth interviews with lecturers and students. Data analysis refers to the Miles and Huberman model which includes the stages of data reduction, data presentation, and conclusion drawing supported by the use of Nvivo software. The results of the study show that lecturers apply learning strategies based on macroeconomic case study analysis, policy evaluation, and the creation of academic solutions that encourage students' HOTS. This strategy is combined with deep learning that includes meaningful learning, mindful learning, and joyful learning, thereby increasing cognitive engagement, reflective awareness, and student learning motivation. The conclusion of this study states that the strategy used by Macroeconomic Theory lecturers through a deep learning approach is effective in developing students' higher-order thinking skills (HOTS), particularly in analyzing, evaluating, and creating. However, its implementation still faces obstacles such as student readiness, lecturers' pedagogical skills, and limited learning resources and time allocation.