Language Competence of Teacher Professional Education Students in Developing Deep Learning-Based Teaching Modules - Ruya Hilal Mukhtar; Rina Rosdiana; Sandi Budiana; Eneng Syifa Rahmadani; Sapira Aprillia (RETORIKA: Jurnal Bahasa, Sastra, dan Pengajarannya, 2026)
This study aims to analyze the language competence of Teacher Professional Education (PPG) students in developing deep learning-based teaching modules, particularly in terms of effective sentence construction. The research employed a qualitative descriptive approach with PPG Indonesian Language students from 2025 as subjects. Data consisting of 478 sentences from 35 teaching modules were analyzed using content analysis techniques based on indicators of unity, coherence, parallelism, accuracy, economy, and logic. Results indicate that language competence falls in the moderate-to-high category. Unity (95.82%), logic (93.51%), and accuracy (81.38%) scored high, while cohesion, parallelism, and economy remained moderate. Dominant errors were found in economy (47.07%) and cohesion (40.59%), indicating a need to strengthen students' language competence as an integral component of PPG programs. This finding has implications for the quality of deep learning based teaching modules, as ineffective language use may affect the clarity of material delivery. Therefore, it is necessary to strengthen language competence in the PPG program, particularly in the application of effective sentence principles in the development of instructional materials.