The DTSM Pedagogical Training Model (DEEP–TPACK–SAMR Moodle): Fostering Teachers’ Competence in Designing Deep Learning Scenarios

Abstract

Background of study: The implementation of Deep Learning requires teachers to possess adequate competence in designing learning scenarios that integrate pedagogy, content, and technology. However, preliminary findings showed that teachers experienced difficulties in translating Deep Learning concepts into concrete learning designs, while technology integration remained predominantly at the Substitution level. Aims and scope of paper: This study aims to identify teachers’ initial competencies and needs, examine the validity of the DTSM (Deep–TPACK–SAMR Moodle) pedagogical training model, and determine its effectiveness in improving teachers’ competence in designing Deep Learning scenarios. Methods: This study employed a mixed-method approach using Collaborative Action Research (CAR) with the Look, Think, and Act procedures. The participants were 20 teachers at SMP Kristen Eben Haezer Salatiga. Data were collected through interviews, observations, document analysis, focus group discussions, expert validation, pre- and post-tests, and assessment of teachers’ Deep Learning plans. Quantitative data were analyzed using descriptive statistics, the Wilcoxon Signed Rank Test, and N-Gain analysis. Result: The pedagogical expert validation obtained a mean score of 4.85/5.00 (very feasible), while IT expert validation obtained 4.00/5.00 (feasible). The mean test score increased from 60.75 to 89.25, with the pass rate increasing from 35% to 100%. The Wilcoxon test indicated a significant difference (p < 0.05), and the N-Gain score of 0.726 indicated high effectiveness. The quality of teachers’ Deep Learning plans reached 83.88%, categorized as good. Conclusion: The DTSM model is feasible and effective for improving teachers’ competence in designing Deep Learning scenarios through the integration of Deep Learning, TPACK, SAMR, and Moodle