The Effectiveness of The Tack Learning Model Using Logic Model Analysis To Enhance Self-Directed Learning - Hayati, Ridha Husnul; Hayati, Nikmah; Budi Utama, Hendri (Universitas Negeri Padang, 2023)
This research aims to evaluate the effectiveness of the TACK (Technological, Andragogical, Content Knowledge) learning model combined with Logic Model analysis in enhancing students' self-directed learning in the Department of Non-formal Education (PNF) at Padang State University. This research employs a quantitative experimental research approach, measuring the difference between pre-test and post-test scores to assess the impact of the TACK learning model on student self-directed learning. Data were collected through questionnaires, observations of the teaching implementation, and documentation. Logic Model analysis is utilized to map the components of the TACK model and evaluate the expected outcomes. The research findings indicate a significant improvement in student self-directed learning, as evidenced by the increase in post-test scores compared to pre-test scores. Statistical analysis supports the hypothesis that the TACK model effectively enhances students' self-directed learning in the Department of Non-formal Education (PNF). This research highlights the potential of the TACK learning model combined with Logic Model analysis as a valuable strategy for enhancing the quality of learning within university environments. Its application offers a promising approach to improving self-directed learning, which is crucial for enhancing learning outcomes. The findings of this research have implications for curriculum design and faculty training in educational institutions. Furthermore, this research contributes to the existing knowledge base on effective teaching methods for students, providing valuable insights for future learning practices.