TPACK-Based Teacher Profiling for Personalized EdTech Recommendation: A Machine Learning Approach
Joselin García-Ortiz, Juan-Fernando Polanco, Jaime Govea · Education Sciences · 2026
The selection of educational technology (EdTech) tools by teachers remains a poorly systematized process, frequently disconnected from their pedagogical and technological competencies. This paper proposes a computational system that integrates teacher profiling based on the Technological Pedagogical Content Knowledge (TPACK) framework with unsupervised learning techniques and a cosine similarity-based recommendation mechanism. Using data collected through an adapted TPACK instrument administered to 303 secondary and higher education teachers, four structurally distinct profiles were identified using hierarchical clustering and K-means analysis. These profiles were used to generate personalized EdTech tool recommendations by matching them to the TPACK feature space. The system was evaluated using Recall@K, Mean Average Precision (MAP), Structural Alignment Index (SAI), and comparison with baseline models, using an expert-validated ground truth. The results show values of Recall@10 = 0.805, MAP = 0.777, and SAI = 0.998, with a Precision@1 improvement of 0.340 over the cosine baseline, suggesting that structural profiling of teacher knowledge improves the quality of the generated recommendations under expert-defined relevance conditions. The results obtained suggest that operationalizing the TPACK framework as a computational representation constitutes a promising basis for developing pedagogically contextualized recommendation systems in educational analytics environments.