TPACK-Based Teacher Profiling for Personalized EdTech Recommendation
A Machine Learning Approach
Bibliographic Data
| ID | 22045512 |
|---|---|
| Authors | Joselin García-Ortiz (0000-0002-4540-9964, Universidad de Las Américas, corresponding author), Juan-Fernando Polanco (Universidad de Las Américas), Jaime Govea (0009-0005-5706-8422, Universidad de Las Américas) |
| Year | 2026 |
| Volume | 16 |
| Issue | 6 |
| Pages | 950 |
| Publication date | 2026-06-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci16060950 |
| OpenAlex | W7165023930 |
| Language | EN |
| References cited | 24 |
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
Analytics · Cluster analysis · Cosine similarity · Learning analytics · Operationalization · Personalized Learning · Visualization · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
Technological Pedagogical Content Knowledge (TPACK)
A Cluster Separation Measure
Data clustering
Toward the next generation of recommender systems
The Measurement of Observer Agreement for Categorical Data
Technological Pedagogical Content Knowledge
Silhouettes
Recommender Systems for Teachers
Developing a short assessment instrument for Technological Pedagogical Content Knowledge (TPACK.xs) and comparing the factor structure of an integrative and a transformative model
Coefficient alpha and the internal structure of tests
| Highly cited | No |
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