Vietnam Digital Pedagogical Competence Dataset Based on the Analytic Hierarchy Process (VDPC Dataset 2025)
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| author | La, Phuong Thuy Kim, Manh Tuan Le, Thi Thu Hien Nguyen, Chi Thanh Nghiem, Thi Thanh |
| author_facet | La, Phuong Thuy Kim, Manh Tuan Le, Thi Thu Hien Nguyen, Chi Thanh Nghiem, Thi Thanh |
| contents | <h1><strong>Vietnam Digital Pedagogical Competence Dataset (VDPC Dataset 2025)</strong></h1> <h2><strong>1. Dataset Overview</strong></h2> <p>The <em>Vietnam Digital Pedagogical Competence Dataset (VDPC Dataset 2025)</em> presents a structured dataset developed using the Analytic Hierarchy Process to represent expert judgments on digital teaching competencies in higher education. The dataset captures relative importance through systematic pairwise comparisons, ensuring internal coherence and methodological rigor.</p> <p>A total of 34 pairwise comparison items were constructed, allowing for complete comparison matrices and reliable multi-criteria decision analysis.</p> <h2><strong>2. Research Context</strong></h2> <p>The dataset is situated within the Vietnamese higher education system, a context characterized by rapid digital transformation and increasing integration of educational technologies. The dataset reflects professional perspectives across key stakeholders involved in teaching, instructional design, and academic management.</p> <p>Variation across roles was intentionally incorporated to represent differences in pedagogical priorities and decision-making orientations.</p> <h2><strong>3. Research Purpose</strong></h2> <p>The dataset was developed to support both methodological and applied research objectives:</p> <ul> <li>To estimate priority weights of digital teaching competencies using AHP</li> <li>To examine consistency in expert judgments</li> <li>To enable comparison across stakeholder groups</li> <li>To provide input for hybrid analytical approaches such as AHP–SEM–ANN</li> </ul> <h2><strong>4. Conceptual Framework</strong></h2> <p>The dataset follows a hierarchical structure with two levels.</p> <h3><strong>4.1 Dimension Level</strong></h3> <p>Five core dimensions are included:</p> <ul> <li>Digital Pedagogical Design</li> <li>Assessment Literacy with Digital Tools</li> <li>Ethical and Responsible Use of Digital Tools</li> <li>Data-informed Teaching</li> <li>Student Agency Support</li> </ul> <h3><strong>4.2 Sub-dimension Level</strong></h3> <p>Each dimension is further decomposed into sub-dimensions representing specific competencies related to:</p> <ul> <li>Instructional design practices</li> <li>Digital assessment and feedback</li> <li>Ethical and responsible technology use</li> <li>Data utilization in teaching</li> <li>Learner autonomy and engagement</li> </ul> <h2><strong>5. Data Generation Process</strong></h2> <p>The dataset was synthetically generated using a theory-driven approach grounded in the Analytic Hierarchy Process.</p> <p>Latent priority structures were defined for each stakeholder group to reflect expected differences in professional perspectives. Individual responses were then generated using controlled variation around these latent profiles.</p> <p>All values were mapped onto the Saaty scale, including both direct and reciprocal values. Each pairwise comparison matrix satisfies the properties of completeness and reciprocity, ensuring mathematical validity.</p> <p>Group-level judgments can be aggregated using the geometric mean method, consistent with standard AHP procedures.</p> <h2><strong>6. Sample Design</strong></h2> <p>The dataset includes 30 experts distributed across three stakeholder groups:</p> <ul> <li>15 faculty members involved in teaching activities</li> <li>10 instructional designers specializing in digital pedagogy</li> <li>5 educational managers responsible for leadership and policy</li> </ul> <p>This composition supports comparative analysis across roles in the digital transformation of higher education.</p> <h2><strong>7. Data Structure</strong></h2> <p>The dataset is provided in Excel format.</p> <ul> <li>Each row represents one respondent</li> <li>Each column represents one pairwise comparison variable</li> <li>Variables are labeled from Q1 to Q34</li> </ul> <p>Additional variables include:</p> <ul> <li>Respondent ID</li> <li>Stakeholder group</li> </ul> <p>All values follow the AHP scale, including both direct values (1–9) and reciprocal values (e.g., 1/3, 1/5).</p> <h2><strong>8. Analytical Applications</strong></h2> <p>The dataset supports a wide range of analytical procedures:</p> <ul> <li>Calculation of priority weights using eigenvector methods</li> <li>Consistency Index and Consistency Ratio assessment</li> <li>Group-level aggregation and comparison</li> <li>Integration with Structural Equation Modeling</li> <li>Application in Artificial Neural Network models</li> </ul> <p>This flexibility makes the dataset suitable for hybrid methodological research in education.</p> <h2><strong>9. Ethical Considerations</strong></h2> <p>The dataset was developed following institutional ethical guidelines. Participation was voluntary, and no personally identifiable information was included.</p> <h2><strong>10. Suggested Citation</strong></h2> <p>La Phuong Thuy, Kim Manh Tuan, Le Thi Thu Hien, Nguyen Chi Thanh, Nghiem Thi Thanh. (2025). <em>Vietnam Digital Pedagogical Competence Dataset Based on the Analytic Hierarchy Process</em>. Zenodo.</p> <h2><strong>11. Keywords</strong></h2> <p>Analytic Hierarchy Process, digital pedagogy, educational leadership, multi-criteria decision making, Vietnam higher education, digital competence framework</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19510136 |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Vietnam Digital Pedagogical Competence Dataset Based on the Analytic Hierarchy Process (VDPC Dataset 2025) La, Phuong Thuy Kim, Manh Tuan Le, Thi Thu Hien Nguyen, Chi Thanh Nghiem, Thi Thanh Analytic Hierarchy Process, digital pedagogy, educational leadership, multi-criteria decision making, Vietnam higher education, digital competence framework Analytic Hierarchy Process digital pedagogy educational leadership multi-criteria decision making Vietnam higher education digital competence framework <h1><strong>Vietnam Digital Pedagogical Competence Dataset (VDPC Dataset 2025)</strong></h1> <h2><strong>1. Dataset Overview</strong></h2> <p>The <em>Vietnam Digital Pedagogical Competence Dataset (VDPC Dataset 2025)</em> presents a structured dataset developed using the Analytic Hierarchy Process to represent expert judgments on digital teaching competencies in higher education. The dataset captures relative importance through systematic pairwise comparisons, ensuring internal coherence and methodological rigor.</p> <p>A total of 34 pairwise comparison items were constructed, allowing for complete comparison matrices and reliable multi-criteria decision analysis.</p> <h2><strong>2. Research Context</strong></h2> <p>The dataset is situated within the Vietnamese higher education system, a context characterized by rapid digital transformation and increasing integration of educational technologies. The dataset reflects professional perspectives across key stakeholders involved in teaching, instructional design, and academic management.</p> <p>Variation across roles was intentionally incorporated to represent differences in pedagogical priorities and decision-making orientations.</p> <h2><strong>3. Research Purpose</strong></h2> <p>The dataset was developed to support both methodological and applied research objectives:</p> <ul> <li>To estimate priority weights of digital teaching competencies using AHP</li> <li>To examine consistency in expert judgments</li> <li>To enable comparison across stakeholder groups</li> <li>To provide input for hybrid analytical approaches such as AHP–SEM–ANN</li> </ul> <h2><strong>4. Conceptual Framework</strong></h2> <p>The dataset follows a hierarchical structure with two levels.</p> <h3><strong>4.1 Dimension Level</strong></h3> <p>Five core dimensions are included:</p> <ul> <li>Digital Pedagogical Design</li> <li>Assessment Literacy with Digital Tools</li> <li>Ethical and Responsible Use of Digital Tools</li> <li>Data-informed Teaching</li> <li>Student Agency Support</li> </ul> <h3><strong>4.2 Sub-dimension Level</strong></h3> <p>Each dimension is further decomposed into sub-dimensions representing specific competencies related to:</p> <ul> <li>Instructional design practices</li> <li>Digital assessment and feedback</li> <li>Ethical and responsible technology use</li> <li>Data utilization in teaching</li> <li>Learner autonomy and engagement</li> </ul> <h2><strong>5. Data Generation Process</strong></h2> <p>The dataset was synthetically generated using a theory-driven approach grounded in the Analytic Hierarchy Process.</p> <p>Latent priority structures were defined for each stakeholder group to reflect expected differences in professional perspectives. Individual responses were then generated using controlled variation around these latent profiles.</p> <p>All values were mapped onto the Saaty scale, including both direct and reciprocal values. Each pairwise comparison matrix satisfies the properties of completeness and reciprocity, ensuring mathematical validity.</p> <p>Group-level judgments can be aggregated using the geometric mean method, consistent with standard AHP procedures.</p> <h2><strong>6. Sample Design</strong></h2> <p>The dataset includes 30 experts distributed across three stakeholder groups:</p> <ul> <li>15 faculty members involved in teaching activities</li> <li>10 instructional designers specializing in digital pedagogy</li> <li>5 educational managers responsible for leadership and policy</li> </ul> <p>This composition supports comparative analysis across roles in the digital transformation of higher education.</p> <h2><strong>7. Data Structure</strong></h2> <p>The dataset is provided in Excel format.</p> <ul> <li>Each row represents one respondent</li> <li>Each column represents one pairwise comparison variable</li> <li>Variables are labeled from Q1 to Q34</li> </ul> <p>Additional variables include:</p> <ul> <li>Respondent ID</li> <li>Stakeholder group</li> </ul> <p>All values follow the AHP scale, including both direct values (1–9) and reciprocal values (e.g., 1/3, 1/5).</p> <h2><strong>8. Analytical Applications</strong></h2> <p>The dataset supports a wide range of analytical procedures:</p> <ul> <li>Calculation of priority weights using eigenvector methods</li> <li>Consistency Index and Consistency Ratio assessment</li> <li>Group-level aggregation and comparison</li> <li>Integration with Structural Equation Modeling</li> <li>Application in Artificial Neural Network models</li> </ul> <p>This flexibility makes the dataset suitable for hybrid methodological research in education.</p> <h2><strong>9. Ethical Considerations</strong></h2> <p>The dataset was developed following institutional ethical guidelines. Participation was voluntary, and no personally identifiable information was included.</p> <h2><strong>10. Suggested Citation</strong></h2> <p>La Phuong Thuy, Kim Manh Tuan, Le Thi Thu Hien, Nguyen Chi Thanh, Nghiem Thi Thanh. (2025). <em>Vietnam Digital Pedagogical Competence Dataset Based on the Analytic Hierarchy Process</em>. Zenodo.</p> <h2><strong>11. Keywords</strong></h2> <p>Analytic Hierarchy Process, digital pedagogy, educational leadership, multi-criteria decision making, Vietnam higher education, digital competence framework</p> |
| title | Vietnam Digital Pedagogical Competence Dataset Based on the Analytic Hierarchy Process (VDPC Dataset 2025) |
| topic | Analytic Hierarchy Process, digital pedagogy, educational leadership, multi-criteria decision making, Vietnam higher education, digital competence framework Analytic Hierarchy Process digital pedagogy educational leadership multi-criteria decision making Vietnam higher education digital competence framework |
| url | https://doi.org/10.5281/zenodo.19510136 |