The rapid digitalization of education has increased reliance on web-based platforms for delivering content, coordinating learning activities, and supporting assessment and feedback. However, institutions often lack a coherent framework for evaluating how effectively such platforms contribute to learning quality and organizational outcomes. This paper proposes an integrative assessment model that captures the role and effectiveness of educational web platforms across three dimensions: pedagogical value, technological performance, and managerial impact. Pedagogical value is assessed through indicators of learning engagement, interactivity, and assessment support. Technological performance is evaluated in terms of usability, reliability, interoperability, and data security. Managerial impact is examined through analytics-driven decision support, process transparency, and scalability. The model operationalizes these dimensions through measurable indicators and a multi-criteria scoring procedure that enables benchmarking across platforms and courses. To demonstrate reproducibility, a prototype evaluation pipeline was implemented and applied to log data from two higher education courses delivered on Moodle and Canvas platforms. Example computations for five indicators and domain scores are reported, accompanied by sensitivity analysis of weight perturbations. The results provide a practical instrument for institutions seeking to strengthen digital pedagogy, improve learning outcomes, and ensure sustainable adoption of web platforms.
C. Villa-Torrano et al., “Using learning design and learning analytics to promote, detect and support socially-shared regulation of learning: A systematic literature review,” Computers & Education, vol. 232, Art. no. 105261, 2025, [Online]. Available: https://doi.org/10.1016/j.compedu.2025.105261.
P. G. de Barba, E. A. Oliveira, and N. English, “Development and validation of a learning analytics rubric for self-regulated learning,” Educational Technology Research and Development, 2025, [Online]. Available: https://doi.org/10.1007/s11423-025-10521-x.
S. Bez, F. Burkart, M. J. Tomasik, and S. Merk, “How do teachers process technology-based formative assessment results in their daily practice? Results from process mining of think-aloud data,” Learning and Instruction, vol. 97, Art. no. 102100, 2025, [Online]. Available: https://doi.org/10.1016/j.learninstruc.2025.102100.
K. Jurāne-Brēmane, “Developing pedagogical principles for digital assessment,” Education Sciences, vol. 14, no. 10, Art. no. 1067, 2024, [Online]. Available: https://doi.org/10.3390/educsci14101067.
C. Slade, K. Mahon, K. Benson, J. Lynagh, D. McGrath, K. Sheppard, and Q. Ahsan, “A pedagogical evaluation of an institution’s digital assessment platform (DAP): Integrating pedagogical, technical and contextual factors,” Australasian Journal of Educational Technology, vol. 40, no. 4, pp. 90-104, 2024, [Online]. Available: https://doi.org/10.14742/ajet.9448.
L. de Vreugd, A. van Leeuwen, R. Jansen, and M. van der Schaaf, “Learning analytics dashboard design and evaluation to support student self-regulation of study behaviour,” Journal of Learning Analytics, vol. 11, no. 3, pp. 249-262, 2024, [Online]. Available: https://doi.org/10.18608/jla.2024.8529.
A. Baykasoglu, B. Felekoglu, and C. Ünal, “Perceived usability evaluation of learning management systems via axiomatic design with a real life application,” Kybernetes, vol. 53, no. 1, pp. 83-101, 2024, [Online]. Available: https://doi.org/10.1108/K-07-2022-1024.
B. van Berk, U. Kroehne, and C. Dignath, “On the right track: Decoding self-regulated learning in young students’ log data with the digital train track task,” Frontiers in Education, vol. 9, Art. no. 1388202, 2024, [Online]. Available: https://doi.org/10.3389/feduc.2024.1388202.
O. Afolalu and M. S. Tsoeu, “Cybersecurity in higher education institutions: A systematic review of emerging trends, challenges and solutions,” Future Internet, vol. 17, no. 12, Art. no. 575, 2025, [Online]. Available: https://doi.org/10.3390/fi17120575.
M. B. Kwapisz, A. Kohli, and P. Rajivan, “Privacy concerns of student data shared with instructors in an online learning management system,” in Proc. CHI Conf. Human Factors in Computing Systems (CHI 24). ACM, 2024, Art. no. 661, pp. 1-16, [Online]. Available: https://doi.org/10.1145/3613904.3642914.
