Smart and Data-Driven Learning Assessment Methods in Universities
Keywords:
Smart learning, Data-driven assessment, Learning analytics, Higher education, Artificial intelligence, Intelligent feedbackAbstract
This study aimed to systematically review and analyze scientific research on smart and data-driven learning assessment methods and frameworks in higher education to propose a conceptual model for improving learning quality. This qualitative study was based on a systematic literature review. Data were collected from international databases such as Scopus, Web of Science, and ScienceDirect. Twelve articles that focused on data-driven assessment, learning analytics, and intelligent evaluation systems in universities were selected according to inclusion criteria. Thematic analysis was performed using NVivo version 14, and data were coded inductively until theoretical saturation was achieved. The analysis revealed three major themes: (1) frameworks and models of smart learning assessment, (2) data-driven technologies and tools, and (3) indicators and outcomes of data-driven assessment in higher education. Results indicated that smart assessment, by leveraging learning data, artificial intelligence, and adaptive algorithms, transforms evaluation from static testing into dynamic, feedback-oriented processes that enhance educational quality, fairness, and evidence-based decision-making. The findings demonstrate that the future of learning assessment in higher education lies in data analytics, artificial intelligence, and adaptive learning systems. Developing ethical frameworks, improving faculty data literacy, and establishing intelligent infrastructures are essential prerequisites for successful implementation. Data-driven assessment can serve as a strategic tool to enhance higher education quality, personalize learning experiences, and promote lifelong learning.
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