Authors - N. Revathy, Tamilmani M, Naveena P, Mariya Nisha S, Mega varshini V, Karthik B Abstract - Virtual Learning Environments (VLEs) are commonly evaluated through expert-driven frameworks that lack reproducibility and objective prioritization of defining features. This study proposes a data-driven framework integrating a Systematic Literature Review (SLR) and the iKeyCriteria method to identify and logically classify core VLE characteristics. A corpus of peer-re-viewed studies was analyzed and divided into VLE-focused (P) and contrastive non-VLE (Q) contexts. Criteria extraction and validation were conducted using tfidf (Term Frequency-Inverse Document Frequency) weighting and Boolean logical matrices to determine necessary and sufficient conditions. Results indicate that structured delivery of learning materials (91.5% in P vs. 12.7% in Q) and shared collaborative workspaces (82.1% vs. 18.2%) function as sufficient but not necessary discriminators of VLEs. In contrast, self-assessment and summative assessment appear frequently across both contexts and are therefore non-distinctive. The proposed framework provides a reproducible and bias-reduced mechanism for distinguishing defining VLE features, bridging systematic review methodologies with logical condition analysis. These findings support evidence-based prioritization in the design and evaluation of digital learning systems and contribute to advancing objective classification approaches in educational technology research.
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