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This study presents a structured method for validating and prioritizing key indicators necessary for implementing blended Massive Open Online Courses (bMOOCs) in English language learning, using the Fuzzy Delphi Method (FDM). Despite the growing adoption of MOOCs in higher education, especially during and after the COVID-19 pandemic, a lack of standardized criteria for evaluating blended learning in English as a Foreign Language (EFL) contexts persists. To address this gap, this method offers a systematic approach that captures expert consensus through fuzzy logic, enhancing decision-making under uncertainty. A panel of 15 experts evaluated 21 proposed sub-indicators across six domains. The FDM process included systematic steps, resulting in a validated set of 20 sub-indicators. • A comprehensive 8-step FDM process was used to validate 20 key sub-indicators for bMOOC implementation in English learning. • Indicators were categorized into six domains: learner analysis, objectives, materials and technology, teaching methodologies, learner participation, and assessment. • The method enhances transparency and replicability in developing evaluation frameworks for blended MOOC environments. This validated indicator set provides a foundation for instructional design, policy planning, and quality assurance in EFL programs adopting blended MOOC models.
Pham et al. (Fri,) studied this question.