VALIDATION OF THE ADAPTED MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE (MSLQ) FOR ONLINE LEARNING: INSIGHTS FROM EXPERTS REVIEWS

Abstract

Adapting Adapting the Motivated Strategies for Learning Questionnaire (MSLQ) for online learning environments is essential to ensure its relevance and accuracy in assessing students’ motivation and self-regulated learning (SRL) strategies in digital contexts. The MSLQ has been widely used in traditional learning settings. However, the rapid growth of online education necessitates its contextual refinement. The main objective of this study was to adapt and validate the MSLQ to suit online learning environments, ensuring that its terminology, structure, and constructs accurately reflect the digital learning experience. This study employed a three-phase expert validation process involving content analysis to adapt the MSLQ. Three experts in Information Technology, Language Education, and Content Creator were engaged to review and evaluate the modified questionnaire. The phases included preparation of the adapted instrument, evaluation through expert discussions, and implementation of feedback. Key constructs such as “class,” “teacher,” and “discussion” were modified to reflect online learning terminology (e.g., “learning management system,” “instructor,” “forum”), and each modification was reviewed for contextual appropriateness. The results revealed significant consensus among experts on the importance of using context-specific terminology and providing clear operational definitions to ensure the instrument’s clarity and applicability. However, some terms, such as “online readings” and “online discussion,” prompted critical feedback, highlighting the balance needed between specificity and generalizability. Final adaptations were made based on expert input, resulting in a more refined and validated MSLQ suited for online learning contexts. The refined MSLQ offers a practical and contextually appropriate tool for assessing SRL and motivation in online settings, with future studies recommended to expand expert involvement and conduct longitudinal testing to strengthen its predictive value and generalizability.