Optimizing instructional language in massive open online courses: a dependency-grammar-based model of linguistic diversity, complexity, and learning effectiveness

Instructional language is central to knowledge construction in online learning, but its syntactic structure remains insufficiently quantified. This study developed a dependency-grammar-based framework to examine how linguistic diversity and complexity relate to instructional effectiveness in Massive Open Online Courses (MOOCs). A stratified random sample of 210 sessions from six transportation-engineering MOOCs was analyzed, including national-level “Excellent Courses” and standard courses. Audio recordings were transcribed with a speech recognition API and manually verified. HanLP was used to extract dependency relations and linguistic indicators, including Type-Token Ratio, dependency distance, word-frequency statistics, speech rate, and pause ratio. Binary logistic regression used national-level “Excellent Course” status as a proxy for instructional effectiveness. Greater linguistic diversity, especially higher proportions of nominal subjects, adjectival modifiers, and determiners, was associated with higher course quality. Excessive lexical and syntactic complexity, reflected by higher mean word frequency and longer dependency distances, was associated with lower effectiveness. Fewer prosodic pauses, moderate speech rate, and shorter instructional units were also linked to better MOOC performance. The framework provides an objective and scalable approach for evaluating and optimizing instructional language in online higher education.