Multimodal Assessment of Mental States and Visual Search for a User-Centred Design of Semantic Web Platforms

Background: Digital learning platforms increasingly leverage semantic web technologies to support interoperable and adaptive e-learning. However, the usability and cognitive impact of web-based authoring tools are still mainly assessed through subjective questionnaires and interaction logs, which provide limited time resolution and weak diagnostic power for identifying specific interface bottlenecks. Methods: We propose a multimodal evaluation of SOULSS, a semantic web-oriented platform for creating and optimizing digital learning contents. Eighteen participants completed an authoring workflow organized into three macro-segments (tutorial, initialization, module creation) while wearable electroencephalography, electrodermal activity, photoplethysmography, and eye tracking were recorded; objective metrics were analyzed both across macro-segments and within predefined micro-activities, whereas subjective engagement was collected after each macro-segment using the UES-SF. Results: Objective measures indicated increased EEG-derived mental workload and stress, higher tonic sympathetic arousal, and greater visual search and interaction effort during initialization and module creation, while UES-SF scores were lower during initialization. Fine-grained analyses localized critical elements to tutorial navigation options, the new course entry point, and spoiler-related controls. Repeated-measures correlations linked subjective scores with objective markers and supported an association between stress-related activation and delayed visual discovery. Conclusions: Integrating neurophysiological and eye tracking measures enables a more diagnostic assessment of semantic web-based authoring platforms than questionnaires alone, providing actionable evidence for iterative UX optimization and supporting a more user-centred design of digital educational tools.